Through a burgeoning array of novel applications, businesses are turning to AI to dramatically expand the value they can create for their customers. Often, such applications involve optimizing internal processes: the data, the workflows, the controls managed inside the four walls of a business. But equally important is the value that AI can help to realize beyond those walls, through collaborative relationships with buyers, suppliers, and other key stakeholders. A firm, after all, is only as successful as its ability to manage spend, suppliers, and risk across its ecosystem. This is why the touchpoints between trading partners鈥攖he digital networks through which day-to-day operational and transactional data flow up and down the supply chain鈥攈old immense potential to unlock value as businesses increasingly become .

Business networks form an interdependent system within which buyers, suppliers, procurement organizations, service providers, financial institutions, and AI agents coordinate decisions, foster collaboration, and deter disruption. As the world鈥檚 largest business-to-business marketplace, facilitates over US$8 trillion in commerce annually across more than 940 million transactions as of the 12 months ending the third quarter of 2026. Aided by an expanding set of AI agents, the network can contextualize vast troves of operational data to help carry out desired tasks within strict parameters set by businesses seeking to manage risk, ensure compliance, and extend competitive advantage. If enterprise resource planning solutions can be thought of as an organization鈥檚 central nervous system, then cloud-based business networks comprise the connective tissue between enterprises seeking to manage spend, facilitate collaboration, and optimize procurement and invoicing processes.

The vision of 麻豆原创 Business Network is to become the intelligence and execution layer for commerce between companies, transforming the market from fragmented, point-to-point collaboration toward a connected ecosystem in which buyers, suppliers, logistics providers, and asset operators can anticipate change and coordinate decisions in real time. By combining the trusted business processes of with multi-enterprise network data, , and , 麻豆原创鈥檚 AI solution embedded across company systems and extensible across non-company systems, 麻豆原创 Business Network can enable increasingly intelligent, predictive, and autonomous processes across sourcing, procurement, supplier management, and finance.

Some of the greatest opportunity to harness value from cross-enterprise AI solutions lies in managing spend and supply chains. Consider, for example, the complexities and costs involved in , where a manufacturer must balance revenue, service levels, inventory, and production constraints across multiple parties in real time. To do so, the business runs simulations to surface recommendations for alternative configurations, sourcing strategies, or distribution scenarios, all in hopes of averting disruption that could affect buyers, suppliers, or end-user customers.

Years ago, such efforts might have proved unreliable owing to sheer complexity or, worse, fragmented data. But when paired with trading partners鈥� synchronous operational data converging through business networks, AI can not only produce actionable recommendations but can carry them out with precision and within specified parameters. It can even prompt crucial questions that businesses may have neglected to consider before previously undetected vulnerabilities have the chance to imperil mission-critical processes. When trading partners achieve end-to-end process transparency via the cloud, the intelligence and insights available to them increase exponentially, as do the efficiencies that result.

Yet it bears noting that the capabilities made possible by agentic AI neither replace nor diminish the importance of human involvement. When the C-suite leaders responsible for spend management and supply chain strategy evaluate autonomous solutions, governance emerges consistently as a top priority. To ensure accountability and oversight at every link of the supply chain, a business-to-business platform must offer agentic solutions that operate strictly within an organization鈥檚 authorized guardrails and internal controls. For compliance and audit purposes, it must also document the various factors that contribute to shaping each automated transaction. Transparency, after all, breeds trust, which in turn spurs adoption at scale.

As automated solutions take on a greater share of tactical tasks and routine processes鈥攕uch as generating demand forecasts, processing purchase orders, optimizing shipment schedules, streamlining warehouse operations, and the like鈥攕trategic judgment remains the undisputed province of supply chain and spend management professionals. Because all of the foregoing types of process data weave their way through business networks, they become the ideal fabric on which to embroider agentic AI capabilities.

The age-old problem in supply chain management posed by fragmented data residing on siloed, or even incompatible, systems falls away when core operational data intersects in the cloud at the business network layer. Such networks deliver not only a comprehensive view of the supply chain but also the agentic AI capabilities necessary to avert disruption, mitigate risk, and foster strategic collaboration among trading partners. By relying on operational data both contemporaneous and historical, business networks deliver the context within which enterprises can apply AI solutions reliably to solve many of their toughest operational challenges鈥攁nd to strengthen their trading relationships for long-term mutual success.

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Joern Keller is executive vice president and chief product officer for 麻豆原创 Business Network.

In Q3, we expanded our portfolio of Joule Assistants and introduced dozens of specialized agents that help customers move beyond isolated tasks toward connected, end-to-end execution. The value of AI has always been about what it accomplishes for our customers. Our recent releases help them streamline processes, increase productivity, and build more resilient operations.

Joule is 麻豆原创鈥檚 portfolio-wide AI engagement layer, combining agentic capabilities with 麻豆原创 business applications to help organizations become an Autonomous Enterprise. Through Joule Work, the new UI for AI, people describe what they want to achieve, get relevant insights, build app-less spaces personalized to their needs, and take action across connected business systems. When it鈥檚 time to execute, Joule Work selects the right Joule Assistant for the job. These assistants act as domain experts, bringing knowledge of roles and processes to the task at hand. They orchestrate specialized Joule Agents to execute tasks, using business context and data, respecting established authorizations, and maintaining audit trails. People set the direction, assistants coordinate agents, and agents carry out work with proper oversight and governance.

For example, billing teams can create invoices, identify anomalies, and resolve posting errors with the Billing Assistant and its agents. Supply chain planners can detect critical shortages earlier, assess trade-offs between service levels and inventory investment, and coordinate responses with the Supply Chain Planning Assistant. Maintenance teams can turn asset signals into coordinated maintenance action, helping address problems before they become larger operational disruptions. People retain oversight and control, while assistants and agents help reduce manual effort.

Customers are already putting these capabilities to work. PwC is using 麻豆原创鈥檚 Billing Assistant to help identify potential issues earlier, supporting better client outcomes. Novartis has launched a pilot of the Sourcing Assistant to help sourcing professionals focus on strategy, supplier engagement, and value creation.

鈥淎gents bring consistency and speed to work that previously required a lot of manual effort and coordination,鈥� , head of Procurement at Novartis. He expects improvements of 25% faster cycle times and a 35% uplift in FTE productivity.

As organizations adopt more agents, they need a clear view of their AI landscape. 麻豆原创 AI Agent Hub provides a central inventory of 麻豆原创 and non-麻豆原创 agents, helping teams keep track of and govern their growing agent portfolio. With 麻豆原创 Signavio agent mining and the Process Consulting Agent, we enable customers to identify their biggest opportunities for agentic AI and execute on them right away. This visibility supports a more coordinated approach to managing AI adoption across the business.

With Joule Studio, accessible through Joule Work, customers can create agents tailored to their business and extend 麻豆原创 standard agents with their own instructions. Browser-based tools and pro-code development options support different levels of technical expertise, while testing, versioning, and deployment turn ideas into agents that take action. This lets customers extend 麻豆原创鈥檚 capabilities to meet their business requirements.

Now, let鈥檚 dive into recent innovation.

Please note that this article covers only AI offerings released since July 1, 2026.

Joule

Joule Work

Employees can spend less time navigating applications and more time turning information into outcomes with Joule Work, which is available on desktop, in-app, web, and mobile. Joule Work, the new UI for AI, connects local and cloud environments, bringing conversational AI closer to the files, information, and business tools employees use every day. Employees can work with local files and folders in a secure sandbox environment, access current information through integrated web search, and connect to 麻豆原创 and non-麻豆原创 applications using Model Context Protocol (MCP) tools and Agent-to-Agent (a2a) integrations. Custom and corporate skills help streamline repetitive tasks, while local data and connected tools support faster creation of presentations, summaries, and other assets. And where a skill is not enough, the 麻豆原创 Knowledge Graph kicks in to search the entire space of business objects available to users for deeper transactional and analytical queries. Together, these capabilities reduce manual information analysis and help employees focus on higher-value work.

Dynamic workspaces, which we refer to as spaces, bring together insights, applications, Joule Assistants, and agents to support business workflows. Employees express what they want to accomplish, and Joule Assistants coordinate specialized agents to carry out the work. This effectively initiates the journey into a world where generative UI delivers an ultimate personalized experience to any end user. Joule Studio complements this experience with capabilities to build, deploy, and govern custom agents and workflows.

Early adopter feedback shows how this experience is changing the way employees interact with business information. Reinhausen, a global technology leader in electrical grid technologies, joined the Joule Work desktop early adopter program to support its AI journey. 鈥淲hat once required up to an hour of manual effort across our 麻豆原创 S/4HANA environment is now accessible in seconds through natural language, fundamentally changing how our teams interact with business data,鈥� said Peter Wied, director of Complexity Management at Reinhausen.

Customers are also using custom assistants to bring expertise closer to employees. Warehouse automation company KNAPP used Joule Studio to build an assistant that helps workers identify and resolve problems without waiting for an expert, reducing issue resolution times by about 90% and time spent on documentation and training by about 80%.

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The general availability for Joule Work is now underway in a phased approach.

Joule Studio

Business and IT teams can tailor Joule to their organization鈥檚 needs with Joule Studio, combining custom AI agents for complex business processes with reusable skills for everyday tasks. The agent builder enables teams to create agents that plan, reason, and dynamically address business challenges across 麻豆原创 and non-麻豆原创 systems. These agents can coordinate multi-step processes, invoke APIs, work with documents, and collaborate with users or other agents, helping organizations handle ambiguity, exceptions, and workflows that go beyond predefined automation.

The skill builder complements these capabilities with reliable, reusable operations accessed through Joule鈥檚 conversational interface. Teams can create skills to retrieve specific data, execute system transactions, and connect 麻豆原创 and third-party applications using predefined logic. Joule can select and trigger relevant skills based on context, helping employees complete frequent tasks more efficiently.

New intent-based development capabilities draw on business context from 麻豆原创 Signavio, 麻豆原创 Knowledge Graph, and 麻豆原创 Domain Models, helping teams translate business needs into tailored solutions. Pro-code capabilities let developers work in their preferred IDE, Git repository, and AI coding tools.

Together, the two builders help organizations combine flexible, goal-driven agents with predictable, task-specific automation. Grounding agents in business data and processes supports reliable outcomes, while centralized management provides visibility into skills, agent execution, and data privacy enforcement. Joule Studio helps customers build agents faster, improve accuracy while reducing costs, deploy with built-in governance, and ground AI in their business context. Estimated benefits include a 35% reduction in the time to build and deploy custom Joule Agents and a 10% reduction in the time to deploy custom Joule skills, alongside less time spent on frequent business tasks.

The agent builder is available for free until December 2026, after which it becomes a premium AI feature. The skill builder is a base AI feature.

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The general availability for Joule Studio is now underway in a phased approach.

麻豆原创 Joule for Consultants, AI-assisted custom knowledge benchmarking
Generally available

麻豆原创 consultants can use custom knowledge grounding in 麻豆原创 Joule for Consultants to receive expert guidance that combines 麻豆原创-curated content with system connectivity for an organization鈥檚 institutional knowledge. Through a grounding service that indexes organizational content with 麻豆原创 AI Core grounding, surfacing relevant information from internal documents into the Joule workflow. This way, responses reflect the firm鈥檚 methodologies, policies, delivery standards, and domain expertise. This reduces time spent searching and switching between tools while supporting growth in knowledge sources over time; it can improve productivity by up to 19%, reduce design iterations and subsequent rework by 50%, increase the share of profit by 1.5% through more optimized 麻豆原创 processes, and accelerate time to realized value by 13.7%.

Customers are already seeing the value of 麻豆原创 Joule for Consultants. reports a 40% reduction in development effort and a 25% decrease in software rework, helping its consultants focus more on innovation and client delivery.  expects to reduce consultant training time by 20 to 30% and save 93 hours each week on information searches, enabling consultants to build expertise faster and spend more time addressing clients鈥� business challenges.

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This capability is generally available.

Autonomous SCM

Product Design Assistant

• Change Record Management Agent
• Product Data Maintenance Agent
• Product Structure Optimization Agent

Design engineers and product managers can bring products to market faster and avoid costly rework with the Product Design Assistant. It connects data across design, engineering, compliance, and downstream processes, analyzing designs, recipes, and product information to surface insights. It also flags issues early and checks materials and requirements against internal rules and regulations. Three agents released in Q3 strengthen the foundations of product development. Engineers can keep product master data accurate with the Product Data Maintenance Agent, while the Product Structure Optimization Agent consistently optimizes bills of material across product views to reduce costs. When designs need to be changed, the Change Record Management Agent identifies similar change records affecting the same product and proposes a new draft version to initiate the change process. This reduces delays caused by fragmented change data and improves governance and traceability.

The result is faster, more confident design decisions and higher product quality, with estimated benefits including a 19% reduction in product design costs, a 10% increase in prototype success, a five percent reduction in cost of goods sold from design improvements, and a 12% reduction in tooling and material waste costs.

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The general availability for Product Design Assistant is now underway in a phased approach.

Logistics Assistant

• Batch Optimization Agent
• Warehouse Workload Optimization Agent
• Fixed Bin Maintenance Agent
• Material Reservation Processing Agent

Warehouse and logistics teams can keep operations running smoothly from inbound shipments to outbound deliveries with the Logistics Assistant. It senses disruptions as they happen and coordinates detection, planning, and execution across warehousing and transportation, managing decisions on labor, inventory, dispatch, and freight. Four agents released in Q3 focus on warehouse execution. Supervisors can match staff to demand and spot potential resource shortages early with the Warehouse Workload Optimization Agent. The Fixed Bin Maintenance Agent adjusts bins and replenishment as demand changes. On the inventory side, the Batch Optimization Agent monitors batch expiration and triggers actions to reduce waste. The Material Reservation Processing Agent lets users manage material reservations in natural language, with built-in human review. Together, these capabilities shorten the time from plan to execution, improve resilience to disruption, and lower the cost to serve by reducing expedites and inventory waste.

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The general availability for Logistics Assistant is now underway in a phased approach.

Manufacturing Assistant

• Production Planning and Operations Agent
• Production Master Data Readiness Agent
• Production Planning Order Progress Agent
• Workplace Safety Agent

Production planners and plant operators can keep manufacturing on track and respond to disruptions sooner with the Manufacturing Assistant. It detects issues in real time and runs corrective workflows for quality, scheduling, and workforce management. It also recommends next-best actions with clear pros and cons, so operators can focus on the exceptions that matter most.

Four agents released in Q3 support production from preparation through execution. The Production Master Data Readiness Agent makes sure materials are fully ready for production without manual setup, and the Production Planning Order Progress Agent identifies coverage issues at every order level. To speed up order-to-delivery, the Production Planning and Operations Agent helps planners release orders in as little as half the time. On the shop floor, the Workplace Safety Agent lets workers report safety concerns by describing what they observe, then assesses risks and suggests actions based on the situation and past data.

By coordinating people, systems, and workflows toward shared operational goals, the assistant helps reduce downtime, waste, and safety response times while supporting compliance through built-in traceability. Estimated benefits include a 14% reduction in production cycle time, a 19% reduction in scrap and defect rates, a six percent reduction in changeover time, a 10% reduction in operating income lost to unplanned downtime, and a 10% reduction in energy costs.

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The general availability for Manufacturing Assistant is now underway in a phased approach.

Supply Chain Planning Assistant

• Exception Management Agent
• Inventory Investment Allocation Agent
• Supply Chain Planning Orchestration Agent

Supply chain planners can detect issues earlier and respond to change with confidence using the Supply Chain Planning Assistant. It brings AI agents into everyday planning work, including exception management, demand fulfillment, inventory assessment, and capacity analysis, with one-click scenario simulation, root-cause analysis, and actionable recommendations. Three agents released in Q3 help planners turn analysis into action. The Exception Management Agent detects and resolves critical supply planning issues, giving planners the insights they need to protect service levels and avoid costly expedites. The Inventory Investment Allocation Agent weighs service levels against working capital to recommend optimal stock targets. The Supply Chain Planning Orchestration Agent coordinates these and other specialized agents across planning workflows. By speeding up decisions and surfacing capacity issues and persistent shortages in advance, the assistant helps planners keep schedules and inventory targets on track, with estimated benefits including a 20% reduction in planning coordination overhead and a 10% reduction in planning cycle time.

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The general availability for Supply Chain Planning Assistant is now underway in a phased approach.

Asset Operations Assistant

• Operational Maintenance Planning Agent
• Maintenance Event Planning Agent
• Maintenance Request Initiation Agent
• Maintenance Job Debrief Agent
• Equipment Insight Agent
• Task List Management Agent

Maintenance teams can identify critical asset conditions earlier and resolve them before they turn into larger operational disruptions with the Asset Operations Assistant. It turns asset signals into coordinated maintenance action and automates prioritization, planning, and scheduling, leaving teams more time for high-value work and exceptions.

Six agents released in Q3 cover the maintenance cycle from first request to final debrief. Technicians can create and route maintenance requests from natural-language input with the Maintenance Request Initiation Agent, while the Equipment Insights Agent provides a complete view of equipment to support better decision-making. The Operational Maintenance Planning Agent combines preventive, predictive, and corrective tasks into executable weekly and biweekly plans. When conditions change, the Maintenance Event Planning Agent analyzes real-time data to reprioritize tasks and adjust schedules. The Maintenance Task List Management Agent automates task list creation for greater consistency, and the Maintenance Job Debrief Agent streamlines job debriefs with AI-driven recommendations. By connecting asset health, risk, work outcomes, and financial impact, the assistant helps organizations increase planned work, reduce unplanned downtime, and improve uptime.

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The general availability for Asset Operations Assistant is now underway in a phased approach.

麻豆原创 Integration Business Planning, AI-assisted scenario planning

Supply chain planners can respond to disruptions with confidence using scenario planning in 麻豆原创 Integrated Business Planning. Within one workspace, they can assess impacts on demand and sales orders, identify alternative supply sources, and simulate mitigation options against KPIs such as service level and utilization before adopting the best-performing scenario. This streamlined approach helps planners make faster, data-driven decisions, build trust in their plans, and strengthen supply chain resilience.

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This capability is generally available.

Autonomous HCM

HR Service Assistant

• HR Knowledge Agent
• HR Workflow Accelerator Agent
• Payroll Explanation
• Time Off Service Agent
• Time Sheet Service Agent
• HR Service Orchestration Agent

HR service teams can resolve routine employee requests conversationally, without creating a case each time, using the HR Service Assistant. The assistant provides always-on support and draws on six agents released in Q3. Employees get fast, cited, policy-grounded answers from the HR Knowledge Agent, while the HR Service Orchestration Agent resolves requests through self-services or routes them to the right specialist agent. Common everyday questions are handled directly in the conversation: the Payroll Explanation Agent clarifies pay statements, and the Time Sheet Service Agent and Time Off Service Agent guide employees through submitting timesheets and creating compliant leave requests. When processes stall, the HR Workflow Accelerator Agent identifies delays and resolves them, allowing HR teams to reserve expert attention for complex cases. Estimated benefits include a 24% increase in case deflection to self-service, a 26% reduction in time to resolution, and a 40% reduction in queries that need Tier 2 or higher intervention.

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The general availability for HR Service Assistant is now underway in a phased approach.

HR Operations Assistant

• Employee Data Integration Agent
• Position Optimization Agent
• Job Change Request Agent
• Compensation Guidance Agent
• Job Architecture Maintenance Agent
• Skills Governance Agent

HR teams and managers can simplify core HR administration while keeping employee, job, and organizational data consistent and compliant with the HR Operations Assistant. Six agents released in Q3 help connect these activities: managers can find, edit, and create positions with the Position Optimization Agent and translate their intent into job changes with the Job Change Request Agent, while the Compensation Guidance Agent recommends policy-aligned salary increases based on performance, compensation, and skills data. To maintain the underlying data, the Employee Data Integration Agent resolves data inconsistencies, while the Job Architecture Maintenance Agent and Skills Governance Agent help keep role鈥搒kill mappings current and the skills ecosystem clean and AI-ready. Working alongside the assistant鈥檚 other specialized agents, they reduce administrative effort and support more reliable HR processes, with estimated benefits including a 39% reduction in data errors, a 20% reduction in HR transactional work, and a 25% reduction in manager time on core HR workflows.

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The general availability for HR Operations Assistant is now underway in a phased approach.

Performance and Goals Assistant

• Calibration Equity Agent
• Goal Creation Agent
• Goal Progress Agent

Managers and HR teams can align employee goals with business strategy and bring greater consistency to performance evaluations using the Performance and Goals Assistant. Three agents released in Q3 contribute to its broader capabilities: the Goal Creation Agent generates SMART performance goals grounded in strategy and role context; the Goal Progress Agent tracks completion in real time and flags risks before deadlines are missed; and the Calibration Equity Agent identifies potential inconsistencies in employees’ performance ratings before or during calibration. By combining clearer goals, proactive progress monitoring, and insights into rating inconsistencies, the assistant helps managers conduct more efficient reviews and make better-informed calibration decisions, with estimated benefits of a 21.4% reduction in review completion time and a 30% reduction in manual decision-making effort for managers.

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The general availability for Performance and Goals Assistant is now underway in a phased approach.

HR System Assistant

• Configuration Transport Agent
• HCM Audit Agent
• Role-based Permissions Agent

HR administrators can resolve access issues, manage configuration changes, and keep systems audit-ready with the HR System Assistant. It automates low-risk tasks and guides higher-risk changes, helping reduce errors and accelerate go-lives. Its Q3 releases address three key administrative needs: the Configuration Transport Agent discovers, bundles, and securely migrates configuration changes across tenants; the HCM Audit Agent enables natural language queries of 麻豆原创 SuccessFactors audit logs; and the Role-Based Permissions Agent queries user access, helps fix permission issues, and automates secure access resolution. As part of the assistant鈥檚 broader capabilities, these agents support faster troubleshooting, fewer configuration defects, and stronger traceability, with estimated benefits of a 70% reduction in configuration transport effort, a 90% reduction in time spent generating audit reports, and a 90% reduction in permission incident resolution costs.

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The general availability for HR System Assistant is now underway in a phased approach.

Autonomous Finance

Billing Assistant

• Billing Posting Agent
• Billing Creation Agent
• Billing Anomaly Agent
• Billing Adjustment Agent
• Error Explanation and Resolution Agent
• Billing Orchestration Agent

Billing teams can improve invoice accuracy and resolve issues faster with the Billing Assistant, which coordinates activities from document creation through adjustments and posting support. There are six agents being released in Q3: teams can prioritize due lists and create policy-aligned billing documents with the Billing Creation Agent, while the Billing Anomaly Agent flags unusual taxes and charges for review. When errors arise, the Billing Posting Agent and the Error Explanation and Resolution Agent help teams understand the issues and identify corrective steps, while the Billing Adjustment Agent cancels and reissues invoices to apply adjustments. The Billing Orchestration Agent coordinates these activities to keep billing processes moving. Together, these capabilities reduce manual effort and support faster invoice correction and dispute resolution, with estimated benefits including a 90% reduction in time spent analyzing and resolving posting issues, a 70% reduction in billing document creation effort, a 70% reduction in anomaly detection effort, and a 54% reduction in invoice reprocessing effort.

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The general availability for Billing Assistant is now underway in a phased approach.

Financial Closing Assistant

• Journal Entry Agent
• Accounting Accruals Agent
• Receivables and Payables Clearing Agent
• Analytical Business Insights Agent
• Asset Accounting Anomaly Detection Agent
• Financial Consistency Analysis Agent
• Financial Closing Orchestration Agent

Finance and accounting teams can accelerate period-end execution with the Financial Closing Assistant while controlling the extent to which agents participate in journal postings, clearing, and analysis. Seven agents released in Q3 support connected closing activities: teams can streamline audit-ready journal processing and accruals management with the Journal Entry Agent and Accounting Accruals Agent, and identify clearing opportunities through AI-based matching and aging analysis with the Receivables and Payables Clearing Agent. The Asset Accounting Anomaly Detection Agent and Financial Consistency Analysis Agent help improve accuracy by preventing invalid asset postings and resolving data errors, while the Analytical Business Insights Agent turns financial KPI analysis into executive summaries. Coordinating these workflows, the Financial Closing Orchestration Agent helps keep activities moving across teams and agents. This approach supports a faster, more reliable close, with an estimated 35% increase in productivity for staff performing reconciliations and adjustments.

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The general availability for Financial Closing Assistant is now underway in a phased approach.

Cash and Treasury Assistant

• Cash Positioning Agent
• Bank Relationship Agent
• Cash and Treasury Orchestration Agent

Treasury teams can manage liquidity more confidently and lower financing and banking costs with the Cash and Treasury Assistant. It monitors internal funding positions and needs, proposes cash transfers between bank accounts, and identifies ways to streamline bank wallet share and fees. Three agents released in Q3 support this work. The Cash Positioning Agent optimizes cash and performs cash-positioning tasks, such as checking balances, forecasting cash flows, and transferring funds. The Bank Relationship Agent gives treasurers a comprehensive view of the enterprise鈥檚 banking relationships, and the Cash and Treasury Orchestration Agent coordinates these activities with the assistant’s other specialized agents. Better interest yields and lower financing costs can free up funds for growth initiatives. Estimated benefits include a 50% increase in cash management and treasury staff productivity and a 10% reduction in bank commissions and fees. Productivity is also estimated to rise by 30% in the following areas: liquidity management and cash forecasting, FX risk and hedging, bank relationship management, and treasury reporting and compliance.

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The general availability for Cash and Treasury Assistant is now underway in a phased approach.

Financial Consolidation Assistant

• Financial Consolidation Data Submission Agent
• Financial Consolidation Orchestration Agent

Consolidation teams and local accountants can complete the financial close faster and with less manual effort using the Financial Consolidation Assistant. The assistant supports work across data preparation and consolidation. It guides users through issue resolution, simplifies master data maintenance, and speeds up simulations of mergers, FX changes, and other business scenarios. Two agents released in Q3 make the close cycle smoother. Local accountants can automate data submissions and resolve errors before data reaches group consolidation using the Financial Consolidation Data Submission Agent. The Financial Consolidation Orchestration Agent coordinates its agents. Teams spend less time collecting and correcting data and more time analyzing results, with an estimated 25% increase in productivity for staff performing reconciliations and adjustments.

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The general availability for Financial Consolidation Assistant is now underway in a phased approach.

Sales Operations Assistant

• Billing Block Resolution Agent
• Credit Block Resolution Agent
• Delivery Issue Resolution Agent
• Supply Issue Resolution Agent

Sales teams can keep orders moving and address fulfillment obstacles directly in their workflow with the Sales Operations Assistant. It automates sales order creation from unstructured data and supports order updates, shortening processing cycles and reducing errors. Four agents released in Q3 target common sources of delay: teams can address billing and credit blocks with the Billing Block Resolution Agent and Credit Block Resolution Agent, tackle delivery issues with the Delivery Issue Resolution Agent, and identify, analyze, and receive recommended solutions for supply problems with the Supply Issue Resolution Agent. By combining faster issue resolution with accurate, up-to-date sales data, the assistant helps improve customer satisfaction and support informed pricing decisions, with estimated benefits including a 15% reduction in revenue lost to fulfillment issues, a 10% reduction in financial losses from stockouts, and a 50% reduction in time spent resolving billing blocks and credit issues.

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The general availability for Sales Operations Assistant is now underway in a phased approach.

Recurring Receivables Assistant

• Collection Insights Agent
• Convergent Invoicing Execution Agent
• Subscription Lifecycle Agent

Finance teams can accelerate collections and manage high-volume recurring receivables more efficiently with the Recurring Receivables Assistant. Supporting recurring billing and subscription-based business models, it brings customer account context, risk insights, and recommended next steps into order-to-cash activities. Three agents released in Q3 help teams act on these insights: collectors get collection support from the Collection Insights Agent, while the Convergent Invoicing Execution Agent automates invoice corrections through geography-aware, regulation-compliant rebilling. The Subscription Lifecycle Agent complements these activities by automating subscription contract management from search through execution. Together, these capabilities help protect revenue, reduce downstream corrections, and free blocked cash, with estimated benefits including a 20% reduction in controllable days sales outstanding, a 20% increase in accounts receivable staff productivity, and a 10% reduction in controllable write-offs.

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The Recurring Receivables Assistant is available in the 麻豆原创 Early Adopter Care program.

Accounts Receivable Assistant

• Collection Account Preparation Agent
• Dispute Resolution Agent
• Dunning Insights Agent
• Collections Email Outreach Agent
• Dispute Creation Agent
• Receivable Account Analysis Agent
• Accounts Receivable Orchestration Agent

Accounts receivable teams can prepare more effective collection calls and release disputed cash sooner with the Accounts Receivable Assistant. Seven agents released in Q3 connect account analysis, customer outreach, and dispute handling: collectors can build a complete customer account picture and prioritize their next steps with the Receivable Account Analysis Agent, Collection Account Preparation Agent, and Dunning Insights Agent, while the Collections Email Outreach Agent automates correspondence for worklist items that do not require a call. The Dispute Creation Agent manages dispute cases using natural language processing and provides insights, while the Dispute Resolution Agent validates cases and suggests actionable solutions. The Accounts Receivable Orchestration Agent coordinates these activities to reduce manual effort and collection delays. Estimated benefits include a 35% increase in accounts receivable staff productivity, a 25% reduction in controllable days sales outstanding, a 15% reduction in uncollectable write-offs, and a five percent reduction in customer churn from disputes.

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The general availability for Accounts Receivable Assistant is now underway in a phased approach.

Tax Compliance Assistant

• Legal Change Agent
• Regulatory Document Mapping Explanation Agent
• Statutory Report Analysis Agent
• Statutory Report Extension Update Agent
• Tax and Compliance Orchestration Agent

Finance and IT teams can keep pace with regulatory changes and reduce manual compliance work with the Tax and Compliance Assistant. It supports continuous monitoring and automation across tax calculations, e-invoicing, statutory reporting, and payments, helping teams identify issues before they become costly disruptions. Five agents released in Q3 connect regulatory discovery with reporting maintenance: the Legal Change Agent helps teams discover relevant regulatory changes, while the Regulatory Document Mapping Explanation Agent clarifies regulated e-invoice field mappings and compliance rules through Joule. Teams can review statutory reports for anomalies and recommended fixes with the Statutory Report Analysis Agent, and merge report extensions when 麻豆原创 delivers new versions using the Statutory Report Extension Update Agent. The Tax and Compliance Orchestration Agent coordinates these activities, supporting traceable processes and faster error correction. Estimated benefits include a 75% reduction in compliance rework costs, a 30% reduction in tax compliance process costs, a 40% reduction in regulatory penalties and fines, and a 50% reduction in compliance monitoring effort.

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The general availability for Tax and Compliance Assistant is available in the 麻豆原创 Early Adopter Care program.

International Trade Assistant

• International Trade Product Profile Agent
• International Trade Classification Agent

International trade teams can streamline goods classification and manage changing trade requirements with the International Trade Assistant, bringing AI-powered support into enterprise import, export, compliance, and origin management processes. Two agents released in Q3 connect product understanding with classification: the International Trade Product Profile Agent collects and unifies product data to establish its semantic meaning, while the International Trade Classification Agent automatically classifies goods for shipping, helping teams save time and support compliance. Alongside capabilities such as sanction screening and EU Intrastat reporting, these agents help replace disconnected, reactive processes with a more governed and scalable approach to international trade.

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The general availability for International Trade Assistant is available in the 麻豆原创 Early Adopter Care program.

Accounts Payable Assistant

• Accounts Payable Orchestration Agent
• Payment Inquiry Handling Agent
• Receivables and Payables Clearing Agent

Accounts payable teams can reduce manual payment handling and reconcile vendor accounts more efficiently with the Accounts Payable Assistant. It combines payment automation, proactive validation, and guidance on exceptions with insights into cash flow and discount opportunities. Three agents released in Q3 support these workflows: teams can manage routine invoice-specific payment inquiries with the Payment Inquiry Handling Agent and generate balanced clearing proposals for open items in accounts payable and receivable with the Receivables and Payables Clearing Agent. The Accounts Payable Orchestration Agent coordinates the agents. By streamlining payment-related tasks and helping teams address discrepancies, the assistant offers estimated benefits of a 35% increase in accounts payable staff productivity and a 20% reduction in duplicate payments and exceptions.

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The general availability for Accounts Payable Assistant is now underway in a phased approach.

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted KPI and report summary

Managers and executives can get a quicker view of business performance with KPI and report summaries in 麻豆原创 S/4HANA Cloud Public Edition. The feature turns Generic Drilldown reports into concise natural-language summaries that highlight key metrics, trends, anomalies, and threshold breaches directly within 麻豆原创 Fiori apps. This makes complex analytics easier to interpret and share, helping users make more informed decisions without manual report analysis.

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This capability is generally available.

麻豆原创 S/4HANA Cloud Public Edition, EHS environment management, AI-assisted compliance management

EHS compliance managers can set up regulatory obligations and permits more efficiently with environment management and compliance management in 麻豆原创 S/4HANA Cloud Public Edition. The feature scans PDF documents to capture header data, identify relevant requirements, and propose related tasks such as maintenance notifications or reporting activities, while keeping the source document and proposed actions visible side by side. This reduces data entry errors and setup time, strengthens auditability by linking requirements to their source, and can reduce compliance obligation management costs by up to 65% while lowering the risk of penalties and fines.

Get started .

This capability is generally available.

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted aging work in progress analytics and management

Project financial controllers can track and manage aging work in process more effectively with Joule in 麻豆原创 S/4HANA Cloud Public Edition. The feature analyzes WIP for time-and-expense projects, identifies delayed billing, summarizes relevant insights in dynamic charts, and supports actions to help prevent revenue loss. This shortens the time needed to review WIP amounts, speeds billing decisions, and helps reduce potential cash flow risk.

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This capability is generally available.

麻豆原创 Cloud Identity Access Governance, AI-assisted access review

Access reviewers can make faster, more informed certification decisions with AI-assisted access review in 麻豆原创 Cloud Identity Access Governance. The feature provides context-aware recommendations based on historical review outcomes, peer-group analysis, and organizational access patterns, along with business-friendly explanations to help reviewers assess whether a user鈥檚 access remains appropriate. This reduces review effort and operational overhead while improving decision quality, strengthening access governance, and supporting compliance.

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This capability is generally available.

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted document extraction for simplified business partner master data maintenance

Sales managers, purchasing managers, and business partner master data specialists can accelerate business partner onboarding with document extraction in 麻豆原创 S/4HANA Cloud Public Edition. Integrated into the Manage Business Partner app, 麻豆原创 Document AI extracts names, addresses, bank account details, and tax numbers from uploaded purchase orders, quotations, and invoices, and automatically populates the relevant fields. This reduces manual transcription and switching between files and screens, helping improve data accuracy, consistency, and operational efficiency.

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This capability is generally available.

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted incoming bill of exchange posting

Finance teams can streamline incoming bill of exchange processing in 麻豆原创 S/4HANA Cloud Public Edition with automated data extraction and posting proposals. By uploading PDF or image files, they can capture bill details, identify missing or inconsistent data, and match open customer items to support posting and reconciliation. This reduces manual entry and errors, improves processing efficiency, and helps teams handle higher volumes across supported countries.

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This capability is generally available.

Autonomous Spend Management

Purchasing Assistant

• Purchase Order Lifecycle Management Agent

Purchasing teams can make faster, more consistent buying decisions and protect margins with the Purchasing Assistant. It identifies spend leakage, recommends the best-fit suppliers with clear, explainable reasoning, and automatically enriches and validates catalog data at upload. It also monitors frequently purchased materials for potential supply gaps and assigns new sources of supply. The Purchase Order Lifecycle Management Agent, released in Q3, extends this support beyond the point of purchase. It flags delivery risks for open orders and guides purchasers on the next steps. By automating analysis and reducing manual follow-up, the assistant improves catalog quality and keeps critical materials in stock, with estimated benefits including a one percent decrease in buying spend and a 30% increase in buying staff productivity.

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The general availability for Purchasing Assistant is now underway in a phased approach.

Supplier Management Assistant

• Supplier Management Identification and Optimization Agent

Supplier managers and procurement teams can build a more resilient supplier base and make better informed decisions with the Supplier Management Assistant. It supports the full supplier life cycle: it unifies supplier data across systems into a single, consistent view, assesses supplier performance to recommend targeted development plans, and continuously monitors financial, compliance, and ESG risk using real-time external data. The Supplier Management Identification and Optimization Agent, released in Q3, adds AI-powered supplier identification and optimization, helping teams find suitable suppliers and make sourcing decisions faster. With less manual effort spent gathering supplier information and assessing risk, organizations can respond to changes in their supply base sooner, with an estimated 20% increase in supplier management staff productivity.

Get started .

The general availability for Supplier Management Assistant is now underway in a phased approach.

Procurement Contract Assistant

• Procurement Contract Compliance Monitoring Agent
• Procurement Contract Analysis Agent
• Procurement Contract Renewal Optimization Agent

Procurement and contract management teams can protect contract value and reduce risk across the contract life cycle with the Procurement Contract Assistant. It produces legally compliant first drafts, continuously monitors terms, and supports decisions on renewal, renegotiation, and new sourcing. Three agents released in Q3 help bring existing and active agreements under control. Teams can speed up the ingestion of legacy contracts with the Procurement Contract Analysis Agent, which extracts metadata and obligations, while the Procurement Contract Compliance Monitoring Agent monitors those obligations and detects deviations in real time. As agreements near expiration, the Procurement Contract Renewal Optimization Agent evaluates them and recommends the next action. By surfacing risks to pricing, usage, SLAs, and obligations earlier, the assistant improves compliance and renewal outcomes, with estimated benefits including a 45% increase in contract management staff productivity and a 50% reduction in gaps in terms-and-conditions monitoring.

Get started .

The general availability for Procurement Contract Assistant is now underway in a phased approach.

Category Management Assistant

• Category Strategy Development Agent
• Market Insights Agent

Category managers can build, refine, and continuously optimize category strategies with the Category Management Assistant, providing procurement teams with a basis for more proactive decision-making. The assistant combines spend data, supplier context, and external market intelligence into a single view of each category, surfacing risks and opportunities such as market shifts, compliance gaps, and value leakage early. Two agents released in Q3 connect market research directly to strategy. The Market Insights Agent automates category research and delivers verified procurement intelligence, and the Category Strategy Development Agent turns market intelligence into data-driven strategy recommendations. Category managers spend less time gathering information and can adapt their strategies as conditions change, with estimated benefits including a 35% increase in category management staff productivity and a one percent reduction in maverick spend.

Get started .

The general availability for Category Management Assistant is now underway in a phased approach.

Invoicing Assistant

• Invoicing Orchestration Agent

Accounts payable teams can move toward touchless invoice processing with the Invoicing Assistant, which automates AP from invoice receipt to payment scheduling with minimal human input. It extracts invoice data, accelerates posting, and draws on integrated procurement and finance data to reduce errors, provide real-time insights, and detect potential fraud. The Invoicing Orchestration Agent, released in Q3, fully automates AP by extracting invoice data and orchestrating end-to-end processing with minimal human input. Less time spent on manual data entry and exception handling results in a more secure, compliant, and efficient process, and more room for higher-value work.

Get started .

The general availability for Invoicing Assistant is now underway in a phased approach.

Receiving Assistant

• Return Goods to Supplier Agent

Procurement and receiving teams can streamline goods receipts, service entry sheets, returns, and quality tracking with the Receiving Assistant, reducing manual steps while reinforcing purchase order and contract compliance. For supplier returns, the Return Goods to Supplier Agent helps identify slow-moving or obsolete stock and uses material details and return quantities to trace the original supplier and purchase order. It then prepares a draft return purchase order and notifies the responsible purchaser, who reviews the proposal and agrees on the return terms with the supplier. This combines automated preparation with human oversight to reduce administrative effort, help release working capital tied up in excess inventory, and lower the financial risk of unresolved dead stock.

Get started .

The general availability for Receiving Assistant is now underway in a phased approach.

Requisition Assistant

• Purchase Requisition Creation Agent
• Purchase Requisition Optimization Agent

Employees can find the items and services they need and prepare policy-compliant purchase requests with the Requisition Assistant. The Purchase Requisition Optimization Agent searches and compares options across multiple catalogs in real time, ranking alternatives against priorities such as price, quality, supplier preference, and delivery time. To simplify request preparation, the Purchase Requisition Creation Agent turns free-text requests or documents into ready-to-submit purchase carts, guides users to the appropriate buying channels, and autofills forms while checking policies and validating key details. These capabilities help employees balance purchasing needs with company preferences, reduce errors before submission, and shorten requisition-to-order cycles, with an estimated 15% increase in requisition staff productivity.

Get started .

The general availability for Requisition Assistant is now underway in a phased approach.

Operational Procurement Assistant

• Invoice Reminder Agent
• Material Spend Advisory Agent

Procurement teams can reduce manual follow-up and strengthen contract compliance across the procure-to-pay cycle with the Operational Procurement Assistant. It monitors and drives the processing of purchase requisitions, purchase orders, supplier contracts, scheduling agreements, service entry sheets, and supplier invoices. Teams can identify off-contract spending, quantify potential savings, and spot quick wins with the Material Spend Advisory Agent, while accountants receive invoice insights through the Invoice Reminder Agent. By reducing manual steps, keeping contracts and sourcing data up to date, and simplifying supplier quote management, the assistant helps accelerate procurement execution, reduce errors and delays, and improve contract utilization.

Get started .

The general availability for Operational Procurement Assistant is now underway in a phased approach.

麻豆原创 Ariba Contracts, AI-assisted contract creation from documents

Suppliers can create contracts in 麻豆原创 Ariba Contracts from existing contract PDFs. The feature extracts relevant contract details, recommends required fields, and provides a preview where users can review, edit, or override values before creating the contract. This reduces repetitive data entry, supports more consistent contract data, and helps accelerate contract onboarding and digitization.

Get started .

This capability is generally available.

麻豆原创 Ariba Contracts, AI-assisted contract creation from prompts

Contract managers can start new contracts in 麻豆原创 Ariba Contracts using natural-language prompts directly within the contract creation experience. The feature translates a small number of inputs into contract header fields in seconds, helping users focus on their intent rather than navigating through separate steps. Users review and confirm the proposed details before creating the contract, reducing effort while maintaining human control as contract volumes grow.

Get started .

This capability is generally available.

麻豆原创 Ariba Contracts, AI-assisted contract field recommendations

Contract managers can complete contracts faster in 麻豆原创 Ariba Contracts with context-aware field recommendations. The feature suggests relevant values from reliable data sources for fields such as supplier, company code, and payment terms, helping users navigate organizational rules and dependencies with less manual effort. Explainable recommendations support accuracy and compliance while allowing contract managers to retain control and flexibility over final values.

Get started .

This capability is generally available.

Concur Travel, AI-assisted trip changes

Business travelers and travel coordinators can manage itinerary changes through a natural-language conversation with Joule in Concur Travel. They can find a trip by describing it, review the estimated cost of a modification before confirming it, and update flights, hotels, or rental cars without switching to separate booking tools. This provides real-time support that reduces manual work and helps users make travel changes faster.

Get started .

This capability is generally available.

Concur Expense, AI-assisted approval manager

Approval managers can use AI-powered insights in Concur Expense to focus their review on the transactions that require attention. The feature identifies unusual patterns, inconsistencies, and missing context, while routine, lower-risk expenses recede from view to reduce review noise. Explainable AI reasoning clarifies why an item was flagged, and direct access to receipts, expense history, and supporting details helps managers quickly validate decisions. Integrated into existing approval workflows, the feature helps speed up reviews, improve consistency, and maintain accountability without requiring approvers to be expense-policy experts.

Get started .

This capability is available in 麻豆原创 Early Adopter Care.

Concur Expense, AI-assisted corporate card management

Card administrators and business travelers can simplify virtual card management with AI-driven automation in Concur Expense. Administrators can translate policies written in natural language into spending limits, merchant category controls, and approval workflows, while travelers can request cards and funds conversationally through familiar messaging channels. This reduces administrative effort, accelerates access to funds, and supports compliance through upfront spending guardrails. The feature is currently available only to eligible customers enrolled in the corresponding 麻豆原创 Early Adopter Care program.

Get started .

This capability is available in 麻豆原创 Early Adopter Care.

Concur Travel, AI-assisted travel filters

Business travelers can find suitable flights faster with AI-assisted travel filters in Concur Travel. By describing preferences such as a preferred airline or departure time window in plain language, they can narrow search results without scanning numerous options or adjusting filters individually. This helps travelers identify relevant itineraries more efficiently, simplify decision-making, and book with greater confidence.

Get started .

This capability is generally available.

Concur Travel, AI-assisted travel search

Corporate travelers can find relevant rail stations more easily with AI-assisted travel search in Concur Travel, even when they do not know the exact station name or local spelling. They can search in supported languages using station names, cities, countries, or points of interest, with support for typos, omitted accents, and different word orders. This helps travelers identify suitable stations with less effort and plan rail journeys with greater confidence.

Get started .

This capability is generally available.

Autonomous CX

Case Management Assistant

• Case Action Recommendation Agent
• Case Preparation Agent
• Case Processing Agent
• Case Management Orchestration Agent

Service representatives can move from case intake to resolution with clearer context and guided actions using the Case Management Assistant. Four agents released in Q3 support this journey: the Case Preparation Agent analyzes incoming requests and extracts key customer context, giving representatives an informed starting point, while the Case Action Recommendation Agent recommends next best actions and helps prioritize tasks based on the live case. The Case Processing Agent assists with processing steps across relevant scenarios and domains, while the Case Management Orchestration Agent coordinates the specialized agents throughout the workflow. By reducing back-and-forth and supporting case summaries and customer communications, the assistant helps teams deliver more efficient, consistent service, with estimated benefits including a 35% reduction in diagnosis effort, a 50% reduction in misrouting, and a 20% reduction in resolution time.

Get started .

The general availability for Case Management Assistant is now underway in a phased approach.

Deal Qualification Assistant

• Lead Conversion Agent
• Lead Enrichment Agent
• Lead Qualification Agent
• Lead Routing Agent
• Deal Qualification Orchestration Agent

Sales teams can prioritize promising prospects and accelerate opportunity creation with the Deal Qualification Assistant. It deduplicates lead data, assesses fit and intent, and automates routing and engagement. Four agents released in Q3 connect these activities: the Lead Enrichment Agent enhances incoming lead records with relevant external data, ensuring sales teams have the context needed to engage effectively, while the Lead Qualification Agent categorizes prospects as qualified, nurtured, or disqualified based on ideal customer criteria. After an account executive confirms qualification, the Lead Conversion Agent creates the required account, contact, and opportunity records, preserving lead details and engagement history. The Deal Qualification Orchestration Agent coordinates the specialized agents across the process. This reduces research and data entry while improving pipeline quality, with estimated benefits including a 35% reduction in manual deal-qualification effort and a 10% increase in the lead-to-meeting ratio.

Get started .

The general availability for Deal Qualification Assistant is now underway in a phased approach.

麻豆原创 Enterprise Service Management, AI-assisted document extraction

Service and support staff can extract relevant information from image and PDF attachments in 麻豆原创 Enterprise Service Management. The feature converts unstructured document content into structured data鈥攕uch as invoice or contract-account details鈥攁nd supports retrieval of related entities from 麻豆原创 back-end systems, including 麻豆原创 S/4HANA Cloud. This accelerates document handling, improves data accuracy, and connects incoming documents more smoothly to existing workflows and business processes.

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This capability is generally available.

麻豆原创 Omnichannel Promotion Pricing, AI-assisted promotional text translation

Promotion planners can use 麻豆原创 Omnichannel Promotion Pricing to translate promotional text into more than 150 languages. AI-powered translation reduces the manual work required to maintain multilingual content and helps keep messaging consistent across global promotion campaigns.

Get started .

This capability is generally available.

麻豆原创 Engagement Cloud, AI-assisted content composer

Marketers can accelerate campaign creation with the AI-powered content composer in 麻豆原创 Engagement Cloud, generating personalized copy and brand-consistent images directly within the campaign editor. Drawing on product catalog data, audience attributes, and reference campaigns, they can create headlines, body copy, calls to action, and hero images while retaining control to preview, edit, or regenerate each element. This integrated workflow helps teams launch campaigns faster, scale personalization, and reduce tool switching.

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This capability is generally available.

麻豆原创 Business AI Platform

Mistral on 麻豆原创 Business AI Platform

Organizations can use Mistral’s enterprise AI platform, including Mistral Vibe Work and AI Studio, hosted on 麻豆原创 Business AI Platform, to enhance productivity. Employees can interact with multilingual large language models to chat, think, search, write, upload files, and receive answers to questions, helping them automate meaningful daily work from a single environment. The offering also provides a no-code environment for teams to design, customize, deploy, and operate AI agents and workflows end-to-end. Mistral models are deployed locally on 麻豆原创 Cloud Infrastructure to support data processing within a trusted region, helping organizations address regulatory requirements, regional compliance mandates, and geopolitical considerations. A fully sovereign version on 麻豆原创 Sovereign Cloud, supporting legal, operational, technical, and data sovereignty, is planned for early 2027.

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This capability is generally available.

麻豆原创 Analytics Cloud, AI-assisted prompt insights

Story designers can embed large language model prompts as widgets in 麻豆原创 Analytics Cloud stories, enabling story consumers to generate AI responses directly within their analytics experience. The feature can provide structured analysis of tables or answer broader business questions, helping users create data-driven narratives and decision support without moving internal data to external AI tools. This reduces manual analysis effort and can cut the time required to analyze 麻豆原创 Analytics Cloud data by 60%.

Get started .

This capability is generally available.

麻豆原创 Analytics Cloud, AI-assisted story code generation

Business analysts can build customized dashboards more efficiently with AI-assisted story code generation in 麻豆原创 Analytics Cloud. The feature automatically generates JavaScript and explains its functionality and coding choices, helping analysts understand, modify, and extend scripts to create low-code analytics applications. This reduces manual coding effort, supports faster delivery of analytics solutions, and gives analysts greater flexibility to tailor dashboards to business needs.

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This capability is generally available.

麻豆原创 Datasphere, AI-assisted SQL generation

Data modelers can create and refine SQL views more efficiently with AI-assisted SQL generation in 麻豆原创 Datasphere. Using natural language prompts in the SQL View Builder, they can generate SQL code for new or existing views. This helps reduce manual coding effort and increase modeling productivity.

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This capability is generally available

Generative AI hub enhancements

麻豆原创-RPT-1.6
麻豆原创-RPT-1.6, now available on generative AI hub, is our latest model in the 麻豆原创-RPT family. It delivers the strongest gains in prediction quality to date, particularly for high-cardinality classification, and is validated across 197 real-world enterprise tasks.

• Explainability: Available in all 麻豆原创-RPT-1.6 variants, this includes column-level feature importances and the ability to surface specific historical rows most influential to each prediction, giving business users and decision-makers a clearer basis for trusting and acting on AI-driven outputs.
• Deep Context Mode: Only available in 麻豆原创-RPT-1.6-large, this allows the model to draw on significantly larger reference datasets to improve prediction quality. It is intended for use cases with large historical data, such as invoice records spanning multiple fiscal years or product catalogs in the tens of thousands. This mode trades higher latency and cost for higher accuracy, and can be enabled via a single parameter in the prediction configuration.

Tabular Orchestration
Tabular Orchestration, now available on generative AI hub, is a new managed workflow for 麻豆原创-RPT models. It handles context management and data access, thereby removing the need to build and maintain custom data pipelines. Teams register their data source and scenario configuration once at design time. Every inference is a single API call, with the workflow automatically handling data ingestion and context selection before routing the prediction request to the specified 麻豆原创-RPT model. This setup is designed to easily carry forward as the workflow expands to support additional tabular foundation models beyond 麻豆原创-RPT in future releases.

Prior Labs’ TabPFN-3.5-Plus
Business analysts and AI teams can turn structured business data into actionable predictions faster with TabPFN-3.5 Plus, now available in 麻豆原创 AI Core. Developed by Prior Labs, an 麻豆原创 company, the tabular foundation model helps organizations predict outcomes such as payment delays, supplier risks, customer churn, and upsell opportunities鈥攚ithout task-specific model training or tuning. Unlike large language models, which excel at language and knowledge tasks, TabPFN-3.5 Plus is purpose-built for the rows and columns that underpin critical business decisions.

The model works with data as it exists in business systems, handling missing values, mixed data types, and inconsistent fields without preprocessing. Through in-context learning, it uses labeled examples to make predictions from raw tabular data, including columns containing thousands of distinct values, such as product codes or customer identifiers. This reduces the manual preparation and trial-and-error configuration associated with traditional machine learning, helping teams move from business data to predictions in minutes.

For customers, this means a faster path to predictive insights with less model-development effort. Finance teams can anticipate payment delays, procurement teams can assess supplier risks, and customer-facing teams can identify churn risks and upsell opportunities. By making prediction more accessible, TabPFN-3.5 Plus helps organizations use their structured data to inform decisions and act earlier on emerging risks and opportunities.

Learn more here and . .

Enterprise Architecture Assistant

• Enterprise Architecture Management Agent

Enterprise architects can find answers and maintain more complete, up-to-date architecture inventories without leaving 麻豆原创 LeanIX using the Enterprise Architecture Assistant. Released in Q3, its Enterprise Architecture Management Agent enables architects to query and analyze inventory data across multiple dimensions, drawing on internal documentation from repositories such as 麻豆原创 LeanIX, Confluence, and SharePoint. Architects can identify missing fields and conduct gap analysis within their workflow, reducing the need to manually search documents or switch between applications. By bringing contextual research and analysis into 麻豆原创 LeanIX, the assistant helps shorten information searches, simplify navigation and data management tasks, and strengthen inventory quality and governance.

Get started .

The general availability for Enterprise Architecture Assistant is now underway in a phased approach.

Data Management Assistant

• Transition Data Quality Scope Agent
• Transition Data Quality Evaluation Agent
• Transition Data Quality Prioritization Agent
• Transition Data Quality Validation Agent
• Transition Data Quality Automated Resolution Agent
• Data Management Orchestration Agent

Data management teams can identify, prioritize, and correct data quality issues across the transformation life cycle with the Data Management Assistant. Its Q3 lineup connects six agents within a governed workflow: the Transition Data Quality Scope Agent selects relevant KPIs, while the Transition Data Quality Evaluation Agent analyzes execution results to identify trends and severity. Teams can focus on the most important corrections with the Transition Data Quality Prioritization Agent, then identify source systems and verify proposed fixes through the Transition Data Quality Validation Agent. The Transition Data Quality Automated Resolution Agent executes corrections only after human approval, with the Data Management Orchestration Agent coordinating the specialized agents throughout the process. By combining ongoing monitoring, actionable recommendations, and sandbox validation before production deployment, the assistant helps teams reduce correction risks, detect emerging issues, and maintain data governance at scale.

Get started .

The general availability for Data Management Assistant is now underway in a phased approach.

Process Transformation Assistant

• Process Consulting Agent

Business users and process analysts can uncover opportunities for improvement and turn insights into prioritized actions with the Process Transformation Assistant in 麻豆原创 Signavio. It combines conversational process mining with data preparation, tailored recommendations, and structured value cases to reduce configuration effort and support faster decisions. In Q3, the Process Consulting Agent lets users explore processes through natural-language questions, using multi-agent reasoning to retrieve relevant data, perform analyses, and translate findings into contextual recommendations and next-best actions. This helps users without specialist analytical skills contribute to process improvement while freeing analysts from time-consuming searches. Estimated benefits of the agent include a 90% reduction in time spent on complex data searches per artifact and a five percent reduction in average time spent analyzing, designing, modeling, and monitoring a process.

Get started .

The general availability for Process Transformation Assistant is now underway in a phased approach.

ALM Dashboard Generation Agent

IT and operations teams can use the ALM Dashboard Generation Agent to create graphical dashboard cards from system and service data in 麻豆原创 Cloud ALM through natural-language prompts. The agent helps users build personalized, role-specific monitoring dashboards without extensive data expertise, making it easier to tailor views to operational needs and deliver relevant insights to each user. This can reduce dashboard creation and maintenance effort by 75% and increase business productivity by 25% through faster access to customized information.

Get started .

This capability is generally available.

ALM Alert Resolution Agent

Alert processors can use the ALM Alert Resolution Agent in 麻豆原创 Cloud ALM to analyze alert details, including error messages, thresholds, related issues, and system context, and gain a comprehensive view of the current situation. The agent automates initial triage, searches help documentation, knowledge base articles, and 麻豆原创 Notes, and provides recommended troubleshooting and resolution steps directly on the Alert Details page. This helps reduce mean time to resolution, lower manual triage and troubleshooting effort, improve system uptime, and standardize incident resolution with consistent, AI-guided workflows.

Get started .

This capability is generally available.

麻豆原创 Regulatory Insights

Compliance officers can use 麻豆原创 Regulatory Insights to manage complex and evolving compliance requirements. The solution extracts and structures control requirements from lengthy regulatory documents, compares regulatory versions to identify new, changed, or withdrawn requirements, and analyzes the extent to which existing internal controls provide coverage. AI-driven recommendations help identify potential controls for closing gaps, while validation and contextual analysis support accurate mapping of related regulatory and control language. APIs enable integration with existing compliance and risk management systems, helping organizations reduce manual effort, improve transparency, maintain consistent data, and strengthen compliance processes at scale. By helping teams identify regulatory changes earlier and understand complex documents faster, the solution can help reduce penalties or rework costs from missed changes by 25% and reduce the average time spent comprehending regulatory documents by 70%.

Get started .

This capability is available as beta release.

麻豆原创 Cloud ALM, AI-assisted operations score optimization

IT and operations leads responsible for 麻豆原创 S/4HANA landscapes in RISE with 麻豆原创 can use the AI assistant in 麻豆原创 Cloud ALM Operations View to understand Operations Index scores and their day-to-day impact. The assistant translates technical metrics, KPIs, and configuration data into clear, context-aware explanations, identifies the primary factors affecting current scores, and highlights the systems, configuration gaps, or runtime issues that need attention first. This helps operations owners prioritize actions, allocate effort effectively, and make faster decisions aligned with clean core principles without manually interpreting every dashboard chart or KPI.

Get started .

This capability is generally available.

Industry AI

Spare Parts Request Processing Agent

Spare Parts teams can use the Spare Parts Request Processing Agent to process requests from multiple input channels and initiate the creation of sales quotations in the target system. The agent identifies the appropriate parts and involves experts when request details are missing or ambiguous, helping ensure accurate responses without requiring specialists to manually manage every request. This reduces the time and effort required to respond to customers, shortens the time to create quotes, increases spare-parts sales, and improves customer satisfaction.

Get started .

This capability is available in 麻豆原创 Early Adopter Care.

Jonathan von Rueden is chief AI officer of 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

Procurement leaders operate in a very different environment than they did just a few years ago. Beyond cost and compliance, they must now strengthen resilience, manage supplier risk, support workforce strategies, and help businesses respond faster to changing market conditions.

Yet, many of the systems supporting these responsibilities remain disconnected. Supplier intelligence lives in one place, workforce information in another, and spend data often requires significant preparation before it can drive decisions.

At 麻豆原创 Connect, we announced a series of innovations across the 麻豆原创 Ariba, 麻豆原创 Fieldglass, and 麻豆原创 Concur portfolios designed to help organizations address those challenges: new AI assistants for spend analysis and contingent workforce management, embedded supplier risk intelligence through a new partnership with Moody鈥檚, a new solution extension with ORO Labs that orchestrates procurement across complex enterprise landscapes, and new 麻豆原创 Concur capabilities that simplify high-volume administrative work.

Together, these innovations point to a broader shift toward , a domain within the . AI connects information, insights, and execution, while people remain in control of the decisions that matter most.

Better spend decisions start with better visibility

Many procurement organizations still struggle to turn spend data into actionable insight quickly enough to support decision-making.

According to the 2026 Economist Enterprise report, , sponsored by 麻豆原创, digital transformation is now the top strategic priority for procurement over the next 18 months, and improving spend analytics is one of its biggest drivers, cited by 53% of executives. These findings reflect growing pressure to improve decision-making and business impact.

Earlier this month, 麻豆原创 announced the availability of 麻豆原创 Ariba Spend Analysis and Insights, giving organizations a new way to bring together spend data across 麻豆原创 Ariba, 麻豆原创 Fieldglass, 麻豆原创 Concur, 麻豆原创 Cloud ERP, and other systems into a single foundation for analysis and action.

Building on that foundation, we announced the new Spend Analytics Assistant at 麻豆原创 Connect. Planned for general availability in Q1 2027, it helps procurement teams identify opportunities for cost savings, compliance, supplier consolidation, and risk mitigation. Rather than searching through reports, teams see highlighted areas that merit attention, along with supporting context to help them prioritize and act, so they spend more time on decisions and less on data preparation.

Bringing supplier risk into everyday decisions

Supplier risk is now a strategic concern across every industry, yet risk data often remains disconnected from the sourcing and purchasing decisions where it matters most.

At 麻豆原创 Connect, we also announced a new partnership with Moody鈥檚 that embeds supplier risk monitoring directly into . By bringing financial health, operational risk, and regulatory status into the same environment where supplier performance and relationship data are managed, procurement teams can evaluate risk as part of everyday decision-making rather than periodic reviews.

The partnership also strengthens the foundation for AI-powered procurement. When risk information is embedded directly within procurement workflows, it becomes part of the business context used to help identify emerging issues and support earlier intervention. This gives teams a more complete view of supplier relationships and greater ability to respond before potential disruptions affect operations.

General availability is planned for Q4 2026.

Connecting procurement across complex enterprise landscapes

The disconnection that slows procurement decisions often extends beyond data; it also shows up across the systems themselves. For many organizations, procurement unfolds across a mix of 麻豆原创 and third-party applications, each supporting different parts of the process. Coordinating work across that kind of landscape, without disrupting the systems teams already rely on, can be one of modern procurement鈥檚 more persistent challenges.

To solve this, 麻豆原创 announced a new solution extension developed with ORO Labs: 麻豆原创 Ariba Procurement Orchestration by ORO Labs. It provides a purpose-built orchestration layer that connects procurement experiences, workflows, and systems across complex environments spanning both 麻豆原创 and third-party applications.

For employees, this means a more unified experience and a simpler way to initiate and navigate procurement work. Low-code/no-code workflows let teams adapt processes as business requirements and policies change, without disrupting underlying systems. Organizations can also apply agentic AI within these workflows while preserving the controls, approvals, and human oversight that enterprise operations depend on.

The solution extension works alongside Joule Work, with Joule Work serving as the primary engagement layer. General availability is planned for November 2026.

Making external workforce management more proactive

For many organizations, contingent workers have become an essential part of how they scale skills, execute projects, and adapt to changing business needs. Managing those workers, however, often requires coordination across procurement, HR, hiring managers, suppliers, and compliance teams.

We also announced the new Contingent Workforce Assistant, designed to bring greater visibility to that process. Planned for general availability in Q4 2026, the assistant helps organizations manage the contingent worker life cycle by surfacing issues such as onboarding delays, pending approvals, compliance requirements, and other exceptions before they affect assignments. By bringing relevant information into a single experience, teams can address issues earlier while maintaining governance and auditability.

This proactive approach addresses one of the persistent challenges in workforce management: identifying issues early enough to act before they become operational problems.

Simplifying travel and expense management through targeted automation

Not every productivity challenge in spend management requires a major transformation initiative.

In many organizations, the greatest source of friction in travel and expense management comes from routine administrative work, such as retrieving receipts, locating invoice images, preparing audit documentation, and reviewing user permissions. Individually small, these tasks collectively consume significant time across finance, audit, compliance, and expense management teams.

To address this, 麻豆原创 Concur announced new additions to . Building on capabilities already used to support tax, audit, compliance, and reporting, 麻豆原创 plans to introduce new micro-apps that help organizations export employee expense information, simplify access-governance reviews, and retrieve vendor invoice documentation in bulk.

The approach is intentionally practical: rather than redesigning entire processes, the micro-apps target specific operational pain points. Customer adoption of the existing micro-apps points to strong demand for this focused automation.

Additional micro-apps are planned for general availability in Q4 2026.

Turning spend intelligence into action

Most organizations have plenty of spend data. The persistent challenge is turning that information into timely decisions and meaningful business outcomes.

The innovations announced this year at 麻豆原创 Connect address that challenge from multiple angles, improving visibility into spend, bringing risk intelligence closer to procurement decisions, connecting procurement processes across complex enterprise landscapes, improving external workforce management, and reducing administrative effort across travel and expense.

Better decisions require better visibility, stronger context, and fewer barriers between identifying an issue and responding to it. This is the goal of Autonomous Spend Management: applying agentic AI across every spend process so disconnected workflows operate as one intelligent, governed system.

This matters more as procurement takes on a larger role in driving resilience, growth, and business performance. For organizations that can identify opportunities faster, respond to risk earlier, and spend less time on manual coordination, spend becomes a source of advantage rather than a cost to control.

Baber Farooq is chief marketing officer for Spend Management at 麻豆原创.

鈥淚t’s time to show you the Autonomous Enterprise in action underpinned by great customer stories,鈥� 麻豆原创 CEO Christian Klein told his keynote audience at the start of the event.  

Klein reminded the audience that 麻豆原创 introduced the Autonomous Enterprise at its 麻豆原创 Sapphire conference in May 2026 as 鈥渢he beginning of better.鈥� Since then, he said, 麻豆原创 has worked with hundreds of customers to implement Joule Work as the AI engagement layer for the Autonomous Enterprise. 

A new UI for AI 

鈥淭his month, we are rolling out Joule Work for all customers,鈥� Klein said, describing it as a new UI for AI. He noted that for decades, users had to learn how 麻豆原创 worked, but with Joule Work, 麻豆原创 learns how people work. 鈥淔rom now on, the more than 400 million 麻豆原创 users can talk in their own language to 麻豆原创, and Joule Work gets the job done.鈥� 

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Klein explained that Joule Work is based on three pillars: conversations, where users ask questions and delegate tasks to agents; spaces, which display content, insights, and shared work; and Joule Studio, where customers extend Joule or build agents tailored to their own processes.   

He called 麻豆原创鈥檚 deep business and data context its 鈥渟ecret sauce.鈥� That context includes 麻豆原创 Knowledge Graph, which maps more than 7 million data fields, 50 million tables, more than 50,000 public and private APIs, 400 data products, and extensive process knowledge. 鈥淭hat’s why Joule doesn’t guess,鈥� he said. 

麻豆原创鈥檚 110,000 employees are already using Joule to perform hundreds of thousands of tasks every day, he shared: 鈥淲e are already seeing productivity gains in central functions like HR, finance, and procurement of over 20% and even bigger gains in more complex analytical and planning tasks.鈥� 

麻豆原创 Autonomous Suite 

Klein then turned his attention to the 麻豆原创 Autonomous Suite, the second layer in the Autonomous Enterprise. The suite initially covers five domains: finance, procurement, supply chain, human resources, and customer experience. 麻豆原创 Autonomous Suite is designed for work where a good answer is insufficient and 鈥渢he right answer鈥� is required every time, he said.  

鈥淟LMs are probabilistic and only give you the most likely answer,鈥� Klein said. He contrasted that probabilistic approach with 麻豆原创’s embedded agents. Because the agents operate within deterministic application workflows, they use the same data, follow the same rules, and leave the same audit trail as the applications themselves. Customers can choose the level of autonomy appropriate to each process, retaining human approval where risk is high and allowing greater automation for routine work. 

Since 麻豆原创 Sapphire, Klein said 麻豆原创 has created more than 20 Joule Assistants and over 200 Joule Agents, and is aiming for more than 400 agents by year-end. 鈥淏ut the real story isn鈥檛 the number of agents. It鈥檚 the business outcomes they deliver,鈥� he said. 

麻豆原创 Business AI Platform 

鈥淭hese agents can only understand your business so well because of the 麻豆原创 Business AI Platform,鈥� Klein said. 鈥淭he platform gives customers the best frontier model for every agentic use case, the deepest out-of-the-box business context and 麻豆原创-managed governance.鈥�  

Building on Klein鈥檚 remarks, Manoj Swaminathan, president and chief product officer of 麻豆原创 Autonomous Suite, described the 麻豆原创 Business AI Platform as the infrastructure that enables the Autonomous Enterprise. The platform is organized around three capabilities: building agents, contextualizing them with business data, and governing how they operate. 

Swaminathan explained how the context layer makes agents more accurate. Joule Studio spans no-code and professional development and offers access to more than 70 models, a strategy he said reduces lock-in and controls token costs.  

鈥淛oule Studio is natively integrated with our context layer,鈥� he said. 鈥淪o, the agents you build are more accurate, faster to deploy, and more efficient to run. Our knowledge graph is the map with the processes, data flows, and business tools. The agents find the right processes among thousands and the right information among over 7 million feeds. 麻豆原创 Business Data Cloud brings that understanding to all your data, one consistent view, the processes, policies, and logic to make it meaningful.鈥� 

Swaminathan said no AI can compensate for a broken data model. To help agents understand customers鈥� business rules, 麻豆原创 Domain Models are trained on 麻豆原创鈥檚 code. For predictions, 麻豆原创 provides two engines grounded in how a customer’s business operates: 麻豆原创-RPT, 麻豆原创’s model family trained on its business data, and the Prior Labs tabular AI model.

Jerseys from Jersey 

During his section of the keynote, Jan Gilg, global president of Customer Success & Americas, used an imaginary startup 鈥� 鈥淛erseys from Jersey鈥� 鈥� to invite a handful of customers to the stage to answer his questions and validate aspects of 麻豆原创鈥檚 Autonomous Enterprise strategy.  

The customers included Novartis, which is piloting Joule sourcing agents for bid analysis and negotiation support; Morgan Foods, which described a supply-chain transformation built on 麻豆原创 S/4HANA and 麻豆原创 Integrated Business Planning; Nestl茅, which emphasized the importance of a unified digital core; and 麻豆原创 customer and partner PwC, which spoke about the value of the Industry AI portfolio from 麻豆原创. 

Summing up their comments, Gilg noted that the elements of the 麻豆原创 Autonomous Suite 鈥渁re more than a collection of functional components. It鈥檚 one system working together like connective tissue that provides connectivity, context, and control.鈥�

Gilg also made several product announcements, including the launch of 麻豆原创 Pay, an embedded payments service that can automatically execute and reconcile supplier payments once an invoice is approved. He also announced 麻豆原创鈥檚 planned acquisition of TechWolf, a Belgian workforce-intelligence startup designed to help close skills gaps. 

Security and governance 

In the closing section of the keynote, 麻豆原创 Chief Security Officer Marielle Ehrmann focused on trust and governance. She noted that ransomware handovers that once took eight hours now take 22 seconds with AI, and while 83% of enterprises use AI daily, only 13% have real visibility into it.  

鈥淭he biggest threat to companies is not malicious AI; it鈥檚 unmanaged AI,鈥� Ehrmann said. To counter that threat, she said every agent should have a verified owner, a risk tier, and scoped access rights, supported by zero-trust checks on identity, purpose, and data sensitivity as well as human oversight. 

She emphasized that 麻豆原创 Business AI Platform incorporates regulatory and localization requirements, applies user authorizations, keeps customer data from being used to train foundation models, and supports three levels of oversight: human-in-the-loop approval, human-on-the-loop monitoring, and human-in-command override authority. 鈥淭he more autonomously an agent operates, the tighter the oversight applied,鈥� Ehrmann said, calling the principle 鈥渇reedom within boundaries.鈥� 

A clear shift among customers 

Wrapping up the keynote, Klein emphasized that trust is key to everything 麻豆原创 does and has been for over 50 years. 鈥淲e are delivering the most trusted and governed AI in the industry,鈥� he said, but added, 鈥淎I also requires significant change management to ensure it delivers the desired outcomes for your business.鈥� 

That is where 麻豆原创鈥檚 business transformation solutions come in, he said. 麻豆原创 Signavio measures process value before and after a change to help justify token spending; 麻豆原创 LeanIX discovers and tracks AI agents; and 麻豆原创 WalkMe supports user adoption and trust. Klein also highlighted an agent-led migration toolchain. Together, he said, these capabilities mean that the days of multi-million-dollar migration spend are gone.

鈥淭his is no longer the start of something better. This is better,鈥� Klein told the audience. He described a clear shift among 麻豆原创 customers: 鈥淔rom manual steps to connected workflows, from reactive decision-making to predictive insights, from humans in spreadsheets to humans in and on the loop–all powered by our AI platform to keep AI accurate, governed, and extensible.鈥�

And, most importantly, Klein said, 鈥淚ts ready for you today鈥� This is the Autonomous Enterprise.鈥� 

Joule Work, Joule Assistants and 麻豆原创 Business AI Platform help organizations turn business AI into trusted outcomes across every core function.

LAS VEGAS 鈥� (NYSE: 麻豆原创) today showcased the Autonomous Enterprise in action at 麻豆原创 Connect, demonstrating how Joule Work and Joule Assistants generate value for customers across every line of business.

“This is a breakthrough moment for business AI,” said Christian Klein, CEO of 麻豆原创 SE. “The Autonomous Enterprise has arrived. With Joule and 麻豆原创 Autonomous Suite, built on 麻豆原创 Business AI Platform, we’re helping organizations like Novartis, Morgan Foods, Nestl茅 and PwC connect intelligence across every function to deliver trusted business outcomes.”

At 麻豆原创 Connect, these customers shared how the Autonomous Enterprise is already driving measurable value across their businesses:

• 鈥淲e’ve co-developed and recently launched a pilot of 麻豆原创’s Joule Sourcing Assistant,鈥� said Christoph Buerki, Head of Procurement, Novartis. 鈥淥ur vision is for AI agents to handle the manual heavy lifting, enabling sourcing professionals to focus on strategy, supplier engagement and value creation.鈥�
• 鈥淎t Morgan Foods, we鈥檙e already putting the pieces in place for agentic adoption, especially through our technical foundation. The biggest shift will be moving from reactive to predictive, where agents can connect demand signals, inventory levels and supplier status faster than ever before,鈥� said Steve Hankins, Chief Financial Officer, Morgan Foods, 鈥淥ur superpower will come from people on top of agents. Our approximately 1,000 employees are what make our supply chains work鈥攖he technology helps make them even better.鈥�
• 鈥淥ur 麻豆原创 S/4HANA transformation across 140 countries is creating a connected digital core with consistent processes, trusted data and greater visibility across markets and functions. With that foundation, Joule and the Accounts Receivable Assistant can automatically prioritize collections work based on risk and business context, recommend the next best action and help our finance teams respond faster to change,鈥� said Luca Dell’Orletta, Global Head IT Innovation & Enterprise Architecture, Nestl茅.
• 鈥淚n professional services, billing can be one of the more error-prone processes, with accuracy playing an important role in the client experience. 麻豆原创鈥檚 Billing Assistant brings greater intelligence to this critical process, helping identify potential issues earlier so our people can deliver better client outcomes and build stronger relationships,鈥� said Jill Notz, Chief Accounting Officer, Controller, Business Services Leader, PwC. Building on the success of the Billing Assistant, PwC and 麻豆原创 are into new areas of joint AI innovation.

麻豆原创’s Autonomous Enterprise Scales Up

Joule Work is beginning its customer rollout as a new AI user interface that lets employees interact with 麻豆原创 solutions in their own language and complete work across 麻豆原创 and third-party data. Grounded in business context from 麻豆原创 Knowledge Graph, which maps more than 7 million data fields, Joule provides information users can rely on. At 麻豆原创, Joule Work is already live with 110,000 employees, with the company seeing 20% productivity gains across finance, HR and procurement. A select group of customers also is using Joule Work in their business environments.

• 鈥淧artnering within the Early Adopter Program for Joule Work Desktop has delivered more momentum in just a few months than we saw in all the years before,鈥� said Peter Wied, Director of Complexity Management at Reinhausen. 鈥淲e see huge potential in making access to our 麻豆原创 S/4HANA data significantly easier and faster through natural language. This has the potential to permanently transform how our teams interact with business data.鈥�
• 鈥淭he dedicated team has been phenomenal. Their proactive communication, focus on accelerating architecture activation, rapid remediation of defects and overall support have enabled us to become Joule ready much sooner than would have been possible had we needed to navigate these challenges on our own,鈥� said Keith Smith, 麻豆原创 Platform Architect, CONA Services. 鈥淭he CONA team is excited about the progress made to date and looks forward to exploring and testing Joule’s capabilities in greater depth over the coming months as we continue our adoption journey.鈥�

When a query is not enough, Joule Assistants execute operational work directly within business workflows, using established data, authorizations and audit trails. Customers control the level of autonomy. 麻豆原创’s approach is also open: through the Agent2Agent protocol, Joule can connect with third-party AI and agents, bringing governed business context into broader AI workflows.

At 麻豆原创 Connect, 麻豆原创 showcased how these capabilities generate outcomes across 麻豆原创 Autonomous Suite:

• In finance, controllers can automate revenue recognition, disclosures and trade compliance, with early deployments of the International Trade Assistant reducing trade classification effort by up to 50%.  
• In supply chain, planners can turn multi-hour disruptions into one-click resolutions across planning, manufacturing and logistics.
• In HR, leaders can determine the best way to close workforce gaps, while employees can make smarter benefits choices tailored to their needs.
• In procurement, decision-makers can identify savings opportunities against peer benchmarks and manage the full contingent workforce life cycle.
• In customer experience, organizations can turn customer engagement into sales opportunities with AI-powered campaigns, quoting and customer workflows.
• Across industries, 麻豆原创 is collaborating with customers to apply AI to specialized industry processes, helping organizations in manufacturing, retail, commodity trading and asset-intensive industries improve decision-making, automate routine actions and build more resilient operations.

The Platform Powering Business AI You Can Trust

麻豆原创 Business AI Platform gives organizations one place to build, contextualize and govern AI across 麻豆原创 and third-party environments. With Joule Studio, business users and developers can build and extend skills and agents from no-code to pro-code workflows, choosing from more than 70 AI models. 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph ground those agents in trusted business data and context, helping organizations select the right model for each use case and better manage AI value and consumption.

麻豆原创-managed governance helps organizations scale AI with transparency, accountability and control. 麻豆原创 AI Agent Hub in 麻豆原创 LeanIX solutions provides a central inventory of enterprise agents, while 麻豆原创鈥檚 independently ISO/IEC 42001-certified governance architecture supports regulatory requirements, established authorizations and auditable AI operations.

To help organizations adopt AI at scale, 麻豆原创’s Business Transformation Management portfolio, including 麻豆原创 Signavio, 麻豆原创 LeanIX and 麻豆原创 WalkMe solutions, provides the toolchain to analyze processes, govern applications and agents, and drive adoption across the enterprise.

麻豆原创 Brings Embedded Payments to Cloud ERP

麻豆原创 also announced that it is entering the payments market with 麻豆原创 Pay, powered by Tereina, an 麻豆原创 company. Embedded directly in 麻豆原创 Cloud ERP, 麻豆原创 Pay executes and reconciles payments within the same workflow when an invoice is due, reducing manual file handling and external processing dependencies, while giving customers greater control.

Partners Expand the Reach of Autonomous Business

麻豆原创 is extending the Autonomous Enterprise beyond 麻豆原创’s own application portfolio through new partnerships that bring specialized intelligence directly into 麻豆原创 workflows.

A new partnership between 麻豆原创 and Moody鈥檚 Corporation is designed to embed Moody鈥檚 supplier risk monitoring data into 麻豆原创 Ariba Supplier Lifecycle and Performance. This gives procurement teams continuously updated insights into supplier financial health, operational risk and regulatory status at the point of decision. Organizations will be able to move from periodic risk reviews toward continuous monitoring, enabling Joule Agents to flag supplier risk signals and surface alternatives before disruption affects the business.

An expanded partnership with ORO Labs brings procurement orchestration to 麻豆原创 customers as a solution extension. 麻豆原创 Ariba Procurement Orchestration by ORO Labs is a purpose-built orchestration layer that connects procurement workflows end to end across heterogeneous enterprise environments, giving business users a seamless experience for intake, actions and visibility.

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About 麻豆原创

As鈥痑 global leader in enterprise applications and business AI, 麻豆原创 (NYSE: 麻豆原创)鈥痵tands at the鈥痭exus鈥痮f business and technology. For over 50 years, organizations have trusted 麻豆原创鈥痶o bring out their best by uniting business-critical鈥痮perations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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We believe this recognition reflects 麻豆原创鈥檚 long-term investment in helping manufacturers connect sourcing, engineering, planning, and supplier collaboration on a single foundation.

Direct materials procurement doesn鈥檛 get enough credit for how hard it actually is. Unlike indirect spend, direct materials sit at the intersection of engineering, planning, finance, and supplier relationships鈥攐ften all at once. A design change triggers a sourcing event. A commodity price spike ripples into contract terms. A supplier quality issue lands on the production floor before procurement even knows it happened.

The organizations that manage this well don鈥檛 just have good sourcing tools. They have connected systems, trusted data, and the ability to act quickly when conditions change. Solving for that complexity is what 麻豆原创鈥檚 direct spend portfolio is built to do.

Connecting design, sourcing, and execution

麻豆原创鈥檚 direct spend offering is built with an industry-specific lens. A cookie-cutter approach to procurement business processes does not work for direct materials.

An engineering-oriented industry like automotive or aerospace, where a design change can trigger a sourcing event, has fundamentally different needs than a brand- or asset-oriented industry such as consumer goods or chemicals, where commodity exposure and supplier scale dominate. 麻豆原创 builds for both, with purpose-built offerings tuned to how each actually works.

What our offerings share is a single, unbroken data model running from new product introduction through sourcing, contract award, and purchase order execution, all within 麻豆原创 Business Suite: no bolt-on connectors, no manual handoffs between systems.

This matters more than it might seem. Data fragmentation across the design-to-source-to-settle workflow remains one of the biggest barriers to AI delivering real value in direct spend. When procurement decisions are connected to engineering, planning, supplier, and financial data on a shared foundation, AI has meaningful context to work with.

A network and ecosystem that extends the core

The sourcing suites are the center of gravity, but what surrounds them is equally important for manufacturers managing complex, multi-tier supply chains.

麻豆原创 Business Network connects millions of trading partners across roughly 190 countries, with more than US$7.7 trillion in annual commerce flowing through it. The network supports forecast collaboration, purchase order execution, inventory management, logistics visibility, quality processes, and supplier communication. For global manufacturers managing complex supply chains, that scale can be operationally significant.

Customers are already seeing the benefits of a more integrated approach to direct spend. For example, selected 麻豆原创鈥檚 direct materials sourcing solution because of its close integration with core ERP processes, including bill of materials management, production planning, and cost calculation. By connecting sourcing more closely to product development and manufacturing workflows, the company is helping procurement, engineering, and suppliers work from the same foundation throughout the product life cycle.

Additional 麻豆原创 capabilities extend the process further across product development, commodity management, supply planning, supplier collaboration, financing, and analytics. Together, these capabilities help organizations manage direct spend as part of an integrated process that spans from early design collaboration through supplier payment, supported by a unified data foundation.

Taken together, these capabilities span a much broader scope than traditional sourcing software, from early design collaboration through supplier payment, all governed on 麻豆原创 Business Data Cloud as a unified analytics foundation.

Where AI stands today, and where it鈥檚 going

Today, 麻豆原创 delivers AI-powered supplier and item recommendations, invoice data extraction, category strategy insights, supplier risk scoring, and Joule capabilities embedded throughout the suite. These are production capabilities already helping customers streamline procurement processes.

Looking ahead, 麻豆原创 plans to expand its AI capabilities across the direct spend life cycle, with new agentic experiences focused on areas such as market intelligence, supplier discovery, RFx orchestration, negotiation support, contract authoring, and supply chain impact analysis. These innovations will be delivered through Joule Work and Joule Studio 2.0, a no-code/low-code environment built on 麻豆原创 Business AI that enables organizations to create and manage agents grounded in live business data and end-to-end process context.

The foundation underneath this road map is what makes it credible. Agents are only as good as the data and process context they can access. When that foundation is fragmented across disconnected systems, AI adds noise as often as it adds value. When it鈥檚 unified, agents can execute with greater relevance and accuracy.

Who this is built for

The 麻豆原创 Ariba offering for direct spend works with any ERP backend, with the added benefit of integrating out of the box with 麻豆原创 Cloud ERP. For aerospace and defense customers with data sovereignty requirements, 麻豆原创 offers deployment options for sovereign cloud that few vendors in this space support. For brand- and asset-oriented enterprises, a consolidated source-to-pay platform spanning both direct and indirect spend can simplify governance while reducing complexity.

For manufacturers operating complex global supply chains, the value of a natively integrated approach is straightforward: fewer manual handoffs, better visibility across functions, faster response to disruptions, and AI that can draw on connected business data rather than isolated point solutions.

What this recognition means to us

We鈥檙e grateful to the customers and partners whose real-world feedback shapes how we build these products. We believe an IDC MarketScape recognition reflects their experiences as much as it reflects our road map, and we don鈥檛 take that lightly.

This recognition energizes us. For more than 50 years, 麻豆原创 has helped organizations run their most critical business processes, and direct spend is one of the areas where that experience comes together most clearly. We鈥檒l continue investing in deeper integration, broader network capabilities, trusted AI, and the industry expertise our customers rely on. We鈥檙e grateful to the customers and partners that continue to help shape these solutions and help us deliver meaningful innovation across the direct spend life cycle.

Fang Chang is executive vice president and chief product officer for 麻豆原创 Procurement and External Workforce Solutions at 麻豆原创.
Baber Farooq is chief marketing officer for 麻豆原创 Spend Management.

*IDC MarketScape: Worldwide AI-Enabled Direct Spend 2026 Vendor Assessment, IDC #US54900426, September 2026.

The anniversary offers an opportunity to look beyond any single year and consider how procurement has changed since the first year of this survey in 2022. What stands out is that the function has not followed a simple path from tactical to strategic. Its mandate has broadened, but so has the expectation to prove its value: control cost, anticipate risk, strengthen supply continuity, and turn technology investment into measurable outcomes.

Five takeaways from the research help show where procurement has been and where it may be heading next.

Takeaway 1: cost never left the job description鈥攂ut it鈥檚 now more important than ever

In 2022, procurement leaders were still operating in the shadow of the pandemic. Supply chain risk ranked as the leading organizational risk, while COVID-19 disruption was the top driver of digital transformation.

As conditions changed, procurement鈥檚 agenda expanded to resilience, visibility, supplier diversification, and digitalization. But cost never disappeared. In 2022, cost savings led the areas where respondents believed procurement could deliver greater value. In the 2026 study, 54% of executives identify cost savings and optimization as procurement鈥檚 primary contribution to the organization. The line of questioning is not directly comparable year over year, but the broader pattern reveals that a more strategic mandate has not lessened the importance of financial discipline.

That is not a retreat. Modern cost management requires understanding demand, supplier economics, working capital, risk, and the operational consequences of commercial decisions. Procurement鈥檚 cost mandate has become more sophisticated, not less important.

Takeaway 2: digital transformation has moved from funding to execution

One of the clearest changes is what now stands in the way of transformation.

In 2022, budget was the leading barrier to digital transformation at 30.2%. By 2026, it dropped to fourth on the list, as ease of deployment became the primary concern of 71.7% of executives, a category that did not exist as a response option in 2022. Budget still matters, but it is no longer the defining obstacle.

Procurement spent years making the case for technology investment. The conversation has moved past 鈥淐an we afford it?鈥� to 鈥淐an we actually implement it?鈥� Procurement leaders are now challenged to embed technology across real processes, data environments, and organizations.

The motivation for transformation has also shifted. In 2022, pandemic disruption was driving both preparation and investment. Today鈥檚 expectation of flat-to-cautious growth is more structurally normal, but the pressure to do more with less remains a constant. The question for today鈥檚 procurement teams is whether they can deploy new technology effectively enough to generate repeatable business value.

Takeaway 3: AI has raised both the opportunity and the standard

The latest findings make the scale of the AI shift unmistakable. Sixty percent of executives name digital transformation as procurement鈥檚 top strategic priority for the next 12 to 18 months, up from 38% in , with agentic AI emerging as the most sought-after near-term technology.

But enthusiasm is now accompanied by greater scrutiny. As AI enters category management, intake, sourcing, and other workflows, procurement leaders need to be clear about the business outcome being improved, the data required, and the controls governing how AI acts.

The five-year view shows how quickly expectations have advanced. In 2022, digital technology broadly led the category-management improvement agenda at 35.8%. By 2023 and 2024, priorities had grown more specific, centering on cost analysis, scenario planning, and data quality. By 2026, AI-driven and predictive insight is the dominant category-management priority, cited by 66.6% of respondents.

Technology is shifting from helping procurement professionals find information toward helping them interpret choices and execute defined tasks. As that happens, the value case must move with it鈥攆rom access to information toward measurable improvements in decisions and outcomes.

Takeaway 4: strategic influence is becoming more operational

For years, procurement leaders have talked about earning a 鈥渟eat at the table.鈥� But the research suggests that where procurement sits on the organizational chart may be becoming a less useful measure of its strategic influence.

In 2022, the COO was already the largest procurement reporting line at 33.5%, with the CFO close behind at 29.3%. By 2026, the COO share has risen to 44%, while the CFO share stands at 24.4%, and reporting lines to the CEO have declined from 18.8% to 15.7%.

This suggests that procurement鈥檚 strategic relevance is increasingly expressed through operational execution. Influence comes from improving decisions across cost, supply, risk, and technology鈥攏ot from where procurement appears on an organizational chart.

The results across recent years reinforce that shift. In 2023, 53% of executives agreed that procurement effectively collaborated with the rest of the organization to meet the company鈥檚 vision. That rose to 75% in 2024 and 90% in 2025, pointing to growing integration between procurement and the wider business.

That growing integration does not necessarily mean procurement has secured a permanent strategic role. In 2024, 70% of executives said procurement was actively involved in developing the wider organization鈥檚 digital transformation strategy, although only 18% strongly agreed. By 2026, executive confidence in procurement鈥檚 influence over digital transformation had fallen to 68%, down from 91% in 2025.

The contrast is telling: procurement is becoming more integrated with the business, but greater visibility does not automatically translate into sustained strategic influence. A seat at the table matters less than what procurement delivers once it is there.

Takeaway 5: the skills equation has changed

Technology is not the only thing procurement teams have had to reinvent. The capabilities required to use it effectively have changed just as quickly.

In 2022, skills gaps were relatively dispersed across technology (34.6%), category knowledge (32.0%), risk management (26.8%), and customer experience (25.6%). By 2025, AI proficiency and ethics had emerged as the leading skills priority at 68.2%, far ahead of more traditional procurement competencies at 34.1%.

But there is another part of the skills picture that deserves attention. In that same 2025 research, curiosity was prioritized by just 10% of respondents. That stands in contrast to what I continue to hear from procurement leaders, who increasingly describe curiosity as one of the skills that will separate the teams that thrive in the AI era from those that merely use the technology.

Traditional procurement expertise is not obsolete. If anything, more capable technology increases the value of professionals who understand categories, suppliers, markets, and organizational change. AI fluency on its own is not enough. As technology takes on more work, professionals need the curiosity to question outputs, challenge assumptions, explore what is changing in a market, and bring business context to the decisions technology helps inform.

The opportunity lies in combining deep commercial knowledge with greater AI fluency. The procurement professional of the next five years will need to know not only how to use new tools, but when to challenge their outputs, how to apply business context, and where human judgment still matters most.

The next chapter is about proof

Five years ago, procurement transformation was heavily shaped by external shocks. Today, organizations are making more deliberate choices about AI, analytics, talent, and operating models while being asked to deliver savings, manage volatility, and improve productivity simultaneously.

The next chapter is about forging a clear connection between strategic and cost value, and between human expertise and AI capability. Five years of research suggest procurement has earned a broader role. The next five will be about proving what it can do with it.

Gordon Donovan is global vice president of Research, Procurement, and External Workforce at 麻豆原创.

Even as procurement鈥檚 responsibilities have expanded, its ability to deliver on them is being scrutinized more closely than ever. The 2026 Economist Enterprise report, , sponsored by 麻豆原创, found that confidence in procurement鈥檚 ability to collaborate effectively with the rest of the business fell from 90% in 2025 to 74% in 2026. That鈥檚 a significant drop in a single year, and it points to a gap between what procurement is being asked to do and what it鈥檚 currently equipped to deliver.

The same report also shows where organizations are focusing their investment. Category and demand management is expected to receive the second-highest level of digital investment among procurement disciplines over the next three years, behind only spend and performance analytics. That finding reflects a practical reality that trusted, timely, and complete spend data has emerged as a foundational requirement for procurement teams expected to deliver measurable value.

Today, 麻豆原创 is announcing the availability of , a new solution designed to help procurement leaders turn spend data into intelligent operations.

A unified foundation for spend data

麻豆原创 Ariba Spend Analysis and Insights helps give CPOs and their teams complete visibility into where and how the organization is spending. It can bring together spend data from across the business into a unified layer, drawing from , , , and , as well as non-麻豆原创 systems and external data providers such as market intelligence and economic indicators.

Rather than requiring teams to manually extract and prepare data from each source, the solution can handle that integration continuously, so users can focus on the insights themselves. Because it was built on , all data retains its original business context and relationships across applications, helping to create a trusted foundation for intelligent decisions and AI-driven operations. Spend data is classified to the United Nations Standard Products and Services Code (UNSPSC) standard and a customer鈥檚 own taxonomy, and enriched with corporate hierarchy information from Dun & Bradstreet on a weekly, soon to be daily, basis.

For many organizations, this represents a meaningful shift. Spend data is often spread across multiple systems, structured inconsistently, and difficult to reconcile鈥攁nd by the time insights are ready, the conditions they describe may already have changed. 麻豆原创 Ariba Spend Analysis and Insights changes where the work begins, so procurement teams can work from data that is already unified and actionable.

That foundation also matters increasingly for AI. According to the same Economist Enterprise report, 60% of executives identified digital transformation as procurement鈥檚 top strategic priority over the next 12 to 18 months, with more than half citing AI as the primary driver. One of the greatest barriers to harnessing AI is the lack of high-quality data. Clean, enriched, and classified spend data feeds into 麻豆原创 Business AI, enhancing its ability to deliver better results over time through Joule Agents and other intelligent tools.

What鈥檚 included at launch

Several capabilities are available from day one, including:

• Automated data ingestion from 麻豆原创-managed source systems鈥斅槎乖� Cloud ERP, 麻豆原创 Ariba, 麻豆原创 Fieldglass, and 麻豆原创 Concur鈥攚ith data accessible within minutes. Customer-managed source systems, whether 麻豆原创 on-premise, non-麻豆原创, or third-party data sets, are supported through flexible integration channels.
• Unified, classified, and enriched spend mapped to UNSPSC v25 or your organization鈥檚 custom taxonomy at 95% accuracy. AI and machine learning models are trained to learn your business, support multiple languages, and are continuously refined with human intelligence built over more than 20 years, with 麻豆原创 among the founding organizations in scaling enterprise-wide spend analysis. 麻豆原创 Ariba procurement benchmarking helps customers compare performance against industry peers and identify where improvement opportunities exist.
• Powerful analytics that help answer not just 鈥淲here did we spend?鈥� but 鈥淲here should we have spent?鈥� and 鈥淲here should we spend now and in the future?鈥濃�攕panning cost savings, risk exposure, compliance, ESG, and productivity.
• Joule can connect users to curated AI-generated recommendations and agents ready to execute within next-gen 麻豆原创 Ariba solutions, helping teams move beyond reporting toward guided action while staying connected to the systems and workflows where decisions are made.
• Native connectivity with 麻豆原创 source-to-pay solutions can make it straightforward to convert analysis into category initiatives with and drive action across other procurement workflows without the manual handoffs that typically slow execution.

An operating model discussion

Procurement鈥檚 mandate has expanded. Its data foundation needs to expand with it.

麻豆原创 Ariba Spend Analysis and Insights is designed to help procurement teams act with greater speed, advise the business with more confidence, and demonstrate value in terms that senior leaders can measure. This is not only an analytics discussion鈥攊t is an operating model discussion.

To learn more, join our webinar, 鈥淩ethinking Spend: The New Role of Spend Data in AI-driven Procurement,鈥� on September 24, featuring Rick Gardner of The Hackett Group. The session will explore how trusted spend data and AI can help procurement teams make better decisions and drive greater business value. .

Organizations that invest in a clean, connected data foundation today will be best positioned to capture the full value of AI tomorrow. With 麻豆原创 Ariba Spend Analysis and Insights, procurement can move beyond fragmented reporting and toward intelligent operations, using spend data not just to understand what happened, but to guide what happens next.

Looking for additional details? Explore our to learn more about the solution鈥檚 capabilities, data foundation, and analytics framework.

Callum Veness is senior director of Product Marketing for 麻豆原创 Procurement & External Workforce.

The hand-crafted stringing on the face of the racket, the customized design, the packaging and distribution, and the systems that enable each touchpoint are all part of a global supply chain that ensures the racket gets from the warehouse into the gear bag of the world No. 5 exactly when he needs it.

That journey鈥攆rom raw materials and global suppliers through manufacturing, warehousing, customs, and last-mile delivery鈥攊s a story. And 麻豆原创 helps power it.

The carefully curated racket, along with Wilson鈥檚 shoes, shirt, cap, and shorts de Minaur chooses to wear as he competes at the highest level, are just a few of the thousands of products Wilson manufactures and delivers to athletes and customers around the world.

Behind each product is an interconnected global operation spanning manufacturing, warehousing, retail, e-commerce, and B2B channels and a digital foundation that helps Wilson keep it all moving seamlessly.

Powering a global sporting goods business

Wilson is a global leader in sports equipment and apparel, with a legacy of more than a century of innovation across tennis, basketball, baseball, golf, and other sports. From developing high-performance equipment for the world鈥檚 best athletes to creating products for players at every level, Wilson combines deep sporting expertise, innovation, and craftsmanship to help athletes perform at their best.

For almost two decades, Wilson has trusted 麻豆原创 to run its global operations, connecting hundreds of employees across finance, sales, logistics, warehousing, and other critical retail functions.

Wilson’s 麻豆原创 landscape spans core enterprise resource planning (ERP), supply chain management, data and analytics, procurement, travel, global trade, integration and enterprise architecture. Its current environment includes 麻豆原创 ERP Central Component (麻豆原创 ECC), 麻豆原创 Analytics Cloud, 麻豆原创 Datasphere, , and 麻豆原创 Business Technology Platform, as well as 麻豆原创 Ariba, 麻豆原创 Concur, and 麻豆原创 LeanIX solutions, among others.

And Wilson’s 麻豆原创 digital transformation is continuing. The company is preparing for a major 麻豆原创 S/4HANA transformation beginning in 2027, which will unlock new capabilities across areas such as extended warehouse management (EWM), transportation, quality, and omnichannel operations. As part of its 麻豆原创 S/4HANA journey, Wilson uses Joule to assist with code conversion marking the beginning of introducing into its business operations

One connected foundation for a connected customer experience

Beyond professional athletes such as de Minaur that choose to utilize Wilson to perform on and off the court, Wilson’s global customers interact with the company in many ways鈥攖hrough retail stores, e-commerce, and B2B channels. Behind those experiences is a complex network of products, inventory, orders, warehouses, and business processes that need to work together.

Built for the pace of sport

The world of professional sports moves quickly. A tennis match can turn in a matter of seconds. Consumer goods organizations like Wilson run an equally dynamic and demanding global operation: designing, manufacturing, moving, and selling products to customers and athletes around the world.

A major event, such as a Grand Slam or a Wilson ambassador winning a tournament, can bring heightened demand for the products athletes use and fans want to buy. This requires Wilson to coordinate inventory, production, warehouses, retailers, and e-commerce across markets and respond quickly as demand changes. 麻豆原创 solutions such as 麻豆原创 Extended Warehouse Management, 麻豆原创 Global Trade Services, 麻豆原创 Concur and 麻豆原创 Ariba provide Wilson with the digital foundation to run smarter, operate faster, and respond to the ever-changing demands running a global retail business

This September in New York fans will see Alex de Minaur compete with his trusted Wilson racket and adorn his new customized Wilson kit. What they won鈥檛 see is the integrated ecosystem and the multifaceted journey behind every single racket, every pair of shoes, every piece of apparel鈥攁nd the 麻豆原创 technology powering it.

Tennis is a game of preparation. When I walk onto the court, every detail matters鈥攆rom how the racket feels in my hand to the kit I’m wearing. Knowing that Wilson and the technology behind their business makes sure everything is ready exactly when I need it, that’s the kind of confidence that lets me focus on competing at my best.

Alex de Minaur

Whether it鈥檚 a game of tennis or the demands of a global business, success depends on having everything working in harmony. With 麻豆原创, Wilson can perform at its best.

And on the court, Alex de Minaur knows he can trust that everything is in place when the moment matters most.

That is the leadership challenge procurement now faces: accelerating the pace and improving the quality of work while maintaining accountability across every supplier interaction and enterprise transaction. That challenge is especially significant because procurement teams are also being asked to control costs, manage risk, strengthen resilience, and support broader digital transformation efforts.

The urgency is real. In research from the 2026 Economist Enterprise report titled聽“,” sponsored by 麻豆原创, 56% of executives identified AI strategy as the main catalyst for procurement鈥檚 digital agenda. The study, which covered 2,648 C-suite leaders, also recorded lower confidence in the function鈥檚 ability to translate technology investments into consistently better outcomes.

What those findings suggest is that the next phase of AI adoption is not about access to technology. It is about building the governance, accountability, and data foundations needed to turn potential into measurable business value. The practical question is where to start.

Start with the outcome, not the technology

AI programs often begin with finding the best possible tool for a problem. Procurement leaders should reverse that sequence and focus on the desired outcome.

The first questions they should ask are, 鈥淲hat outcomes would impact the broader business, and what decision or workflow needs to improve?鈥� A sourcing team may need to shorten event preparation. A category manager may need earlier warning of price or supply changes. A purchasing organization may want to reduce off-contract buying. Each objective carries different data requirements, risk levels, and measures of success.

Defining the outcome first forces the question early, before deployment choices narrow your options. Leaders can specify which actions AI may complete, which recommendations require review, and which decisions must remain under human control. They can also set escalation rules for exceptions involving sensitive data, high-value commitments, supplier concentration, or regulatory obligations.

This turns governance from a final approval step into part of the operating design.

Build a connected data foundation

AI cannot provide dependable guidance when supplier records, contract terms, spend information, and risk signals are fragmented across systems. More importantly, an agent cannot safely execute work without the context that accompanies this information. Procurement needs unified data governance that covers common definitions, data ownership, access controls, and traceable sources. Without it, AI operates on assumptions rather than facts.

Perfection is not a realistic prerequisite, and waiting for it will stall progress. But organizations should be explicit about uncertainty. When information is incomplete, the system should surface that limitation or route the matter to a person rather than present an assumption as fact. As the Economist Enterprise research highlights, fragmented data remains one of the most significant barriers to realizing AI鈥檚 potential in procurement, and it is a barrier that governance can address.

Apply human oversight where it matters most

The right balance between human and AI involvement varies depending on the procurement activity. Routine, rules-based work can support greater automation, while strategic supplier decisions require a different standard.

The Economist Enterprise research shows that fewer than one in 10 respondents would give AI the lead across most procurement choices within three years. By contrast, 46% expect the technology to assist with tactical work, while people retain authority over strategic matters.

That balance reflects something procurement practitioners understand from experience. Data can indicate that a supplier offers favorable terms or strong performance. It cannot tell you whether that supplier will collaborate during a disruption, bring you new ideas before they to a competitor, or treat your business as a priority when capacity is tight. Those judgments depend on relationships, commercial context, and years of accumulated experience that no system fully captures.

As organizations adopt similar tools and draw from increasingly comparable data, the real source of differentiation will be how procurement leaders interpret those outputs and apply judgment.

Human review should therefore be concentrated where the consequences are greatest, not added indiscriminately to every automated step. Clear thresholds can protect control without recreating the delays AI is intended to remove.

Measure value and risk together

Responsible adoption will not scale through policy alone. Employees need to understand how AI changes their work, when to challenge an output, and who is accountable for the final decision. Procurement, finance, IT, legal, and operations also need a shared view of ownership before something goes wrong rather than after.

Metrics to determine success and failure should be defined before deployment. Cycle-time reduction, contract compliance, spend under management, user adoption, supplier performance, and risk response can all show whether a use case is working. These measures should be paired with indicators such as exception rates, human overrides, data-quality failures, and control breaches.

This matters because AI can produce visible efficiency without improving the decisions that matter most. In the same research, many executives reported that AI has yet to meaningfully improve procurement decision-making quality despite growing investment in the technology. That gap is the real opportunity.

Procurement leaders who link AI to specific outcomes, build connected data, assign clear decision rights, and prepare their teams to work alongside the technology will move beyond isolated automation toward something more durable. The organizations that get this right will not simply be the ones that automate the fastest. They will be the ones where AI amplifies judgement, relationships, and experience that procurement professionals have always brought to the table, and where accountability for the decisions that matter most remains firmly in human hands. 

Gordon Donovan is vice president of Research for Procurement and External Workforce at 麻豆原创.

This year at 麻豆原创 Sapphire, we shared our vision for the Autonomous Enterprise — the next evolution of how businesses run, where AI agents execute critical workflows so people can focus on innovation, customer value, and business growth.

This vision comes to life through a reimagined Joule Work, evolving from an AI assistant into the central workspace for enterprise AI. We also introduced the 麻豆原创 Autonomous Suite, bringing AI agents and assistants across core business functions to execute complex workflows with human oversight. With 麻豆原创 Business AI Platform, customers and partners can build, manage, and govern AI agents. And by expanding Industry AI, we’re delivering AI grounded in deep business context and domain expertise to solve industry-specific challenges.

Customers are already benefiting. IT division, Bosch Digital, integrated 麻豆原创 Joule for Developers directly into their coding workflows. Developers saw a 20% increase in productivity using Joule to automate routine coding tasks and optimize code. Joule also generates test cases, speeding up unit testing by 15% to 20% and freeing senior developers for high-value tasks. , the country鈥檚 leading airport operator, defines safety thresholds, service levels, and playbooks, and its agent, Smart Network for Operative Winter (SNOW), executes them. The SNOW agent is a winter operations system that integrates real-time weather, runway, and operations/maintenance data to automatically orchestrate work at Patagonian airports. The agent has improved runway safety, cut direct costs by 16%, and reduced administrative effort by 90%.

built a tool using  so its clients can better handle international tax rules by developing and managing their own custom AI agents and solutions. This way, PwC鈥檚 clients can focus on strategy while AI handles tax. PwC鈥檚 tool helped one pharmaceutical company handle VAT on international transfers 60% more efficiently.

Another customer, , a global fashion retailer, used an AI agent, built on 麻豆原创 Joule, to cut HR process cycle times by 40% to 60%. The agent helps employees quickly handle HR transactions, such as leave requests and payroll queries, through natural language conversations. Reducing time spent on administrative tasks allows HR teams to focus on strategic talent management. These are just some of the customers getting value. There are many more.

Now let鈥檚 dive into the releases from Q2 2026.

Please note that this article covers only AI offerings released from April 1, 2026, to June 30, 2026.

Joule

Joule Work
麻豆原创 Early Adopter Care program (registrations closed)

redefines how people interact with and execute end-to-end business processes. As the user engagement component of the Joule solution, it moves the user experience beyond fragmented, transactional interfaces toward a unified, intelligent way of working across 麻豆原创 and non-麻豆原创 systems. Its dynamic workspace adapts to users’ intent, helping them focus on outcomes rather than spending time finding information. And because it can delegate execution to AI, users will no longer need to coordinate work across multiple application interfaces manually.

Joule Work will allow users to express in natural language what they want to accomplish, triggering to coordinate teams of Joule Agents that will surface the right insights and automate routine work across business domains and systems to achieve the goal. This happens in intent-driven, adaptive workspaces built in real time that keep teams focused on driving decisions and impact. Joule Work can help reduce manual handoffs, shorten cycle times, and enable teams to turn decisions into actions faster. A key function of Joule Work is to connect users with Joule Assistants, which are like smart teammates organized by function. These assistants use context to intuit people鈥檚 intent and act by coordinating the appropriate Joule Agents across the business. Joule Assistants understand organizations deeply and can automate complex tasks within and across functions, freeing employees to address more strategic work.

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Joule Work mobile app
General availability

Employees who use 麻豆原创 on the go can rely on the Joule Work mobile app to interact with 麻豆原创 applications in natural language on their smartphones or tablets. Joule is integrated directly into the app, so a simple chat can surface the latest figures, help complete approvals or maintenance tasks, and support work across areas such as sales, HR, and supply chain processes without having to navigate multiple mobile apps. On iPhone and iPad, users can even start by saying 鈥淗ey Siri, ask Joule in Joule Work,鈥� then speak their question, which is passed straight to Joule for a response. This gives organizations a single, mobile-enabled entry point to Joule capabilities and lets employees gain insights and act on tasks across their 麻豆原创 solutions using everyday language.

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Voice for Joule
麻豆原创 Early Adopter Care program (registrations closed)

A new partnership with LiveKit delivers intelligent voice for Joule, extending the experience beyond the keyboard and into settings where work happens. This partnership helps advance 麻豆原创’s vision for the Autonomous Enterprise. With LiveKit, 麻豆原创 customers can use real-time voice capabilities in Joule and access reliable, always-on conversational AI. This brings voice AI to a full range of roles, devices, and environments, putting Joule within reach of employees whose work is done away from a keyboard.

Enhancements for Joule
Multi-system support for 麻豆原创 S/4HANA Cloud Editions

Joule now supports connecting multiple 麻豆原创 S/4HANA Cloud Private Edition systems or clients and multiple 麻豆原创 S/4HANA Cloud Public Edition systems within a single Joule formation.

Work seamlessly across different 麻豆原创 S/4HANA environments through one unified Joule interface, increasing flexibility and efficiency for organizations operating multiple systems. Administrators enable this feature by configuring system-specific destinations with naming conventions, including additional systems in the Joule formation via System Landscape, and mapping system identifiers in the Joule Admin Center.

Developers can build custom capabilities that leverage data and functionality from multiple back-end systems. Business users access and execute processes across all connected systems naturally within their workflow.

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Autonomous SCM

麻豆原创 Digital Manufacturing, AI-assisted production engineering
General availability

Production engineers can analyze error logs to identify root causes and generate resolution instructions for production processes using 麻豆原创 Digital Manufacturing. The feature also enables engineers to extend production processes via script tasks generated based on natural language input.

Organizations can reduce error analysis time for production process errors by 20%, reduce error analysis time for connectivity errors by 20%, and cut the time to handle a production process or connectivity error from 4.5 to 3.6 hours — while improving operating time from 92% to 92.92%.

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麻豆原创 Digital Manufacturing, AI-assisted description enhancement
General availability

Quality managers can generate clearer and more structured initial descriptions of complex issues using 麻豆原创 Digital Manufacturing. By reducing bias and subjective language, a more balanced and factual representation of the problem at hand is created. Users can also refine and rephrase initial rough descriptions, facilitating more effective follow-up and thorough investigation, and translate descriptions into different languages.

This offers organizations an up to five percent improvement in the efficiency of quality engineers during issue handling and resolution, and an up to 10% reduction in errors during problem handling.

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Autonomous Finance

Project Billing Price Verification Agent
Beta release

Billing specialists can quickly identify mismatches between agreed prices and billing amounts using the Project Billing Price Verification Agent in the manage project billing application of 麻豆原创 S/4HANA Cloud Public Edition.

The agent identifies the relevant contracts and statements of work for the related customer project, extracts key pricing data, and compares them with the values in the project billing request. It highlights discrepancies, provides context, and suggests corrective actions.

Organizations can reduce time spent resolving price discrepancies by 75%, cut revenue leakage from undetected incorrect billing by 75%, and improve cash flow while reducing days sales outstanding by fewer billing cycle delays.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted localized business data management
General availability

Accounts receivable managers can access and explore localized financial and logistics data through Joule using natural language, without leaving their daily workflows. The capability enables users to run complex reports using natural language instead of manual selection screens, and instantly filter, navigate, and explore results with AI-supported context awareness. This way, finance teams can reduce the amount of training effort required and increase productivity and confidence across the organization.

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Autonomous Spend

Expense Automation Agent
General availability

Expense Automation Agent helps employees who submit business trip expenses by creating a first draft of their expense reports. It automatically collects and adds transactions, fills in relevant fields using contextual information and past behavior, and lets employees quickly review and adjust before submission. Customers can reduce manual data entry, shorten report completion time by up to 30%, and allow employees to focus more on their core work.

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麻豆原创 Ariba Contracts, AI-assisted contract creation from documents and prompts
General availability

Contract managers who create and manage large volumes of agreements can use AI鈥慳ssisted contract creation from prompts to start new contracts more efficiently. The feature lets users enter a simple natural-language prompt directly in the contract creation flow, then proposes contract header fields in seconds for review and confirmation before finalization. Organizations can reduce the effort required to initiate contracts, provide a guided in鈥慶ontext experience, and build a scalable foundation for future AI capabilities while maintaining clear human oversight of each contract.

麻豆原创 Fieldglass, AI-assisted SOW worker role recommendations
General availability

Procurement specialists who manage statements of work can now define suitable worker roles more quickly. This feature applies generative AI to the SOW context, including scope, outcomes, and timelines, to propose relevant roles that users can review and refine. Organizations benefit from faster, more consistent SOW authoring, improved fit鈥憈o鈥憇cope, and clearer, better-governed worker role definitions.

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麻豆原创 Ariba Invoicing, AI-assisted multi-model data extraction
General availability

Procurement and accounts payable teams working in 麻豆原创 Ariba Invoicing can rely on multi鈥憁odel data extraction to capture invoice information more accurately. The feature leverages the latest large language models in the content extraction service to interpret and extract key invoice data, enabling a smoother capture process. Organizations gain a more reliable and efficient invoice processing experience, with improved data quality that helps reduce manual corrections and downstream errors.

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Concur Travel, AI-assisted policy rule generator
麻豆原创 Early Adopter Care

Travel program administrators who manage Concur Travel policies can set up and adjust travel rules more efficiently with the policy rule generator. By pasting existing policy text into an AI-based rule generator, they can automatically produce multiple rule classes and rules in a single step, then apply them via a guided wizard. Organizations save time on policy implementation, reduce configuration errors, and promote more consistent, compliant travel policies across their programs.

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Autonomous CX

Order Reliability Agent
Beta release

The Order Reliability Agent helps customer service and order management teams stay on top of order issues consistently. The agent continuously monitors orders in 麻豆原创 Order Management Services, detects risks such as failures or delays, and either takes automated corrective action or presents clear recommendations and root-cause insights for staff to review. Companies can cut the time spent analyzing and handling exceptional orders by around half. The agent can also reduce customer churn related to fulfillment problems by about 20%, helping create a more reliable order experience.

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麻豆原创 Revenue Growth Management, AI-assisted trade promotion creation
General availability

Key account managers who plan trade promotions in 麻豆原创 Revenue Growth Management can set up promotions more quickly. When they enter a promotion name in the relevant account context, the system proposes key details such as dates, promotion type, duration, and sell鈥慽n timing based on master data, historical promotions, and past user edits. Organizations can shorten promotion setup time by up to 75% and reduce data鈥慹ntry errors and rework by around 30%, improving both efficiency and consistency in promotion planning.

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麻豆原创 Revenue Growth Management, AI-assisted deal sheet generation
General availability

Key account managers can quickly turn promotion data into retailer-ready deal sheets. Starting from a single promotion, the feature fills in system-of-record fields, applies appropriate PDF or Excel templates, and checks that required information is present before the document is created. This helps organizations produce consistent, audit-ready deal sheets in seconds, reduce formatting and data-entry errors, and give account teams more time to focus on customer negotiations rather than document preparation.

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Joule with 麻豆原创 Order Management Services
Beta release

Operations managers and order management teams using 麻豆原创 Order Management Services can rely on Joule to handle everyday operational questions and tasks through simple natural language. By enabling conversational access to key data and actions across areas such as order processing, orchestration, sourcing, availability, returns, and flows, Joule provides real-time, role-aware guidance directly in the flow of work. Organizations benefit from faster access to relevant transactions and insights, can act earlier to prevent issues from escalating, and support smarter, more timely decisions that save both time and operational cost.

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麻豆原创 Engagement Cloud, email campaign duplication
麻豆原创 Early Adopter Care program

Marketing teams can duplicate existing email campaigns to speed up everyday execution. When a marketer copies a previous campaign, email campaign duplication carries over layout, branding, and technical settings, so they only need to update content such as copy or offers. This helps organizations reduce campaign setup time, keep branding and formatting consistent, and limit repetitive configuration work and related errors across channels.

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麻豆原创 Business AI Platform

Build

Joule Studio
麻豆原创 Early Adopter Care

The new Joule Studio gives product teams, architects, and developers a single place to extend, build, and integrate AI experiences from business intent through to production-ready solutions. It starts from the outcomes you want to achieve, uses your own processes and data for context, and connects out of the box across your application landscape. At the same time, it can generate product requirements and technical specifications from your company-specific context, apply eval-based, data-driven guardrails to AI coding assistants under 麻豆原创-managed enterprise controls, and remain open so you can work with third-party or 麻豆原创 models in the development environment that fits your needs.

There will be a migration path from the original Joule Studio to the new version to help customers transition without disruption.

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麻豆原创 S/4HANA custom code migration agent
General availability

ABAP developers and migration teams moving from 麻豆原创 ECC to 麻豆原创 S/4HANA can use the 麻豆原创 S/4HANA custom code migration agent to automate the complex, time-consuming process of migrating custom ABAP code from 麻豆原创 ECC to 麻豆原创 S/4HANA. Delivered as an agentic AI capability in 麻豆原创 Joule for Developers, ABAP AI, the agent runs 麻豆原创 S/4HANA readiness checks via ABAP test cockpit across entire custom code packages, interprets the findings, categorizes issues, and applies a mix of deterministic quick fixes and AI-based code changes with confidence scores, while recording all updates in transport requests for full traceability. High-confidence fixes are applied automatically, and lower-confidence proposals are added as comments for developer review, so teams retain control over final code quality while spending far less time on object-by-object analysis, freeing capacity for higher-value design decisions and overall migration governance.

and .

Contextualize and Reason

Generative AI hub, enhancements

The generative AI hub in the 麻豆原创 AI Core infrastructure integrates with hyperscaler-agnostic operations to improve accuracy and support enterprise-wide adoption of business AI.

Batch API enabling processing of high鈥憊olume foundational model (FM) workloads
Developers and platform teams working with 麻豆原创 AI Core can use the batch API to process high-volume foundational model workloads more efficiently. By submitting large collections of non-urgent AI requests as a single input file, they can run jobs asynchronously in the background. At the same time, 麻豆原创 AI Core writes results to an object store, ensuring real-time, fast, and responsive user experiences. This improves scalability for high-volume processing, simplifies the developer experience across different models and providers, and ensures fair, predictable throughput so that large jobs do not block smaller ones.

Inference observability service: centralized logging and feedback for generative AI workloads
Teams running generative AI workloads on 麻豆原创 AI Core can use the inference observability service to monitor, analyze, and systematically improve model outputs. The service centrally records prompts, responses, and key context with controlled overhead, lets developers and users rate the quality of each response, and attaches lightweight feedback. It also supports labels and filters so interactions can be easily discovered and exported as datasets for fine鈥憈uning, prompt engineering, and benchmarking. Organizations gain standardized transparency into prompt and response quality, reduce costs and effort by replacing ad hoc logging with a unified, compliant feedback channel, and accelerate iterative improvement using structured inference data stored in S3 or metadata-only mode, and managed via REST APIs for labels, feedback, and record retrieval.

Speech-to-speech
The availability of speech鈥憈o鈥憇peech (S2S) recognition helps agent and app developers build natural, end鈥憈o鈥慹nd voice experiences into 麻豆原创 applications.

New models available
New models are supported, including Gemini 3.1 Flash Lite, Claude Opus 4.7, GPT Realtime and Mistral Small, GPT 5.4, GPT 5.4-nano, and GPT 5.3-Codex.

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麻豆原创 Joule for Consultants, enhancements

Custom knowledge grounding connects 麻豆原创 Joule for Consultants to a separate grounding service that indexes organizational content via 麻豆原创 AI Core. It enables Joule to securely index and reference an organization鈥檚 methodologies, policies, templates, and delivery standards alongside 麻豆原创鈥慶urated knowledge. By grounding responses in company鈥憇pecific documentation, consultants can receive more accurate guidance that aligns with established ways of working across projects and engagements.

Expert workspace introduces personalized 鈥渆xperts鈥� that help tailor guidance for specific projects, domains, or workstreams. Context is retained across conversations, so users can switch between initiatives while preserving project-specific knowledge.

and .

麻豆原创 Document AI enhancements

Model selection
This new feature allows you to choose the large language model (LLM) used for document processing. The Default LLM reflects the best-performing model at any given time, while additional models such as Gemini 2.5 Flash and GPT-5 are also available. The list of supported models is updated frequently to ensure access to the latest advancements.

New standard document types
麻豆原创 Document AI workspace and OData V4 APIs now support three additional standard document types: learning certificate, order confirmation, and traffic violation notice. This expands the range of business documents that can be processed out of the box, reducing the need for custom configurations.

Configuration of document-level confidence ranges
麻豆原创 Document AI now supports configurable document-level confidence thresholds, making it easier to assess extraction quality at a glance. Custom confidence ranges 鈥� low, medium, and high 鈥� can be defined on the configurations tab of a schema version. When documents are processed, the overall confidence score is displayed in the document header with color-coding: red for low, orange for medium, and green for high confidence.

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麻豆原创 Domain Models

are AI models trained on 麻豆原创 domain knowledge, including code, data, metadata, business processes, architectural knowledge, and documentation. When customers initiate a query or want to create code, the models are designed to provide results firmly grounded in the 麻豆原创 context instead of relying on generic internet knowledge. Combined with context graphs and agents, the models bring deep 麻豆原创 knowledge to Joule, Joule Studio, Joule Agents, and 麻豆原创 applications.

麻豆原创 Domain Models will help:

• Create custom extensions in 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 Ariba: Developers in Joule Studio can use specialized models for 麻豆原创 S/4HANA and 麻豆原创 Ariba to understand and generate clean core-compliant code from natural language.
• Query information in 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 Ariba: Customers can use natural-language prompts in Joule to access customer data that is grounded in the underlying data models and the business context.

These capabilities will help create clean core extensions while preserving 麻豆原创 standards and governance. 麻豆原创 Domain Models are running under the hood of Joule and Joule Studio and are not directly exposed to customers.

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Govern

麻豆原创 AI Agent Hub enhancements

麻豆原创 AI Agent Hub gives organizations a single control pane for all AI agents, LLMs, and MCP servers across the enterprise. Featuring automated AI asset discovery across major platforms, including Microsoft, Google, AWS, and now ServiceNow and 麻豆原创 AI Core, alongside structured governance assessments and a verification badge that integrates directly with runtime solutions to control which agents and MCP servers are approved for use.

Looking ahead, we will expand into runtime observability and governance, identity and access control, agent-in-process mining, and workforce impact mapping to make the AI Agent Hub the central command center for AI governance at scale.

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Process Consulting Agent
General availability

Process owners, analysts, and operational leaders can turn process data into clear, practical insights without needing specialist analytics skills with the Process Consulting Agent. Users can ask questions in natural language, and the agent retrieves and analyzes relevant process information through a multi鈥慳gent system, returning structured findings along with suggested next steps. Organizations can cut the time spent searching complex data per artifact by up to 90% and reduce the effort to analyze, design, model, and monitor processes by around five percent, helping teams move more quickly from insight to action.

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Enterprise Content Research Agent
General availability

Enterprise architects and portfolio managers can quickly find and understand architectural information through the Enterprise Content Research Agent in 麻豆原创 LeanIX. By querying inventory data and related documentation across sources such as 麻豆原创 LeanIX, Confluence, and SharePoint, the agent highlights missing fields, supports gap analysis, and helps keep records complete and consistent, while leveraging MCP Server tools as needed. This reduces the time spent on informational searches and navigation, simplifies data management tasks, and supports stronger governance over architecture data across the landscape.

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WalkMe solutions, AI knowledge indexing
General availability

Digital adoption and enablement teams can use AI knowledge indexing to make internal documentation easily available to WalkMe鈥檚 AI services in a controlled way. The feature processes connected knowledge sources, such as web pages and files, extracts text content, and converts it into secure vector embeddings, enabling WalkMe鈥檚 contextual AI assistance to ground guidance in company policies, wikis, and procedures rather than generic models. Organizations can improve real-time compliance outcomes and see a 21% increase in procurement policy adherence, while giving employees faster, policy-aligned answers directly in their workflows.

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WalkMe solutions, AI knowledge referencing
General availability

Digital adoption teams and application owners receive app guidance that aligns with their company鈥檚 policies and standards with WalkMe鈥檚 AI knowledge referencing. When certain conditions are met, such as editing a specific field or completing a form, the feature retrieves relevant content from connected internal documentation so tools like AI SmartTips or chat can compare user input with best practices and provide tailored feedback. This allows organizations to anchor AI assistance in trusted company information and help employees access the right policy or governance details when needed.

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WalkMe solutions, pinned AI
General availability

Operations, HR, finance, and other business teams now benefit from on-screen assistance exactly where they enter data or complete tasks, using WalkMe鈥檚 pinned AI. By attaching AI smart tips to input fields and AI Launchers to specific elements, the feature provides contextual guidance in place, grounded in company knowledge sources, so users can continue their work without switching applications. Organizations can improve data quality across key forms and workflows, reduce errors and rework, and see measurable gains such as a 41% improvement in data quality.

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WalkMe solutions, on-demand AI
麻豆原创 Early Adopter Care program

Employees working across line-of-business applications can turn to WalkMe鈥檚 on-demand AI for quick answers or step-by-step support without leaving their current screen. Through a conversational in-app menu that travels with users across applications, they can ask questions, retrieve company knowledge, and trigger automations or Smart Walk-Thrus, keeping guidance and execution closely connected. Organizations benefit from faster access to trusted information and a measurable impact on quality, including up to a 41% reduction in time spent correcting ERP-related business tasks.

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麻豆原创 LeanIX solutions, AI-assisted enterprise architecture decision management
General availability

Enterprise architects and architecture review boards get faster, more consistent decisions with enterprise architecture decision management in 麻豆原创 LeanIX solutions. By providing context, such as transformation diagrams or landscape changes, they can ask the AI to generate a draft architecture decision entry that includes the relevant background, decision, and implications for stakeholders to review and approve. This reduces manual data extraction and authoring effort, streamlines collaboration on approvals, and helps ensure architecture decisions are documented and concluded in a timely, traceable way.

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麻豆原创 LeanIX solutions, AI-assisted fact sheet calculations
General availability

Enterprise architects and workspace admins using AI-assisted fact sheet field calculations in 麻豆原创 LeanIX solutions can quickly turn plain-language business rules into working calculations. When they describe the rule they need, the feature generates readable, commented code that is aware of their fact sheet types, fields, relations, and enums. Hence, calculations align with the actual workspace configuration. This helps teams move from a business question to a usable metric in minutes, increase self-service configuration, reduce reliance on JavaScript skills, and speed up the delivery and maintenance of calculated fields that downstream reports and views depend on.

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麻豆原创 LeanIX solutions, AI-assisted automation creation
General availability

Enterprise architects and workspace admins can transform plain-English workflow descriptions into working automations with AI-assisted automation creation in 麻豆原创 LeanIX solutions. When they describe the review, update, or governance flow they need, the feature generates the appropriate triggers, conditions, and actions with field mappings aligned to the current workspace configuration. This lets teams build and scale automations themselves, increasing EA productivity, reducing reliance on technical experts, and making it easier to keep key processes such as onboarding workflows and lifecycle checkpoints consistently automated.

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麻豆原创 Cloud ALM, AI-assisted document summary
General availability

Document summary helps project teams and engineers understand long documents more quickly. Within the documents application, users can trigger an AI-generated summary, review and edit it in a separate window, and then apply it as a persistent summary section in the document. This shortens the time spent manually reading and extracting key points, supports faster comprehension of complex engineering content, and enables quicker decisions without leaving the document workflow.

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麻豆原创 Signavio solutions, AI-assisted context analyzer, text-to-event matching, and sentiment analysis
General availability

The context analyzer helps process owners and analysts match free text with process objects, such as sales orders or purchase requisitions, to the corresponding process events in event logs. The feature links free-text records such as survey responses, feedback, comments, and tickets to the corresponding process events, so qualitative experience data appears alongside operational logs. This enriches process mining with unstructured text, reduces manual mapping work, and improves process analysis accuracy by around 30%, helping teams pinpoint bottlenecks and experience issues more effectively.

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Jonathan von Rueden is chief AI officer for 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

External talent, including contractors, consultants, and project-based specialists, is becoming a core component of how work gets done rather than a stopgap solution.

Leading organizations are responding by treating external talent less as a short-term fix and more as a standing part of workforce strategy. With 74% of employers worldwide , workforce planning has become less about filling roles in sequence and more about maintaining access to critical capabilities. This shift moves organizations to a workforce model that can respond quickly, scale efficiently, and align with long-term business priorities.

End of 鈥渢emporary鈥� talent

External workers have historically been brought in to meet short-term needs, helping fill gaps during peak periods or support one-off projects. While that approach still exists, ongoing volatility has proven that this is no longer sufficient.

Demand signals change quickly, transformation is continuous, and new skill requirements emerge faster than internal teams can adapt.

In this context, external talent provides a clear advantage. It gives companies access to specialized expertise on demand, helps accelerate innovation, and supports operations without overextending internal resources. It also allows leaders to rethink workforce composition to better balance stability with adaptability.

This shift mirrors the recent shifts seen in procurement and supply chain functions, where visibility and cross-functional integration have become drivers of long-term success. Workforce strategy is moving in a similar direction.

You can鈥檛 manage what you can鈥檛 see

As organizations expand their use of external talent, visibility remains essential. Many companies still manage contingent labor in disconnected ways, which makes it harder to understand where talent is deployed, what it costs, and how effectively it is being used. Without that visibility, workforce decisions remain reactive.

When organizations can see how external talent is deployed across business units, geographies, and projects, they can plan with greater confidence. This level of insight also supports stronger governance by improving compliance, supplier performance, and consistency from sourcing to offboarding.

In practice, organizations that invest in visibility often see measurable improvements in efficiency, productivity, and decision-making speed. More importantly, they begin to treat external labor as a strategic lever rather than a cost center.

From reactive hiring to predictive planning

Visibility is essential, but the real opportunity lies in turning workforce data into actionable insight.

AI is playing an increasingly important role in this transformation. By analyzing hiring patterns, project pipelines, and market signals, AI can help organizations anticipate future talent needs instead of reacting to them. This is especially valuable in environments where workforce decisions need to balance cost, speed, and quality. For example, organizations can use AI to:

• Anticipate external talent needs tied to major initiatives, such as ERP rollouts or expansion projects, before staffing gaps affect delivery
• Identify where external specialists can help address immediate skill gaps while longer-term hiring continues
• Analyze market signals and workforce composition to help guide insourcing vs outsourcing strategies
• Flag bottlenecks and make corrections in onboarding, approvals, or assignment start times that delay productivity and increase costs

As organizations look to make external talent a more strategic part of workforce planning, technology becomes increasingly important. helps organizations gain greater visibility into their external workforce, connect talent data across the enterprise, and use AI-driven insights to make more informed staffing decisions.

By bringing together workforce planning, services procurement, and external talent management, organizations can better anticipate skill needs, improve agility, and align workforce investments with business priorities.

More connected approach to talent

One of the most important shifts underway is how organizations think about workforce composition. Rather than treating external and internal talent as separate categories, forward-looking companies are managing both as part of a single ecosystem.

This integrated approach offers several advantages. First, it more closely aligns with business goals. Leaders can allocate resources based on outcomes rather than employment type, ensuring the right skills are applied where they create the most value. Second, it improves agility. When workforce models are designed to flex continuously, organizations can quickly respond to changing conditions without disrupting operations. Third, it enhances the employee experience for both internal teams and external contributors by streamlining processes and making them more efficient.

Technology plays a key role in enabling this shift and provides organizations with the tools to manage external talent alongside internal workforce data, improving visibility and supporting more data-driven decision-making. Solutions such as 麻豆原创 Fieldglass help organizations bring greater transparency, consistency, and insight to how external talent is sourced, managed, and aligned to business needs. While no single solution defines success, the ability to connect data, processes, and insights is increasingly important.

Building resilience in an always-on economy

Business no longer moves in predictable cycles. Demand shifts quickly, priorities evolve in real time, and skills gaps can emerge faster than traditional hiring models can address. In that environment, resilience depends on staying adaptable while keeping work moving.

That is why external talent is becoming a more strategic part of workforce planning. With finding skilled talent becoming increasing more difficult, many organizations are looking for ways to maintain access to specialized capabilities as business needs shift. External talent can help teams move faster, bring in targeted expertise, and sustain progress on critical initiatives without overextending the core workforce.

For many organizations, this reflects a broader change in mindset. External talent has moved closer to the center of workforce strategy, especially in areas where speed, specialization, and adaptability matter most. How well organizations plan for and manage that talent will shape their ability to execute, compete, and grow.

Amber Roth is vice president of GTM for 麻豆原创 Fieldglass.

Over the past several years, procurement has steadily expanded its influence inside the enterprise. As supply chains faced unprecedented disruption, procurement leaders became trusted advisors to the C-suite on resilience, risk management, and sustainability. Their visibility increased and so did the expectations placed upon them.

Now, the playbook is being rewritten once again.

Research from the 2026 Economist Enterprise Report titled , sponsored by 麻豆原创, finds that financial performance has reemerged as the primary benchmark of procurement鈥檚 success. Drawing on a global survey of 2,648 C-suite executives, the report found that 54% cite cost control as procurement鈥檚 greatest contribution to the business, up from 43% just one year earlier. The findings point to a function that is increasingly stretched, yet positioned to deliver measurable business outcomes鈥攊f it can navigate a difficult balancing act.

Cost control returns to center stage

The shift is not surprising given the environment. Persistent inflation, tariff uncertainty, and continued investment in supply chain resilience have pushed cost management back to the top of the executive agenda. At the same time, companies are investing in dual sourcing, nearshoring, and inventory buffering to reduce exposure鈥攕trategies that strengthen resilience but often raise costs. Procurement is expected to offset those increases elsewhere.

What makes this moment particularly challenging is that procurement鈥檚 broader responsibilities have not diminished. Teams are still expected to manage geopolitical risk, advance sustainability, and support digital transformation. The mandate has expanded significantly, often without a corresponding increase in capacity, tools, or operating model support. Delivering savings, limiting cost increases, and managing input costs while fulfilling a growing strategic role is the defining tension facing procurement leaders today.

AI is becoming procurement’s digital imperative

Technology, and AI in particular, is increasingly seen as the key to resolving tension. In the Economist Enterprise study, 60% of executives identified digital transformation as procurement’s top strategic priority over the next 12 to 18 months, up sharply from 38% in 2025. More than half (56%) identified AI as the primary driver of that transformation.

The emergence of agentic AI is accelerating expectations further. More than half of executives are planning to implement or evaluate agentic AI capabilities within the next 12 to 18 months. Unlike earlier generations of AI that focused primarily on generating insights, agentic AI introduces the ability to execute workflows鈥攆rom guided buying experiences to automated purchase order creation鈥攅nabling procurement to move beyond recommendations and drive actions.

Even so, executive expectations remain grounded. Only 9% of survey respondents want AI to lead most procurement decisions within three years. Procurement鈥檚 highest-value work continues to rely on human judgment, strong supplier relationships, and the ability to navigate complex trade-offs. AI plays a critical role in strengthening these capabilities, but it does not replace them.

Realizing that potential, however, requires the right foundation. Connected data, clear governance, and close collaboration across procurement, finance, IT, and operations are prerequisites for generating AI outputs that are reliable and accountable.

Category management takes on greater importance

As procurement balances cost pressures with broader business priorities, category management is emerging as a critical discipline. The report found that category and demand management are expected to receive the second highest level of digital investment among procurement disciplines over the next three years, trailing only spend and performance analytics.

This reflects the growing complexity of procurement decisions. Category leaders are no longer simply awarding projects to the lowest-cost supplier. They are expected to weigh cost, risk, sustainability, and supplier performance simultaneously鈥攁 level of complexity that demands better analytics and faster insight-to-action capabilities.

That level of nuance can strengthen procurement鈥檚 impact, but it can also slow execution. More sophisticated category strategies require better data, sharper analytics, and faster insight-to-action capabilities. The report found that category strategy has become the third most common source of process delays, behind only contracting and sourcing.

Success increasingly depends not on collecting more data, but on turning data into confident decisions quickly. Organizations that close that gap will be better positioned to execute strategy, not simply develop it.

Procurement’s strategic value is being tested

Perhaps the most striking finding in the report is a growing confidence gap. While nearly three-quarters of executives still believe procurement collaborates effectively across the organization, that figure dropped from 90% in 2025 to 74% in 2026. Confidence in procurement鈥檚 role in shaping digital transformation strategy also declined meaningfully over the same period.

These numbers do not signal a retreat from procurement鈥檚 strategic importance. Rather, they reflect a broader shift in how enterprise decisions are being evaluated. As AI democratizes access to data across the organization, more stakeholders have the information to question decisions and demand clearer evidence of value.

Procurement leaders are now expected to control costs, manage risk, strengthen resilience, and help guide AI adoption, often simultaneously and without additional resources. The question is no longer whether procurement belongs at the leadership table. It is whether procurement can consistently deliver the value expected of it across an expanding and increasingly complex set of priorities.

Procurement鈥檚 next chapter

The Economist Enterprise findings paint a picture of a function at a pivotal moment. Cost savings has returned as the primary mandate, yet procurement is still expected to manage risk, protect supply continuity, and lead digital transformation.

Meeting those expectations will require more than layering AI onto existing processes. It demands connected data foundations that make AI outputs trustworthy, visibility across suppliers and spending, and technology that enables teams to move from reactive decision-making to proactive intervention.

The leaders who will define procurement鈥檚 next chapter are those who can turn AI, data, and connected processes into faster decisions, stronger resilience, and measurable business impact.

Gordon Donovan is vice president of Research for Procurement and External Workforce at 麻豆原创.

The findings arrive as companies pour more resources into travel programs. Eighty-two percent of CFOs plan budget increases this year, while geopolitical tensions and cybersecurity risks reshape the travel landscape. Organizations are challenged to meet rising employee expectations while managing unprecedented complexity.

Business travel still delivers value, but expectations are evolving

Despite ongoing disruption relative to global travel, 93% of business travelers say that work trips positively impact their mental or physical well-being. The reasons are varied: 45% appreciate the break from their usual work environment and 44% experience a change of scenery as mental recharge. For 36%, business travel offers opportunities for face-to-face interactions with colleagues and customers that aren鈥檛 possible day-to-day.

Parents feel this even more acutely. Thirty-three percent say business travel provides a break from family or personal obligations, compared to 26% of non-parents. Parents were also more likely to cite access to hotel wellness amenities (33% vs. 25%) and fitness opportunities they don鈥檛 usually have (22% vs. 14%).

In an era shaped by burnout and digital fatigue, many workers see travel as an opportunity to reconnect with colleagues, customers, and themselves鈥攕uggesting employees increasingly view business travel as more than a transactional business function.

Yet the value employees place on travel exists alongside mounting uncertainty. Sixty-seven percent of business travelers say they are hesitant to travel for business this year, driven by safety concerns around geopolitical conflicts or tension (31%), the likelihood of travel disruptions such as flight delays or cancellations (28%), and worries over visas, immigration status, and digital IDs (16%).

Overtourism: the hidden cost of peak season travel

Nearly one in four travelers (24%) say overcrowding or overtourism has negatively impacted a business trip. In response, 87% deliberately avoid staying in tourist-heavy areas while travelling for business.

The impact is real: 43% of business travelers report higher costs in crowded destinations, 38% struggle with transportation in over-touristed areas, and 33% can鈥檛 book preferred options due to overbooking or limited supply. Additionally, 27% cite discomfort being surrounded by so many people, and 26% note greater safety risks or concerns about hostility toward tourists.

On the road: employees expect more support

The survey also reveals a fundamental shift in how employees view employer responsibility during business travel. More than a quarter (27%) of business travelers now hold their employer most accountable for protecting their safety while traveling for work, up from 18% in 2020 when a similar question was asked. This expectation is even more pronounced among younger generations, with only 35% of millennials and 33% of Gen Z holding themselves primarily accountable, compared to 47% of baby boomers.

Confidence in employer preparedness is mixed: only 58% of travelers are mostly or completely confident their organization could successfully extract them if trapped in a country due to a dangerous situation or emergency.

From the travel manager’s perspective, the situation is serious: 86% are concerned their organization is not doing enough to protect employee safety while traveling. Interestingly, only 38% of CFOs say their organization is completely responsible for ensuring employee safety while traveling for work鈥攖he majority view this responsibility as falling primarily on employees.

The greatest threat to business travel, according to travel managers, is the risk of sensitive information being hacked when traveling abroad (44%)鈥攎ore common than geopolitical conflicts (43%), last-minute delays (39%), or severe weather disruptions (39%).

Location tracking can help increase employee safety. While travelers are willing to share location data in general, there are conditions. Seventy-nine percent say they are at least somewhat comfortable with employers tracking their location while traveling for work to better protect their safety. But trust remains fragile: nearly one-third (29%) say real-time monitoring of location and expenses would cause them to lose trust in their organization鈥檚 leaders.

The findings highlight a growing balancing act: employees increasingly want reassurance and support from employers during travel, while also expecting transparency around how traveler data is used.

Rising budgets, missing tools, and backup

Finance leaders continue to recognize business travel鈥檚 strategic value. Nearly all CFOs surveyed (97%) say business travel is important to their organization鈥檚 overall growth strategy, and 82% expect their company鈥檚 travel budget to increase this year. But growing investment is also bringing greater scrutiny. Nearly nine in ten CFOs (89%) agree that travel managers need to better justify how business travel helps their organization to meet business goals.

Travel managers, however, say they are being asked to prove ROI without adequate organizational support. Eighty-four percent agree it鈥檚 impossible to fully meet their organization鈥檚 business goals without more backing from finance leadership. Their top needs include better data and insights to demonstrate ROI (44%), training on effective AI use (43%), greater buy-in on policy changes or new initiatives (42%), and updated technology solutions (40%).

This disconnect reveals a critical challenge: as organizations invest more in business travel, they risk undermining that investment by failing to equip travel managers with the tools and support needed to deliver measurable results.

The trust equation

The 2026 findings show business travel continues to deliver significant value for growth, collaboration, and employee experience. But they also reveal widening gaps between what organizations expect from travel programs and how employees experience business travel on the ground鈥攇aps that increasingly affect traveler well-being, duty of care, compliance, and confidence in leadership.

Organizations that can close those gaps through clearer communication, better governance, stronger traveler support, and tools employees want to use will be better positioned to build travel programs that employees trust and leadership teams can confidently support.

in the eighth annual 麻豆原创 Concur Global Business Travel Survey.

Charlie Sultan is president of Concur Travel at 麻豆原创 Concur.

The 麻豆原创 Concur Global Business Traveler Survey was conducted by between April 1-20, 2026, among 3,300 business travelers in 21 markets: ANZ (Australia, New Zealand), Benelux (Belgium, Netherlands, Luxembourg), Brazil, Canada, Denmark, Finland, France, Germany, India, Italy, Japan, Mexico, Norway, Portugal, Saudi Arabia, Singapore, Spain, Sweden, Switzerland, UK, and U.S.
The 麻豆原创 Concur Global Travel Manager Survey was conducted by between April 1-20, 2026, among 800 travel managers, defined as those who direct or administer travel programs for businesses, across eight markets: ANZ (Australia and New Zealand), Canada, Germany, India, Italy, Japan, UK, and U.S.
The 麻豆原创 Concur Global CFO Survey was conducted by between April 1-20, 2026, among 700 CFOs across seven markets: ANZ (Australia and New Zealand), Canada, Germany, Italy, Japan, UK, and U.S.

The data suggests that while organizations are becoming more thoughtful about trip economics, most are still prioritizing in-person meetings and employee travel.

Travel costs rose across most categories 

According to 麻豆原创 Concur global data from January 1鈥揗ay聽31, 2026, overall business travel costs increased across聽nearly every聽major category year over year. Airfare rose聽more than聽8%,聽hotel rates increased nearly 6%, and car rental costs climbed聽roughly聽5%.

At the same time, the average cost of fuel-related expenses surged. The average transaction in Concur Expense in the gas category increased approximately 22% globally, rising from $50 in February to $61 in April with similar spikes seen in countries around the world. 

Transportation patterns shifted in select segments

Those increases are already influencing some travel decisions. While overall air and hotel booking volumes remained relatively flat year over year, there have been some shifts in how employees travel once they arrive at their destinations, as car rental bookings declined roughly 4% globally. Additionally, rail bookings increased approximately 4%, suggesting some organizations may be looking for more cost-effective or efficient transportation alternatives as fuel prices rise.

Premium travel demand remained strong

Even with costs rising, companies did not significantly reduce premium travel spending. Premium cabin bookings鈥攊ncluding business and first class鈥攊ncreased about 9% year over year. By comparison, economy bookings remained flat, while premium economy bookings declined approximately 15%.

Companies are balancing traveler experience alongside budget pressures. For longer flights and international trips in particular, some organizations still view premium travel as a worthwhile investment.

What to watch in the second half of 2026

There are early signs that rising costs and operational disruptions could begin affecting demand, with significantly higher average airfares and reduced airline capacity in some parts of the world. The coming months will help determine whether these disruptions create a short-term adjustment or shape a broader shift in business travel behavior. For now, the data suggests companies are willing to absorb higher travel costs rather than scale back travel plans.

Research shows that business travel remains closely tied to professional opportunity and relationship building. In a  conducted in the U.S. on behalf of 麻豆原创 Concur, 90% of frequent business travelers say traveling for work has positively impacted their careers, underscoring the employee experience and retention benefits of continuing to prioritize business travel even as costs rise.

As organizations navigate higher travel costs in 2026, the data suggests many still view business travel as a worthwhile investment in relationships, employee development, and long-term growth.

Charlie Sultan is president of Concur Travel at 麻豆原创.

Methodology: 麻豆原创 Concur analyzed expense transactions tagged as 鈥済as鈥� in Concur Expense between January 1, 2026, through May 31, 2026, and equivalent time periods from 2025. 麻豆原创 Concur analyzed air, rail, hotel, and car bookings in Concur Travel for trips booked and undertaken between January 1, 2026 and May 31, 2026 and the equivalent time period in 2025.

The journey began in early 2019 with the launch of Agile Finance, an end-to-end transformation initiative designed to support the company’s aggressive growth trajectory, which included a goal to double in size. The initiative was built around three strategic pillars: enhancing the efficiency and effectiveness of the finance organization, promoting career fulfillment, and establishing a robust digital operating model. The impact was significant, with the finance function achieving approximately 35% productivity gains in its labor force.

The third pillar鈥攖he move to a digital operating model鈥攊s where the partnership with 麻豆原创 Taulia began.

鈥淭he 麻豆原创 Taulia Dynamic Discounting solution was introduced not merely as a cost-cutting measure, but as a strategic tool to transform and digitize the interaction with Applied’s extensive, global supplier base,鈥� Junaid Ahmed, corporate VP, Finance at Applied Materials, says. 鈥淲e understood that to reap the benefits of digitization, we had to ensure the suppliers were on board. It needed to be a win-win outcome.鈥�

Unprecedented flexibility for suppliers

The program empowers suppliers鈥攖housands of them worldwide鈥攖o self-select which approved invoices they wish to discount for early payment. This is not a continuous, all-or-nothing commitment but rather a decision made on an invoice-by-invoice basis. This flexibility allows suppliers to manage their working capital needs with greater precision, taking advantage of early payment during their own critical periods, such as quarter-end or year-end, to help meet their own financial targets.

The system also drastically improves transactional efficiency. Suppliers no longer have to call Applied to track invoice status, approval, or payment date. All this information is available 24/7 in the 麻豆原创 Taulia solution, reducing resource allocation on both sides and ensuring both reap the benefits of moving to an integrated, digital system.

Strategic benefits for Applied Materials

For Applied, the program is a testament to its focus on balancing efficiency with strong supplier relationships. The philosophy is a 鈥渨in-win鈥� built on a crucial spread: Applied Materials, as a Fortune 500 company with strong cash flow, has a significantly lower cost of capital than many of its suppliers. By funding the discounts, Applied captures a return鈥攖he discount income鈥攚hile offering its suppliers funding at a rate close to their cost of capital, but with greater convenience.

This relationship-focused approach is critical. Applied鈥檚 supplier account managers actively support the program because they recognize its mutual benefit, not viewing it as a finance mandate to push costs onto the supply base.

Furthermore, the “dynamic” nature of the discount rates is a powerful risk mitigation tool. Unlike fixed contractual discounts, the rates can be adjusted in response to global economic changes, such as shifts in interest rates. When interest rates rose after the pandemic, Applied was able to adjust the discount rates accordingly with minimal pushback, as the core proposition remains the valuable spread between the parties’ cost of capital.

The 麻豆原创 Taulia Dynamic Discounting solution has been rolled out globally, giving all suppliers the opportunity to use it. This has been critical over the last 12 months as many businesses around the globe have been subject to new and often unexpected tariff costs impacting their margin and their liquidity.

鈥淭he flexibility of the solution means suppliers can access funds when they need them, which helps them navigate some of the economic uncertainty that many businesses are facing,鈥� Dirk Holoubek, managing director, Finance Shared Services, explains. 鈥�2025 saw a 23% increase in usage of the discounts, reflecting the pressures that suppliers are feeling right now on their cash flow.鈥澛�

The solution’s capability to drive sophisticated analytics is also a major strategic asset. It helps provide insights into the different costs of capital between Applied and its supplier base. This data allows for targeted outreach and communication, ensuring that the offer of capital support is proactively extended to the suppliers that need it most.

The strategic value of the solution is further cemented by its ownership. The acquisition of Taulia by 麻豆原创 brings several advantages.

鈥淭rust is really important to both us and our suppliers,鈥� Ahmed says. 鈥淔or our suppliers to adopt a new solution, they need to know its technology they can rely on in the long term. Being part of 麻豆原创 creates that assurance in the long-term future of the program.鈥�

Looking forward, Applied Materials is already focused on the next stage of the transformation project: Agile Finance 3.0, which is focused on enabling the organization to become AI-first. The company is deploying a global, organization-wide AI assistant to drive personal productivity, but the strategic application of AI in the supplier management space is even more profound.

AI is expected to transform decision-making enablement by analyzing critical information and communicating effective options. In the future, AI will be able to proactively assess the specific needs and attributes of the supplier base, enabling Applied to address issues more quickly and resolve them earlier. The benefits are already tangible in e-invoicing: AI has made the solution more flexible and “human-like,” capable of reading minor changes in invoice format that would have previously caused electronic errors. This reduced rigidity and increased flexibility are directly contributing to the overall efficiency of the digital operating model.

By leveraging the 麻豆原创 Taulia Dynamic Discounting solution, Applied Materials has not only digitized a process but also strategically transformed its financial operations, creating a system that is agile, resilient, and focused on maintaining mutually beneficial relationships with its global supplier ecosystem.

Cedric Bru is CEO of 麻豆原创 Taulia.

What made the event distinctive was its candor. Customers did not come to present polished success stories. They came to compare realities. And those realities were refreshingly honest.

Why direct procurement is hitting a breaking point

The pressure on direct procurement is not coming from one direction. Geopolitical instability and accelerating technological change are forcing sourcing decisions earlier in the product lifecycle, at precisely the moment when many organizations are least equipped to act. Meanwhile, institutional knowledge is leaving faster than systems are modernizing. The experienced individuals who once held fragile processes together are retiring or moving on, and the systems meant to replace that knowledge are not yet ready.

The result is an operating model that prevents procurement leaders from influencing value at the moments that matter most. Several customers described a tension they are actively dealing with. Sourcing is being pulled upstream into design and development, while the tools and processes that support it are still anchored downstream.

What customers shared about their reality

While all participants operate with a strong 麻豆原创 footprint, spanning and , many acknowledged that direct materials sourcing remains fragmented and disconnected from the digital core. The picture they described was familiar but worth stating plainly: engineers, buyers, and suppliers still collaborating through e-mail, local tools, and disconnected applications; there’s an overreliance on a small number of experienced individuals to make things work; and multiple ERP landscapes run in parallel, with direct sourcing living largely outside all of them.

One observation stood out clearly. The real friction is not the sourcing events themselves. It is the handoffs, the gaps between systems and teams where decisions get made too late, data is reconciled manually, and no single digital thread connects product intent to sourcing execution.

In other words, the process functions, but it functions in silos.

Participants also noted that traditional indirect source-to-pay approaches simply do not support direct materials adequately. They lack native support for procurement embedded in new product development, sourcing scenarios that evolve with engineering change, demand aggregation across programs, and contracts treated as executable objects rather than static documents. That last point came up repeatedly, particularly the need to treat contracts as executable objects. This is also where the add-on in 麻豆原创 S/4HANA is starting to resonate more strongly in ongoing customer discussions.

Where customers are focusing next

What emerged from the discussions wasn鈥檛 a long list of priorities, but a firm shift in where companies are focusing their efforts.

Moving sourcing upstream into product development鈥攔ather than reacting after design decisions are already locked鈥攚as a consistent theme. So was reducing dependency on hero buyers: individuals whose personal expertise and relationships are currently holding critical processes together.

Commodity volatility and renegotiations also came up as structural challenges, not one-time events. Organizations want to handle these systematically rather than heroically. And several participants raised the reality of managing multi-year 麻豆原创 S/4HANA journeys without stalling progress in the meantime鈥攁 genuine tension that demands honest road map planning.

While the direction is widely understood, most organizations do not yet have the setup to execute against it at scale.

These priorities help explain why customers are increasingly adopting the 麻豆原创 Ariba direct materials sourcing add-on alongside , , and 鈥攃apabilities that together can support the connected execution model direct procurement actually requires.

How AI fits into direct procurement

AI generated significant interest, but expectations were measured and, I would say, appropriately so.

The consistent message was this: AI only matters once the fundamentals are addressed. Agent-based capabilities depend on clean processes and consistent data. Without a unified digital thread across product design, sourcing, contracting, and execution, AI does not generate insight鈥攊t amplifies noise.

Leaders also expressed clear skepticism toward black-box automation. They want AI that is explainable and embedded directly into sourcing, negotiation, and execution workflows, not layered on top of broken processes and presented as a fix.

This thinking aligns closely with 麻豆原创鈥檚 vision for the Autonomous Enterprise, introduced at 麻豆原创 Sapphire just weeks after our Walldorf discussions. The vision anchors AI agents directly in transactional business processes, data, and governance鈥攅xactly what customers said they needed before they could trust AI in direct procurement environments. Hearing them articulate that requirement so clearly, before the announcement, felt like meaningful validation.

Where this is all heading

The Walldorf roundtable confirmed a clear trajectory. Direct procurement organizations are moving away from heroics and spreadsheets and toward system-led execution. They are aligning sourcing transformation with their 麻豆原创 S/4HANA road maps and preparing their organizations鈥攏ot just their systems鈥攆or a future where AI supports decision-making across the full procurement lifecycle.

Direct procurement, seen through this lens, is not a standalone transformation. It is a foundational building block. Connecting product, sourcing, contracts, and execution through a single digital thread is what enables AI to operate accurately, compliantly, and at scale. That connection has to exist before any of the more ambitious automation goals become realistic.

For 麻豆原创, conversations like the one in Walldorf directly inform our product direction and investment priorities. There is no substitute for sitting in a room with people navigating these challenges every day, without a script.

What was clear in Walldorf is that the direction is no longer in question for participating organizations. The challenge now lies in execution and in how quickly organizations can move from fragmented, person-dependent processes to cohesive models that reflect how direct procurement operates today.

Karolina Bombardelli is global go-to-market lead for Direct Procurement at 麻豆原创.

They are expected to manage risk, ensure compliance, and deliver strategic value, all while navigating talent shortages and increasing operational complexity. And most are doing it without the end-to-end visibility they need.

Workflows are disconnected, decision-making is reactive, and policies are inconsistently enforced. I have heard this from customers across every industry and, frankly, it is a problem that traditional approaches to procurement technology haven鈥檛 fully solved.

That鈥檚 what makes this moment different. At 麻豆原创 Sapphire, we introduced the Autonomous Enterprise, a fundamental shift in how businesses operate, with AI assistants and agents powering end-to-end execution at scale, with governance built in. Critically, this isn鈥檛 just about adding AI features to existing tools. It is about moving from AI in applications to AI on applications鈥攊ntelligence that works across your entire landscape, not just inside individual products.

Autonomous Spend Management: From concept to reality

Autonomous Spend Management is a core pillar of the Autonomous Enterprise vision, designed to address the fragmentation that holds procurement and finance teams back. By applying agentic AI across procurement, travel, expenses, and external workforce processes, we鈥檙e creating continuity where disconnection exists today鈥攊ntelligent systems that orchestrate activities, connect context, and surface the right insights at the right moment.

What this means for the people doing the work is equally significant. When AI handles routine execution, decision-makers get time and clarity back. They can intervene earlier, with better information, and focus on more strategic work that actually moves the needle.

To bring this to life, we are introducing a new set of Joule Assistants, AI-powered teammates designed to support procurement and spend management across the full life cycle:

• Category Management Assistant: Analyzes spend patterns, delivers market intelligence, and helps build sharper category strategies
• Sourcing Assistant: Manages the entire sourcing life cycle, from drafting RFPs and bids to recommending negotiation strategies
• Supplier Management Assistant: Provides comprehensive oversight of the supply base, from intelligent classification to continuous multi-dimensional risk monitoring
• Contract Assistant: Streamlines contract authoring, flags renewal opportunities, and connects supplier selection through to contract execution
• Requisition Assistant: Guides users to the right buying channel, auto-fills fields, and uses advanced trade-off analyses to help maximize volume discounts
• Buying Assistant:Helps professional buyers identify spend leakage, surface optimal suppliers, and automate order consolidation
• Receiving Assistant: Auto-creates goods receipts and service entry sheets and guides users through quality tracking so nothing falls through the cracks
• Invoicing Assistant: Handles invoice capture, duplicate detection, and payment proposals so finance teams can close faster with fewer errors
• Services Procurement Assistant: Manages the full SOW life cycle from creation through compliance tracking
• Travel Assistant: Simplifies trip planning with pre-spend estimates, streamlined approvals, and built-in compliance guidance
• Expense Management Assistant: Automates expense reporting, capturing details, flagging errors, and keeping everything compliant

The Autonomous Spend Management capabilities run across our cloud ERP application portfolio, including 麻豆原创 Cloud ERP Private, for end-to-end coverage across business processes and systems.

Why connected processes are critical

Connection is just as powerful as intelligence, and that conviction runs through everything we  announced this week. AI can only do so much if the underlying processes are still fragmented.

In next-gen 麻豆原创 Ariba Buying, new Joule Agents support purchasing and policy management through a more intuitive, persona-driven experience, guiding users toward compliant, contract-linked options while improving catalog management and document traceability. Deeper integration with 麻豆原创 S/4HANA Private Cloud Edition and 麻豆原创 ERP Central Component means these capabilities work with existing ERP investments, not around them.

麻豆原创 Ariba Contracts now brings contract creation, approvals, and compliance tracking into a single unified workspace. AI-assisted drafting lets teams create contracts using natural language, while centralized visibility into terms, pricing, and key dates keeps data consistent and connected to downstream procurement processes.

We also introduced a new Joule Agent in 麻豆原创 Ariba Intake Management to automate how procurement requests are captured and routed across 麻豆原创 and non-麻豆原创 systems. And expanded supplier evaluation capabilities in 麻豆原创 Ariba Supplier Lifecycle and Performance let teams segment performance data by geography, business unit, or category 鈥� with insights feeding directly into to inform sourcing and procurement decisions.

Expanding visibility into services spend and supporting adoption

Nowhere is the need for connected processes more apparent than in asset-intensive industries. In oil and gas, mining, and utilities, external workers can make up 40% of the workforce, yet most organizations are still managing them through manual processes and disconnected systems. The risks are real: expired certifications, overpayments, and poor visibility into work billed versus work actually done.

New 麻豆原创 Fieldglass capabilities address these challenges by bringing together the full contractor life cycle, from the moment a worker arrives on site through to final payment. Organizations can now automate time tracking, verify worker credentials and safety requirements before granting site access, maintain tighter controls over equipment, and dramatically reduce the manual effort involved in invoicing.

We鈥檙e also using AI to accelerate SOW creation by automatically recommending worker roles based on the SOW description and historical buyer data, which reduces manual setup and improves consistency from the start. And to support adoption, WalkMe Premium is now integrated with 麻豆原创 Fieldglass and 麻豆原创 Ariba, providing in-app guidance for tasks such as creating statements of work, approving timesheets, and hiring candidates.

The future of spend management

Autonomous Spend Management marks a fundamental shift from managing processes to delivering business outcomes. From chasing cost savings to actively shaping resilience, margin, and growth. From reacting to events to anticipating them.

The real strategic implication is this: Spend does not happen in isolation. Every contract and invoice has a downstream effect on financial performance. When those decisions are made in context鈥攚ith AI connecting procurement, supply chain, and finance鈥攖he enterprise doesn鈥檛 just run more efficiently, it runs as one system.

That鈥檚 what we are building, and what we announced this week marks a significant step forward.

For more details on this week鈥檚 announcements, see the . For more details on the latest updates in travel and expense, please refer to the

Etosha Thurman is co-business lead and chief marketing officer for 麻豆原创 Finance & Spend Management.

In his kickoff keynote at 麻豆原创 Sapphire Orlando, Florida, Klein and other 麻豆原创 Board members detailed how 麻豆原创 plans to bring agentic AI to the world’s most critical business workflows so that humans and AI can meet the accelerating demands of global business profitably, strategically, and safely.

鈥淭oday I鈥檓 super proud to launch our new 麻豆原创 Business AI Platform, which forms the basis for our vision of the future of business: the Autonomous Enterprise, where agents run the business and you can focus on what truly matters,鈥� Klein said.

Enterprise AI is at an inflection point, Klein told his 30,000-strong in-person and virtual keynote audience, and 麻豆原创 is in a unique position to deliver what customers need to turn their businesses into autonomous enterprises.

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The business AI imperative

Across industries, organizations are investing heavily in artificial intelligence, yet many still struggle to translate that investment into meaningful business value. At 麻豆原创 Sapphire, the message was clear: This isn鈥檛 a technology problem; it鈥檚 a context and execution problem.

While 80% accuracy may be sufficient for consumer AI applications, Klein said, 鈥淓ighty percent is just not good enough when you run the world鈥檚 most business-critical businesses. They [LLMs] should not guess; they should deliver accurate, compliant, and secure outcomes.鈥� 

Klein acknowledged that while adoption of AI has become near-universal, tangible business value remains elusive. Citing a recent Stanford AI survey, he noted that almost every company is now using AI, but seeing only limited return.

The reason, he argued, lies in a structural gap. Above the waterline of enterprise AI, LLMs continue to improve at tasks trained on publicly available data, while below it lies what enterprises truly need: AI that understands mission-critical business data, end-to-end processes, and operates within security, compliance, and governance frameworks.

ERP as the foundation for business AI

麻豆原创鈥檚 answer to this challenge begins with what Klein described as 鈥渢he brain of every company: its ERP system.鈥� For over 50 years, 麻豆原创 has had solutions with incredibly deep process and data domain know-how alongside the governance requirements, compliance controls, and company-specific configurations that define how businesses actually run.

Now, as part of the company鈥檚 new vision, 麻豆原创 plans to infuse this institutional knowledge into AI agents, enabling them to navigate thousands of business processes, select from more than 7 million data fields, and verify identity and access authorizations before returning any output.

鈥淲e鈥檙e bringing together LLMs with 50 years of business know-how stored in our ERP. But to do this, we had to do nothing less than completely reinvent our company,鈥� he told the audience. 鈥淭oday we are very excited to show you the new 麻豆原创 and our vision for the Autonomous Enterprise.鈥�

麻豆原创 Business AI Platform

To bring this vision to life, 麻豆原创 executives on stage announced a series of important innovations, beginning with the launch of the new 麻豆原创 Business AI Platform, a unified architecture bringing together 麻豆原创 Business Technology Platform, 麻豆原创 Business Data Cloud, and AI Foundation under a single roof.

鈥淭he heart of this new platform is the rich context layer,鈥� said Klein. 鈥淗ere, we infuse the deep ERP business domain know-how into the AI agents. Through our knowledge graphs, our AI agents have now a compass, a map, to find the right process and data in your ERP universe. And to provide the agents even more context, we are also introducing our new 麻豆原创 Domain Models. They have been trained on 麻豆原创’s code to even better understand the business logic of your company.鈥�

But, he said, 麻豆原创 is going further: 鈥淏ecause you run your business not only with 麻豆原创 solutions, our AI agents have to also understand non-麻豆原创 data. That’s why we included our 麻豆原创 Business Data Cloud in the context layer to build a single semantical data layer across 麻豆原创 and non-麻豆原创. No more silos, no spaghetti data sprawl鈥攂ecause no AI agent can compensate for a broken data model.鈥�

Echoing Klein, 麻豆原创 CTO Philipp Herzig, who presented the platform in detail, said it has been designed to close the agent adoption gap in the enterprise by delivering outcome, speed, enterprise-readiness, and context. 鈥淚t’s the place where you build, contextualize, reason, and govern AI,鈥� he said.

Herzig explained that the platform is structured around three layers: the context layer which Klein referenced, the build layer, and the governance layer. 鈥淎gents are only as powerful as the context they operate on,鈥� he said. 鈥淟acking context is the number one reason why enterprise AI projects fail to deliver value.鈥�

Within the build layer of the new platform, the new Joule Studio is designed to understand a company鈥檚 business challenges and enables the building of new AI agents quickly and easily.

The third tier is the governance layer, anchored by the new 麻豆原创 AI Agent Hub built on 麻豆原创 LeanIX. This provides a single command center to discover, manage, and govern all AI agents鈥斅槎乖� and non-麻豆原创. It will be generally available in Q3 and included in 麻豆原创 Business AI Platform at no additional charge.

Underscoring the changing AI marketplace, Herzig was joined on stage by KPMG Global Head of Advisory Rob Fisher, who told the audience: 鈥淲hat I鈥檓 hearing from clients is a clear shift; they鈥檙e moving from AI pilots to embedding integrated AI and agents into how work gets done. Where we see leaders really separating from the pack is in the execution and the organizational adaptability.鈥�

麻豆原创 Autonomous Suite

Building on the platform, 麻豆原创 Executive Board Member Muhammad Alam, 麻豆原创 Product & Engineering, announced the transformation of 麻豆原创鈥檚 SaaS application portfolio into the 麻豆原创 Autonomous Suite, described as the most significant evolution of 麻豆原创鈥檚 applications business in the company鈥檚 history.

The suite spans five domains: Autonomous Finance, Autonomous Spend, Autonomous Supply Chain Management, Autonomous HCM, and Autonomous CX, with more than 200 agents and over 50 assistants available in the coming months. Each assistant is mapped to core business roles and carries defined KPIs tracked through 麻豆原创 AI Agent Hub.

鈥溌槎乖� Autonomous Suite brings together the depth of our process expertise, semantically rich data, and built-in governance and compliance,鈥� said Alam. 鈥淭hese agents are designed with outcomes as a core objective. Each assistant has a defined set of ROI KPIs that you can expect it to deliver.鈥� 

鈥淯nderpinning the autonomous suite are out-of-the-box agents鈥攈undreds of agents cutting across all core business processes,鈥� he shared. 鈥淭hese agents come together into what we call assistants, or Joule Assistants. We’ve mapped these assistants to roles across the core processes of an organization, because we know that the first step 
in realizing value from AI is to empower your people to do more, do it better, or do things that just weren’t possible to be done before.鈥�

Turning to Joule itself, Muhammad said 麻豆原创 is fundamentally reimagining how users will interact with 麻豆原创 applications in the future.

鈥淲e call this Joule spaces and along with the familiar Joule conversations experience and Joule Studio 2.0, it is now part of what we call Joule Work,鈥� he explained.

鈥淛oule Work represents a massive step forward in super-charging the capabilities of Joule as we know it today,鈥� Alam said. 鈥淲ith Joule Work, we’re bringing a claw-based agentic harness to Joule along with computer and file access, better support for open standards such as MCP and A2A, access to a more complete knowledge base, and, of course, amazing visualizations on the fly.鈥�

Industry AI: H&M and Sector-Specific Transformation

During the keynote, 麻豆原创 Chief Operating Officer Sebastian Steinhaeuser introduced the Industry AI initiative, delivering AI-powered solutions built on decades of sector-specific expertise across 26 industries. In life sciences, he highlighted how 麻豆原创 customer Takeda is achieving up to 10% productivity gains, up to 25% reduction in revenue loss from stock-outs, and up to five percent reduction in safety stock through Autonomous Regulated Manufacturing.

He was also joined on stage by H&M Group CDIO Ellen Svanstr枚m, who discussed how the fashion retailer is embedding AI across its value chain. Built on RISE with 麻豆原创, 麻豆原创 Business Data Cloud, 麻豆原创 Commerce Cloud, and 麻豆原创 SuccessFactors solutions, H&M has developed a Store Intelligence Agent that processes real-time signals to generate actionable recommendations for store managers. Svanstrom also demonstrated the AI-powered InStore Concierge, a customer-facing agent that bridges digital and physical retail through personalized outfit recommendations and real-time availability.

RISE with 麻豆原创 and 麻豆原创 GROW: Path to the Autonomous Enterprise

Returning to the keynote stage, Klein emphasized that technology adoption alone does not create business value. Simply plugging AI agents into your system landscape will drive zero value, he said. 鈥淢oving to the Autonomous Enterprise requires serious change management. Adoption of AI goes hand-in-hand with business process change and end user enablement.鈥�

To support customers on this journey, 麻豆原创 announced a comprehensive reset of its RISE with 麻豆原创 and 麻豆原创 GROW offerings. RISE with 麻豆原创 customers will receive contractual commitment to activate three Joule Assistants within the first year, with the Max Success Plan extending adoption across the full enterprise.  

麻豆原创 GROW customers will receive more than 20 AI assistants from day one, with an AI-enabled toolchain designed to support go-live in weeks. New partnerships with Palantir and Accenture will support the most complex migration scenarios.

Closing: The Autonomous Enterprise

Klein closed the keynote by asking Joule to summarize the key takeaways and noting that 麻豆原创 is evolving from being a software company to becoming a business AI company.

鈥淲e showed how to turn the promise of business AI into reality with 麻豆原创 Business AI Platform, which provides the data processes and governance AI need to deliver accurate and secure outcomes at scale; we introduced the 麻豆原创 Autonomous Suite, where applications reason, decide, and act for you; and we showed how to manage change management with RISE with 麻豆原创. Together with customers and partners, we showed how 麻豆原创 is helping companies realize the vision of the Autonomous Enterprise.鈥�

鈥淲e鈥檝e been reinventing how businesses run for over 50 years, and now by infusing 麻豆原创鈥檚 ERP brain into the new 麻豆原创 Business AI Platform, we鈥檙e solving one of the biggest challenges businesses are facing today: how to turn AI into business value,鈥� he said. 鈥淚t鈥檚 the end of long negotiations, supply chain disruptions, financial blind spots, and the beginning of better: Welcome to the Autonomous Enterprise.鈥�

The company introduces a unified 麻豆原创 Business AI Platform, deepening partnerships with Anthropic, Amazon Web Services, Google Cloud, Microsoft, NVIDIA and Palantir

ORLANDO聽鈥� At 麻豆原创 Sapphire in 2026, (NYSE: 麻豆原创) introduced the to help enhance the world’s most critical business workflows, so that humans and AI work together to meet the accelerating demands of global business profitably, strategically and safely.

鈥淔or the mission-critical processes of our customers, ‘almost right’ just isn鈥檛 good enough,鈥� said Christian Klein, CEO of 麻豆原创 SE. 鈥淏y uniting 麻豆原创 Business AI Platform with 麻豆原创 Autonomous Suite, we anchor AI agents in the business processes, data and governance so they can deliver accurate, compliant and secure outcomes, unlocking new sources of revenue and meaningful cost savings.鈥�

The Autonomous Enterprise includes a unified AI platform for building, contextualizing and governing agents, an autonomous suite that executes core business operations and a new user experience that redefines how people work with enterprise software.

Introducing 麻豆原创 Business AI Platform

麻豆原创 Business AI Platform is a new foundation for building and deploying enterprise AI grounded in real business context. 麻豆原创 Business AI Platform now unifies 麻豆原创 Business Technology Platform, 麻豆原创 Business Data Cloud and 麻豆原创 Business AI into a single, governed environment.

At its core is the 麻豆原创 Knowledge Graph solution, which gives AI agents a structured map of business entities, processes and relationships across a customer’s 麻豆原创 landscape. Joule Studio is 麻豆原创’s AI-first solution for building enterprise agents, applications and agentic workflows. Developers can build using the no-code, pro-code and AI frameworks of their choice on 麻豆原创-managed infrastructure that is secure, scalable and optimized for enterprise AI.

Deploying 麻豆原创 Autonomous Suite Across Every Business Function and Industry

Building on this foundation, 麻豆原创 also introduced 麻豆原创 Autonomous Suite, which enables 麻豆原创’s existing business applications with AI agents capable of running processes from start-to-finish.

The suite will deploy more than 50 domain-specific Joule Assistants across finance, supply chain, procurement, human capital management and customer experience. These assistants will automate end-to-end processes by orchestrating a subset of over 200 specialized agents to execute precise tasks. For example, the new Autonomous Close Assistant can compress the financial close process from weeks to days by automating journal entries, reconciliation and error resolution across the entire process.

麻豆原创 also launched Industry AI, expanding its deep industry portfolio through seven autonomous solutions that will enable start-to-finish industry processes and embed sector-specific process logic, data models and regulatory requirements. At 麻豆原创 Sapphire, 麻豆原创 showcased its work with European energy giant RWE to leverage Industry AI, helping reduce unplanned downtime across its offshore wind turbines. With 麻豆原创’s Autonomous Asset Management scenario, AI agents are designed to analyze data from thousands of past incidents, identify the likely root cause and generate pre-filled work orders with the right tools and proven fixes from other sites.

Designing the Autonomous User Experience

The company also revealed Joule Work, redefining how users engage with 麻豆原创 software. Instead of navigating individual applications and entering data across several screens, users will now interact primarily with Joule. By describing a desired business outcome, Joule will orchestrate the right combination of workflows, data and agents to get it done.

Joule Work goes beyond conversation, proactively surfacing relevant insights and automating routine tasks behind the scenes so work moves forward even when humans aren’t actively steering it. It will be available on desktop, mobile and voice across 麻豆原创 and non-麻豆原创 systems.

Accelerating the Customer Journey Toward Autonomy with 鈧�100 Million Infusion

麻豆原创 evolved its customer and partner programs to help accelerate the organization’s journey to the Autonomous Enterprise. To catalyze adoption, the company has launched a 鈧�100 million fund for 麻豆原创 partners to help customers deploy 麻豆原创-built AI assistants and agents. The fund is also available to partners that extend or build new partner agents on the new 麻豆原创 Business AI Platform using Joule Studio.

麻豆原创 has enhanced its RISE with 麻豆原创 and 麻豆原创 GROW offerings to accelerate AI adoption. Both include access to the Joule Assistants portfolio; RISE with 麻豆原创 customers will have three assistants activated within their first year, while 麻豆原创 GROW customers receive full portfolio access at onboarding. 麻豆原创 S/4HANA on-premises and 麻豆原创 ERP Central Component (麻豆原创 ECC) customers are not excluded: those that commit to transitioning the majority of their current landscape to 麻豆原创 Cloud ERP gain access to select AI scenarios, bridging the gap between their current landscape and their cloud destination

麻豆原创 also introduced new agent-led transformation tooling that can reduce ERP migration efforts by more than 35 percent, driving faster and more predictable projects by automating system analysis, code remediation, configuration and testing at scale.

Lastly, 麻豆原创 announced a full slate of strategic partnerships across each category:

• Platform and suite partnerships include Anthropic, with Claude among the foundation models 麻豆原创鈥檚 AI platform will leverage to power Joule agents across HR, procurement and supply chain; Amazon Web Services, bringing zero-copy data integration between 麻豆原创 Business Data Cloud and Amazon Athena; Google Cloud and Microsoft, enabling bidirectional agent-to-agent interoperability between Joule and external agent frameworks; Mistral AI and Cohere, delivering sovereign model options on 麻豆原创’s cloud infrastructure; , providing visual AI workflow orchestration inside Joule Studio; NVIDIA, whose OpenShell provides the trusted secure runtime for Joule Studio; and , bringing AI agents into 麻豆原创 Service Cloud to handle customer interactions with full access to business data and service processes.
• Implementation partnerships include Palantir and Accenture, partnering on complex data migration scenarios, and for AI-powered cloud ERP migrations.

.

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About 麻豆原创

As鈥痑 global leader in enterprise applications and business AI, 麻豆原创 (NYSE: 麻豆原创)鈥痵tands at the鈥痭exus鈥痮f business and technology. For over 50 years, organizations have trusted 麻豆原创鈥痶o bring out their best by uniting business-critical鈥痮perations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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Global Customer Center: +49 180 534-34-24
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麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
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, our new user experience, is gaining momentum and driving significant impact for our customers. Customers are already efficiency, enhancing processes, improving , and .

Joule is now live across 35 solutions and will continue to meet our customers where they are: across the applications they use, with a firm understanding of their business context and data. That鈥檚 why in Q1 we are embedding Joule into more applications鈥攆rom 麻豆原创 Datasphere, where it can now execute tasks or explain specific functionalities, to 麻豆原创 Intelligent Clinical Supply Management, where users can use natural language to retrieve critical data and navigate to relevant applications.

Joule Agents, such as the Tender Analysis Agent, are boosting customer revenue growth by extracting critical requirements and flagging risks in complex documents. While project managers in 麻豆原创 S/4HANA Cloud Public Edition are saving time setting up projects with the new Project Setup Agent. Plus, there are many more agents to discover below.

Agents are becoming a key new user鈥攁nd enabler鈥攐f enterprise software, joining humans as the only other non-deterministic operators while simultaneously expanding enterprise software鈥檚 scope and usefulness. Our agents will continue to deliver trustworthy, repeatable, and auditable results every time.

We now have over 30 specialized agents and more than 2,500 Joule Skills. The agent-to-agent protocol means our agents work across 麻豆原创 and non-麻豆原创 systems. As the number of agents grows across both, 麻豆原创 AI Agent Hub already today provides customers with the essential infrastructure and guardrails to manage, govern, and discover agents in this new ecosystem.

Some highlights from Q1 2026:

• 麻豆原创 Joule for Consultants is a conversational AI solution that provides expert guidance on cloud transformations, drawing on 麻豆原创鈥檚 knowledge base. To improve trust and traceability, citations are now displayed in a dedicated side panel and can be grouped for clarity. Administrators can enable web search, allowing Joule to draw from public content while maintaining clear source attribution. For tailored answers to problems where the system may not have customer-specific documentation, consultants can now upload up to 10 PDF or text files directly into the chat. This is further enhanced by the inclusion of content from the 麻豆原创 Enterprise Architecture Reference Library, which provides more complete and accurate answers to complex queries. Get started here.
• 麻豆原创 Business AI for supply chain minimizes disruptions and simplifies planning. The Project Setup Agent allows project managers to rapidly establish new projects by drawing on data from past initiatives. 麻豆原创 Integrated Business Planning users can now generate complex formulas in Microsoft Excel with natural language. 麻豆原创 Digital Manufacturing can distill complex manufacturing issues into clear descriptions. Joule is also helping 麻豆原创 Integrated Product Development users create problem reports and requirement models with simple, natural-language commands. Explore more below.
• 麻豆原创 Business AI for finance offers greater efficiency and insight across critical processes. Joule now translates complex e-invoicing errors into plain language. The Dispute Resolution Agent automates root-cause analysis for invoice disputes, while payment advice processing significantly reduces document processing time. Unstructured data, such as PDFs, can now be automatically transformed into sales orders, and accountants can access natural language explanations for complex fixed asset calculations. Users can personalize their home page and easily understand system errors using natural language across 麻豆原创 S/4HANA Cloud Public Edition. Learn more below.
• 麻豆原创 Business AI for procurement and customer experience enhances the entire commercial journey with new capabilities. In procurement, automated statement of work (SOW) creation in 麻豆原创 Fieldglass reduces the time to define deliverables. The Catalog Optimization Agent means e-commerce managers can continuously improve product data quality. In retail, managers can get instant, conversational answers from Joule on order management data. There’s so much more to learn below.
• 麻豆原创 Business AI for IT and developers puts the latest tools and greater control directly into the hands of developers and data professionals. Joule is now generally available in 麻豆原创 Datasphere, enabling users to navigate the platform, get answers, and execute tasks using simple conversational language. The generative AI hub in AI Foundation continues to expand, offering developers access to the newest models, including OpenAI GPT 5.2, Gemini 3.0 Pro, Anthropic Claude Opus 4.6, and Claude Sonnet 4.6. Developers also gain greater power through enhancements such as advanced prompt optimization, metadata filtering, and declarative orchestration configurations in the prompt registry. Additionally, 麻豆原创 Document AI now offers more granular control with custom confidence thresholds and expanded document support. Dive into everything below.
• 麻豆原创 Business AI for industries delivers specialized intelligence to solve unique business challenges. Sales teams can accelerate their response process with the new Tender Analysis Agent, which automates the review of complex RFQ documents to improve win rates. Joule now works with 麻豆原创 Commodity Management to turn verbal or written negotiations directly into detailed draft deals. In life sciences, clinical supply professionals can use predictive analytics to reduce inventory waste costs, and Joule dramatically cuts information search time. 麻豆原创 Self-Billing Cockpit automates invoice data extraction from any format, significantly reducing manual processing time. Discover more for industries below.
• 麻豆原创 Business AI for business transformation management provides the critical insights needed to navigate and accelerate organizational change. Joule is now in 麻豆原创 Signavio, enabling natural-language searches that cut information discovery time. Business process model and notation simulations in 麻豆原创 Signavio provide clear, actionable summaries directly within process diagrams. Meanwhile, enterprise architects can leverage guidance in 麻豆原创 LeanIX to surface actionable insights directly from their architecture inventory, accelerating transformation execution and reducing the time to uncover them. Read more about transformation management below.

Joule

Joule, enhancements

User experience is improved by streamlining startup times and introducing cross-thread search functionality that lets end users find information across all conversation threads without manually checking individual histories. The document grounding capability has also seen a substantial upgrade, now supporting seamless integration with Google Drive.

To set up, see: , , and .

Furthermore, scalability has been greatly improved, as the system now supports up to 8,000 documents per pipeline, enabling large-scale data repositories to be processed and utilized efficiently.

For more information, see .

麻豆原创 Joule for Consultants, enhancements

Enhanced Citation Visibility
麻豆原创 Joule for Consultants has improved how citations are displayed for all identified sources returned by the product. Citations have been relocated to the right side in a dedicated panel for clearer visibility, and now also include public web search results when applicable (see below).

A new grouping feature has also been added, allowing citations to be grouped. This update provides users with a more transparent view of where information originates, strengthens trust, and improves traceability across all responses.

To see the sources and panel, click the sources button below each message; the panel will open on the right, showing all grouped sources.

Enable Web Search
Administrators can now enable/disable web search via the control panel for all assigned end users in 麻豆原创 Joule for Consultants.

When enabled, 麻豆原创 Joule for Consultants will consider public web content in its reasoning and cite relevant public sources in responses when they contribute to the answer. This enhancement gives organizations greater flexibility and transparency by enabling broader coverage of information while maintaining clear source citations for all sources used.

File Uploads in the Joule Message Input
End-users can now upload up to 10 files directly from the conversational message input box and reference them throughout the entire conversation.

Supported file types include PDF and TXT. Each file should be no more than 10 MB/600K characters; for PDFs, an approximation. A 100-page limit applies; if your file is larger, split it into multiple documents. Image files are currently not processed and will be ignored. We are working diligently to make this feature even more useful to end users. This enhancement enables richer, context-aware interactions by allowing you to incorporate your uploaded documents into its conversational responses throughout the session. Please be aware that the standard data privacy terms apply. See also the help documentation for additional information on the free user quota.

Content: 麻豆原创 Enterprise Architecture Reference Library
麻豆原创 Enterprise Architecture Reference Library data has been ingested and is now available for use in conversations. As more data is added, relevant portions may be included in 麻豆原创 Joule for Consultants鈥� responses, enabling more complete, accurate, and context-rich answers to user queries. Since 麻豆原创 Enterprise Architecture Reference Library content cannot be link-referenced, you won鈥檛 see the additional content listed under sources, even though it will be referenced.

Get started .

SECTION

麻豆原创 Business AI for supply chain

Project Setup Agent
Beta release

Project managers can now rapidly establish new projects by drawing on data from similar past initiatives. The agent bypasses complex interfaces and reduces reliance on the project management office (PMO) to facilitate the swift allocation of key resources needed to launch projects effectively. With a 10% reduction in project creation time, 16% faster resource allocation, and 30% less time spent reworking projects due to incorrect templates, teams can shift focus from operational coordination to improving project profitability and driving efficiency.

Get started .

麻豆原创 S/4HANA Cloud Private Edition, AI-assisted retrieval of equipment information in service management
General availability

Service managers using the AI-assisted retrieval feature in 麻豆原创 S/4HANA Cloud Private Edition gain a complete 360-degree view of customer equipment. The feature provides instant access to warranty information and a full history of service transactions, complemented by an AI summary and actionable recommendations. This allows service managers to more efficiently oversee service schedules, reduce potential downtime, and ensure customer equipment operates at peak performance.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted input recommendations for returns order creation
General availability

Returns clerks can accelerate the creation of customer returns with data field recommendations powered by historical data. This feature analyzes past return documents with similar process variants to automatically suggest the most common input values and return reasons, minimizing manual data entry and reducing errors. Organizations benefit from a one percent reduction in data management costs and a five percent decrease in business and operations analysis expenses, enabling returns teams to process orders more efficiently while maintaining accuracy.

Get started .

麻豆原创 Integrated Business Planning, AI-assisted MRO inventory analysis
General availability

Inventory planners get a new analytical assistant in the MRO inventory analysis feature for 麻豆原创 Integrated Business Planning. The feature accelerates root cause analysis by generating clear, natural-language summaries that explain the key drivers behind recommended safety stock and reorder points. By translating complex calculations into understandable insights, this capability enables planners to reduce time spent analyzing inventory runs by 30%, leading to faster adoption of outputs and ensuring that inventory parameters align with strategic business goals.

Get started .

麻豆原创 Integrated Business Planning, add-in for Microsoft Excel, AI-assisted planning
General availability

Supply chain planners can now simplify their work with a new AI-assisted planning add-in for Microsoft Excel. Instead of manually creating complex formulas or formatting rules, which often require technical expertise, they can simply describe their needs in natural language, and the system automatically generates the correct syntax. This intuitive way of interacting with the system removes technical barriers and improves a planner鈥檚 efficiency by 10%, freeing them to focus on strategic analysis rather than implementation details.

Get started .

麻豆原创 Integrated Business Planning, AI-assisted system security check
General availability

Supply chain planners and security analysts gain a robust way to assess system configurations against established security recommendations. The feature evaluates compliance states and provides clear guidance on required adjustments, helping administrators identify and address potential gaps while aligning configurations with 麻豆原创 best practices. Organizations can expect a 27% increase in compliance with hardening guidelines and a 32% reduction in the effort required to meet security recommendations. This feature strengthens the protection of sensitive data and reduces the risk of security breaches.

Get started .

麻豆原创 Integrated Product Development, AI-assisted problem report creation
General availability

Maintenance engineers can simplify the creation of formal problem reports by leveraging AI capabilities in 麻豆原创 Integrated Product Development. By describing an issue in their own words to Joule, it intelligently extracts key details like the problem name, tags, and priority, and then automatically generates a structured report. This streamlined process dramatically reduces manual data entry and ensures all reports are consistent and compliant with organizational standards, improving overall efficiency.

and get started .

麻豆原创 Integrated Product Development, AI-assisted requirements model creation
General availability

Requirements managers now have a more direct path to creating requirement models within 麻豆原创 Integrated Product Development by using natural language commands with Joule. This feature allows them to initiate new models, specify names, and apply templates in a single step, completely bypassing the need to navigate through complex folder structures. This streamlined approach provides a much faster starting point for new projects and empowers users to begin their work immediately without requiring deep knowledge of the repository layout.

Get started .

麻豆原创 Field Service Management, AI-assisted automated scheduling analytics
General availability

Field service dispatchers and consultants can now access clear, on-demand explanations of auto-scheduling results that demystify complex system logic. The new feature interprets scheduling reports and translates technical scoring details into business-friendly insights, explaining why specific technicians were assigned, why alternatives were passed over, and why certain activities remained unscheduled. This transparency drives a 12.5% increase in dispatcher productivity and a five percent reduction in erroneous resource allocations, strengthening trust in automated decisions while significantly reducing analysis time.

Get started .

麻豆原创 Digital Manufacturing, AI-assisted description enhancement
General availability

Quality managers documenting complex manufacturing issues can now generate clear, objective, and structured descriptions with minimal effort. 麻豆原创 Digital Manufacturing for issue resolution offers description generation that refines rough initial inputs, removes bias and subjective language, and produces balanced, factual problem statements. With support for multilingual translation and enhanced clarity, organizations can achieve up to five percent improvement in quality engineer efficiency during issue handling and up to 10% reduction in errors throughout the problem resolution process.

Get started .

麻豆原创 Business AI for finance

Dispute Resolution Agent (for 麻豆原创 S/4HANA Cloud Public Edition)
Beta release

When invoice disputes arise, accounts receivable specialists need to act quickly without sacrificing accuracy. 麻豆原创 S/4HANA Cloud Public Edition introduces an agent that automates root-cause analysis, scanning invoices, sales orders, delivery records, pricing agreements, and tax rules to identify the source of discrepancies. The agent detects incorrect charges and recommends compliant solutions, such as credit memo creation, enabling finance teams to resolve disputes faster, minimize manual investigation, and cultivate stronger vendor relationships through transparent, efficient processes.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted smart personalization of my home for applications
General availability

麻豆原创 S/4HANA Cloud Public Edition users can easily configure their home page with the most relevant applications through AI-assisted smart personalization. By describing their task in natural language, the system identifies the appropriate app, which can then be added to their home screen with a single click. This intuitive capability reduces the cost of personalizing the home page by 33%, shortens the learning curve for new users, and improves satisfaction by keeping frequently needed tools readily accessible.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted error explanation
General availability

When encountering system errors, 麻豆原创 S/4HANA Cloud Public Edition users can turn to a new feature that generates clear, natural language explanations and resolution recommendations. This capability transforms cryptic error messages into easy-to-understand guidance, helping users of all experience levels quickly rectify issues and continue with their work. By reducing error resolution time by five percent, organizations benefit from increased productivity, improved data quality, and shorter training cycles for new team members.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted sales order creation from unstructured data
General availability

Sales representatives benefit from a streamlined order creation process in 麻豆原创 S/4HANA Cloud Public Edition that handles unstructured data like PDF or image-based purchase orders. After uploading a file, 麻豆原创 Document AI automatically extracts the relevant information and proposes the data for a corresponding sales order request. This automation significantly reduces manual data entry, minimizes errors, and improves overall operational efficiency, allowing teams to process orders faster and enhance customer satisfaction.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted processing of payment advices with 麻豆原创 Document AI
General availability

Accounts receivable clerks can accelerate their workflow using the 麻豆原创 Document AI-powered payment advice processing feature in 麻豆原创 S/4HANA Cloud Public Edition. The system automatically extracts payment amounts, references, and currencies from diverse invoice formats across multiple languages, with a self-learning capability that continuously improves recognition accuracy. Organizations implementing this feature can reduce document processing time by 70%, cut template maintenance time by 83%, and decrease value loss from manual processing delays by 40%.

Get started .

麻豆原创 S/4HANA Cloud Private Edition, AI-assisted fixed asset key figures explanation
General availability

Asset accountants gain clarity on complex fixed asset calculations through a new AI feature in 麻豆原创 S/4HANA Cloud Private Edition. The feature generates natural-language explanations that detail the origins of displayed values and how figures such as depreciation are calculated; for example, illustrating the impact of mid-year acquisitions with specific depreciation keys. This transparency reduces the effort required to analyze asset values, enables faster responses to asset-related questions, and helps mitigate compliance risks.

Get started .

麻豆原创 S/4HANA Cloud Private Edition, AI-assisted settlement rule proposal for asset capitalization
General availability

Overhead and asset accountants can now streamline the complex process of creating settlement rules for investment measures, eliminating the traditionally time-consuming, error-prone manual configuration. The solution automatically determines receivers, calculates percentages, and proposes feasible rules based on contextual data and user-defined instruction profiles. Organizations reduce the effort required to create full settlement rules by 50% while simultaneously improving accuracy in asset capitalization and enhancing overall operational efficiency across their financial processes.

Get started .

麻豆原创 Document and Reporting Compliance for 麻豆原创 S/4HANA Cloud Private Edition, AI-assisted electronic document error handling
General availability

Tax accountants navigating the growing complexity of e-invoicing mandates across multiple countries gain an easy way to decode technical errors without wading through intricate XML or JSON formats. Joule, integrated with 麻豆原创 Document and Reporting Compliance, delivers plain-language explanations of electronic document errors, enabling faster root-cause identification and more efficient resolution. Organizations get an 80% reduction in time spent understanding and resolving errors, dropping from 150 minutes to approximately 30 minutes. This results in faster processing cycles, reduced penalty risks, and improved cash flow.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted error resolution for cost accounting
General availability

Operations managers in retail organizations can now access Joule via 麻豆原创 Order Management Services, enabling them to query order data and receive real-time, role-specific operational guidance across order processing, orchestration, sourcing, availability, returns, and fulfillment flows. Joule surfaces instant insights and recommended actions directly in the workflow, reducing the need to navigate multiple systems. This enables proactive intervention before issues escalate. The feature offers faster transaction access, improved responsiveness and accuracy, and lower operational risk, which support smarter, quicker decisions across the order lifecycle.

Get started .

麻豆原创 Business AI for spend management

Expense Report Validation Agent
General availability

Business travelers can enjoy a smarter, guided approach to expense report completion with an agent that proactively identifies missing items, prompts for necessary details, and clarifies confusing alerts throughout the submission process. By simplifying how users understand and resolve issues, the agent ensures accurate, policy-compliant reports with minimal effort required. This means a 30% reduction in time spent preparing and submitting reports, a 24% increase in first-pass approvals, and a noticeably improved employee experience that removes friction from the expense management process.

Get started .

Expense Pre-Submit Audit Agent
麻豆原创 Early Adopter Care

Expense report submitters can now catch receipt accuracy issues and policy breaches before hitting the submit button, avoiding the frustration of rejected reports and delayed reimbursements. This agent automatically reviews expenses during creation, surfacing compliance problems and offering smart suggestions for quick corrections. The agent uses a non-blocking design that keeps users in control of final decisions. Organizations benefit from a 10% decrease in sent-back expense reports, reduced rework for travelers, managers, and auditors alike, and a noticeably smoother reimbursement process that enhances the overall employee experience.

Get started .

Expense Automation Agent
麻豆原创 Early Adopter Care

Employees burdened by the administrative chore of creating expense reports can now delegate the heavy lifting to a Joule Agent. This agent automatically builds expense reports by aggregating transactions, populating custom fields based on contextual details and user history, and preparing everything for a quick review before submission. The outcome is up to 30%鈥� reduction in time on task for auto-generated expense reports. This offers a modern expense management experience that slashes manual data entry, accelerates the submission process, and frees employees to focus on high-value work rather than paperwork.

See the demo .

Concur Expense, AI-assisted configuration for audit rules
General availability

Expense administrators responsible for managing complex audit rule setups can now interact with their configuration environment in plain language, eliminating the need for deep technical expertise or tedious manual adjustments. This AI-assisted feature enables admins to search existing rules, create new ones, and receive real-time explanations simply by asking questions like “What rules apply to meals in France?”, delivering clear, actionable guidance instantly. The outcome is a 40% reduction in audit rule configuration effort, fewer support tickets, and empowered administrators who work with greater independence, accuracy, and confidence in maintaining compliance logic.

Get started .

Policy Navigator
麻豆原创 Early Adopter Care

Business travelers seeking quick answers to company travel and expense policies no longer need to sift through lengthy documents or wait for admin responses. Policy navigator in Joule allows employees to ask questions in natural language and receive clear, contextual guidance grounded in approved policies, whether planning a trip, in the middle of a journey, or completing an expense report. The result is in-the-moment policy clarity that prevents non-compliant spend before it happens, reduces support tickets, and empowers travelers to make confident, compliant decisions without disrupting their workflow.

Get started .

麻豆原创 Business AI for procurement

麻豆原创 Fieldglass Services Procurement, AI-assisted SOW deliverables creation
General availability

Procurement specialists can accelerate the development of their statements of work using the deliverables feature in 麻豆原创 Fieldglass Services Procurement. The feature analyzes the defined project scope and automatically generates precise, relevant deliverables that ensure tight alignment between buyer expectations and supplier commitments. By adopting this capability, organizations can reduce the time required to manually create SOW deliverables by 70% and cut the risk of poor outcomes by 50%, while fostering stronger collaboration during the negotiation process.

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麻豆原创 Business AI for customer experience

Catalog Optimization Agent
General availability

E-commerce product managers tasked with maintaining large 麻豆原创 Commerce Cloud catalogs gain an always-on agent that continuously reviews product descriptions, attributes, and translations against company quality standards. This agent pinpoints merchandising gaps and delivers actionable recommendations to enhance catalog accuracy, ensure consistency across languages, and improve product discoverability. The business impact is a 70% reduction in time to translate catalog data, 65% less time spent adding descriptions per asset, and a five percent reduction in data quality costs, all of which contribute to higher conversion rates and a more agile merchandising operation.

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麻豆原创 Revenue Growth Management, AI-assisted trade promotion creation
General availability

Key account managers in consumer industries can benefit from a streamlined, single-view promotion-creation experience in which simply naming a promotion automatically populates key fields. Drawing on master data, historical promotions, and learned preferences specific to each retailer, the system suggests dates, types, durations, and sell-in periods, then continuously refines its recommendations based on user edits over time. The impact is a 75% reduction in promotion setup time, 30% fewer data-entry errors and rework, and increasingly personalized suggestions that eliminate repetitive manual effort across promotion cycles.

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麻豆原创 Business AI for IT and developers

Joule Studio code editor and Joule Studio CLI

Building on the transformative capabilities of Joule studio low-code, 麻豆原创 is expanding the Joule studio family with two powerful new offerings designed to meet developers exactly where they work: Joule Studio code editor, a Visual Studio Code IDE extension, and Joule Studio CLI, a versatile command-line interface. Together, these tools deliver a unified, AI-assisted development experience that spans the full spectrum of development personas and preferences on Joule.

• Joule Studio code editor brings the intelligence of Joule directly into Visual Studio Code, the world’s most popular development environment, empowering pro-code developers with AI-guided scaffolding, contextual code generation, intelligent recommendations, and seamless integration with Joule, all without leaving their preferred IDE. 
• Joule Studio CLI extends this same power to the terminal, enabling developers and DevOps teams to automate project creation, manage configurations, execute deployments, and orchestrate CI/CD workflows through scriptable, command-line commands鈥攊deal for headless environments, automation pipelines, and teams that value speed and precision at the command line.

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Joule with 麻豆原创 Datasphere
General availability

Data professionals working within 麻豆原创 Datasphere can now accomplish informational, navigational, and transactional tasks through natural conversation with Joule. Whether asking how to use specific functionalities, retrieving details about a 麻豆原创 Datasphere instance, or switching system settings like language preferences, users receive instant answers with direct references to product documentation. Joule can even execute tasks directly from the conversation without requiring interaction with the standard interface. This direct execution reduces reliance on internal IT support and enables faster, more intuitive navigation throughout the platform.

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麻豆原创 Document AI, enhancements

Document level confidence
Customers now set confidence ranges for fields in the Schemas feature. When customers edit field settings, they can define their own thresholds for low, medium, and high confidence. These custom settings are reflected in the extraction results displayed for the relevant fields on the document details screen. See and .

Expanded Transportation Management
Customers can now use the Transports feature to export and import channels and workflows. See .

New schemas: business partner + delivery note for WM
The service plans embedded edition and premium edition now also support the standard document type, business partner document. See the list of supported document types in . Get started with 麻豆原创 Document AI, and .

Generative AI Hub in AI Foundation, enhancements

Metadata
Customers can now manage metadata for documents, collections, and chunks created with the Vector API to enable advanced filtering and organization of their content. For more information, see .

Retrieval API
Customers can merge and rank search results across multiple data repositories using the Retrieval API’s post-processing capabilities. For more information, see .

Prompt optimizations
Custom metrics are supported in prompt optimizations, enabling customers to define and optimize prompts based on their specific evaluation criteria. Only LLM-as-a-judge metrics with numerical or Boolean output types can be used in optimization tasks.For more information, see and . Customers can provide separate test and train datasets for prompt optimization. For more information, see .

Prompt registry
The prompt registry now enables customers to create and manage orchestration configurations declaratively, allowing them to version and track complex AI workflows alongside their prompts for better governance and reproducibility.For more information, see .

Secrets
Customers can now enter generic secrets using a form instead of JSON. The form appears in the Add Generic Secret dialog when you activate document grounding. A dropdown menu lets them choose the type of document repository. Depending on their selection, the remaining fields adjust dynamically, allowing them to complete the data. Some fields are already prefilled.If they prefer working directly with JSON, switch to the code view by clicking the 顒� icon. For more information, see .

New models available
New models are supported, including OpenAI GPT 5.2, Gemini 3.0 Pro, Perplexity Deep Research, and Anthropic Claude Opus 4.6.For more information on new and deprecated models, .

麻豆原创 Joule for Developers, ABAP AI capabilities, enhancements

New ABAP AI capabilities mean developers can expect a 20% reduction in time and effort to write ABAP/JAVA code, 25% reduction in time and effort to test ABAP/JAVA code, and 4.4% faster time to realized value.

This quarter, developers can now easily generate ABAP Unit tests for:

• Public, protected, and private methods of global ABAP classes
• Public methods of local classes within global class pools

See .

In addition, the documentation chat allows developers to interact with documentation on the 麻豆原创 Help Portal, providing context-aware answers and links to relevant documentation. This capability enhances productivity by offering quick access to related documentation directly within the development environment. See .

Finally, developers can now get AI-powered explanations of their ATC findings and code in the Custom Code Analysis/Custom Code Migration app. See and .

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麻豆原创 Business AI for industries

Tender Analysis Agent
General availability

Sales teams can elevate their tender response process with the Tender Analysis Agent, which automates the review of complex RFQ documents. The agent extracts critical product requirements, flags potential risks and policy gaps, and suggests optimized configurations tailored to customer needs. By reducing the effort to process incoming tenders by five percent and improving win rates, organizations can achieve measurable revenue growth while accelerating sales cycles and uncovering valuable cross-sell and up-sell opportunities.

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麻豆原创 Commodity Management, AI-assisted commodity work center
General availability

Commodity traders can transform how they capture and manage complex deals using the commodity work center in 麻豆原创 Commodity Management. Working alongside Joule, the feature converts verbal or written negotiations into detailed draft deals, automatically populating the numerous fields that traditionally require extensive manual entry. This enables traders to redirect their focus toward negotiating better commercial outcomes, while improving data accuracy and driving greater operational efficiency across their trading activities.

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麻豆原创 Intelligent Clinical Supply Management, AI-assisted predictive subject dynamics
General availability

Clinical trial coordinators seeking to boost their supply planning capabilities will find a powerful ally in 麻豆原创 Intelligent Clinical Supply Management. The predictive subject dynamics feature analyzes historical and real-time data to forecast patient enrollment trends and dropout rates, automatically generating insights that would otherwise require extensive manual analysis. This enables supply chain teams to redirect their focus to strategic decision-making, while reducing clinical inventory waste costs by up to two percent and improving demand forecasting accuracy across their trial operations.

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Joule with 麻豆原创 Intelligent Clinical Supply Management
General availability

Clinical supply professionals juggling multiple tasks and complex systems need quick access to information without disrupting their workflow. Together with Joule, 麻豆原创 Intelligent Clinical Supply Management delivers an intuitive, conversational interface that understands natural-language requests, enabling users to retrieve critical data and navigate to relevant applications effortlessly. This streamlined experience results in an 83% reduction in time spent on information searches, freeing teams to concentrate on higher-value activities and significantly boosting overall productivity.

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麻豆原创 Self-Billing Cockpit, AI-assisted document processing
General availability

Billing clerks managing self-billing workflows frequently encounter invoices arriving in a mix of formats鈥擡xcel, PDF, CSV, or text files鈥攐ften unstructured and spanning multiple languages. 麻豆原创 Self-Billing Cockpit addresses this challenge by leveraging intelligent document processing to parse and extract invoice data from virtually any format, converting it into structured payloads ready for automated billing. The result is significantly reduced time spent processing invoice line items, fewer customer-specific interfaces for integration specialists to build and maintain, and improved extraction accuracy through minimized manual intervention.

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麻豆原创 Business AI for business transformation management

Joule with 麻豆原创 Signavio solutions
General availability

Process analysts and optimization specialists working across complex organizational workflows require rapid access to diagrams, documentation, and performance metrics. 麻豆原创 Signavio solutions integrate with Joule to enable natural-language keyword searches across process diagrams, dictionary items, and help resources. At the same time, best-practice KPI recommenders guide users to the most relevant success measures. This intuitive approach delivers 50% faster information searches and navigation, ensuring teams make data-driven decisions with improved search quality and an enhanced overall user experience.

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麻豆原创 Signavio solutions, AI-assisted business process model and notation simulation insights
General availability

Process analysts leveraging 麻豆原创 Signavio can now access embedded business process model and notation simulations directly within their process diagrams, eliminating the need for fragmented tools and manual interpretation. Key metrics such as costs, cycle times, and resource utilization are automatically translated into clear, actionable summaries that highlight bottlenecks and opportunities for improvement. This streamlined approach reduces time to access process modeling insights by 50%, empowering teams to compare scenarios effortlessly and communicate findings to stakeholders with greater confidence and clarity.

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麻豆原创 LeanIX solutions, AI-assisted architecture guidance
General availability

Enterprise architects seeking to accelerate transformation initiatives can leverage 麻豆原创 LeanIX to surface actionable insights directly from their architecture inventory. The feature analyzes enterprise architecture data to identify opportunities and guides users through the workflows and tasks needed to efficiently act on recommendations. Organizations benefit from a 95% reduction in time to discover insights, 80% faster transformation execution, and a five percent reduction in value erosion from delayed action. Overall, this feature drives greater architectural productivity and more agile decision-making.

Get started .

Jonathan von Rueden is chief AI officer of 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

I鈥檓 pleased to announce that next鈥慻en 麻豆原创 Ariba is now available, marking the next phase in 麻豆原创鈥檚 journey to reimagine source鈥憈o鈥憄ay for the age of AI. This milestone represents the transition from announcement to execution, bringing a fundamentally rebuilt platform into customers鈥� hands.

This milestone comes alongside strong industry recognition. 麻豆原创 has been named a Leader in the 2026 Gartner庐 Magic Quadrant™ for Source鈥憈o鈥慞ay Suites, an acknowledgment that aligns with the delivery of next-gen 麻豆原创 Ariba and our continued focus on platform modernization and AI鈥慸riven innovation at enterprise scale.

Why rebuilding the foundation matters

As procurement leaders know, AI鈥檚 potential is widely recognized, but its impact has often been uneven. Confidence is high, yet results depend on more than algorithms alone. AI only delivers value when it is supported by the right data, processes, and platform architecture. That belief shaped our decision to rebuild 麻豆原创 Ariba from the ground up.

Independent analyst firm as 鈥渁 complete reengineering of the largest and most entrenched source鈥憈o鈥憄ay platform in the world,鈥� emphasizing that this move goes far beyond adding AI features to legacy systems. Instead, it establishes the architectural foundation required for AI to operate reliably and at scale.

This aligns with what we consistently hear from customers that sustainable impact requires modernization at the core.

An AI鈥憂ative source-to-pay platform built on 麻豆原创 Business Technology Platform

Next鈥慻en 麻豆原创 Ariba is built on (麻豆原创 BTP), providing a unified, real鈥憈ime data foundation across the source鈥憈o鈥憄ay lifecycle. This shift enables tighter integration with , improved extensibility, and faster innovation delivery.

By moving to 麻豆原创 BTP, 麻豆原创 Ariba can support open APIs, cross鈥憇uite data consistency, and the responsiveness required for intelligent, AI鈥慸riven procurement operations鈥攃apabilities that are increasingly expected of leading source鈥憈o鈥憄ay platforms but are difficult to achieve on legacy architectures.

Current next鈥慻en capabilities will continue to be delivered incrementally throughout 2026 and into 2027, giving customers flexibility to adopt innovation at a pace that aligns with their business priorities.

Embedded intelligence with Joule: moving from insight to action

A defining element of next鈥慻en 麻豆原创 Ariba is the deep integration of directly into procurement workflows. Rather than treating AI as an optional add鈥憃n, next鈥慻en 麻豆原创 Ariba can embed intelligence where work happens鈥攕upporting faster, more informed decisions while reducing friction across everyday processes.

Early capabilities include:

• A Bid Analysis Agent, which can automatically evaluate complex bid scenarios, including total cost considerations
• AI-assisted contract support to help automate routine inquiries, generate summaries, and provide instant access to contract details

These capabilities reflect a broader shift from systems that require constant manual input to platforms that actively support outcomes.

A more unified, intuitive procurement experience

Next鈥慻en 麻豆原创 Ariba also addresses long鈥憇tanding fragmentation across the source鈥憈o鈥憄ay lifecycle.

Key improvements include:

• 麻豆原创 Ariba Intake Management, now globally available, providing a single entry point for procurement requests
• A simplified 麻豆原创 Fiori鈥慴ased user experience, delivered through a central launchpad
• A modernized contract lifecycle, supported through integration with Icertis Contract Intelligence

Together, these improvements are designed to make procurement easier to engage with while working to ensure processes remain connected, compliant, and intelligent behind the scenes.

What this means for customers

For existing 麻豆原创 Ariba customers, next鈥慻en 麻豆原创 Ariba provides choice and continuity. Customers can:

• Transition to next鈥慻en 麻豆原创 Ariba on a voluntary basis.
• Access next鈥慻en capabilities without commercial implications.
• Run current and next鈥慻en environments in parallel during an active transition, reducing risk and disruption.

麻豆原创 is providing tools, services, and advance timelines to support a managed transition, allowing organizations to move forward with confidence rather than urgency.

The foundation for what comes next

Next鈥慻en 麻豆原创 Ariba is not an endpoint, it is the foundation for the future of procurement.

With an AI鈥憂ative architecture, embedded agentic intelligence, and a unified user experience, this new generation of 麻豆原创 Ariba helps organizations move beyond transactional efficiency toward smarter decisions, greater resilience, and measurable business outcomes.

As Ardent Partners observed, this rebuild has the potential to act as a catalyst鈥攏ot just for 麻豆原创 Ariba customers, but for the broader procurement technology landscape. By combining scale, data, and AI in a fundamentally new way, next鈥慻en 麻豆原创 Ariba is helping define what modern source鈥憈o鈥憄ay platforms can deliver.

With the solution now available, we look forward to partnering with customers as they move from vision to value. Together, we can shape the future of intelligent procurement.

Baber Farooq is senior vice president of Product Marketing for 麻豆原创 Ariba and 麻豆原创 Fieldglass.

Building resilience and control across every category of spend

Organizations are under increasing pressure to manage costs, improve agility, and drive measurable outcomes. 麻豆原创鈥檚 connected and intelligence-driven source-to-pay suite is designed to help customers meet these challenges head on.

麻豆原创 Ariba solutions can deliver broad and deep functionality across the full source-to-pay lifecycle, spanning sourcing, contracting, procurement, invoicing, and supplier management. With the industry鈥檚 largest supplier network, 麻豆原创 enables buyers and suppliers to collaborate with confidence, consistency, and scale.

麻豆原创鈥檚 investment priorities: platform modernization and agentic AI innovation

Rather than reflecting external judgments, 麻豆原创鈥檚 strategic focus is centered on advancing its platform foundation, AI capabilities, and user experience to help customers operate with greater intelligence and agility. Our long鈥憈erm investments concentrate on three core areas:

Modernizing the platform for the future

A platform update arriving in 2026 will complete the modernization of 麻豆原创鈥檚 technical architecture. This modernized foundation can deliver greater extensibility, improved performance, and faster delivery of innovation, particularly in agentic and generative AI.

Built as an AI-native architecture, the next-generation platform can embed intelligence directly into workflows to help anticipate needs, guide decision-making, and automate actions across the entire source-to-pay process. This positions 麻豆原创 to deliver the first truly AI-native source-to-pay suite built for the future of procurement.

Expanding intelligence with Joule

plays a central role in bringing intelligence and insight to every stage of the source-to-pay process. Joule鈥檚 advanced AI agents can help automate tasks, support decision-making, enhance compliance, and unlock new productivity across sourcing, procurement, and supplier collaboration.

Reimagining the user experience

麻豆原创 is delivering an updated, consistent UI/UX across 麻豆原创 applications. For procurement teams, this means smoother navigation, modernized interfaces, and enriched contextual intelligence, including enhanced supplier 360 profiles and strengthened collaboration capabilities.

Strengthening global scale and operational flexibility

麻豆原创 continues to demonstrate industry-leading global scale, supporting high-volume transactions and diverse compliance requirements across regions and industries. With multiple cloud deployment options across major hyperscalers and a robust portfolio of security and regulatory certifications, including FedRAMP, customers can operate confidently wherever they do business.

Connected solutions across 麻豆原创 Business Network and the intelligent suite

麻豆原创 Business Network remains the largest supplier network in the source-to-pay market, spanning more than 190 countries. 麻豆原创鈥檚 broader spend ecosystem鈥攊ncluding innovations such as , , and 鈥攅nables organizations to unify data, intelligence, and processes across sourcing, procurement, invoicing, and spend management.

Importantly, 麻豆原创鈥檚 capabilities extend well beyond traditional source-to-pay. Through connected solutions covering travel and expense, contingent workforce management, external labor, and additional spend categories, 麻豆原创 provides a truly comprehensive spend management platform that can deliver visibility and control across the full spectrum of enterprise spend.

As organizations increasingly operate in heterogeneous application landscapes, 麻豆原创 helps deliver openness, security, and extensibility so customers can maintain cohesive and connected processes throughout 麻豆原创 and non-麻豆原创 environments.

Customer impact: outcomes that scale

Customers across industries and regions continue to demonstrate what鈥檚 possible with 麻豆原创鈥檚 source-to-pay solutions. Organizations report meaningful improvements in areas such as compliance, cost optimization, supplier collaboration, operational efficiency, and workforce productivity鈥攆rom managing millions of invoices to running global sourcing initiatives to scaling AI-powered automation across distributed operations.

麻豆原创 remains deeply committed to helping procurement and finance organizations navigate complexity with confidence. Our investments in platform modernization, agentic AI, user experience, and cross-suite integration are all grounded in a single mission: to help customers achieve sustainable, long-lasting impact.

We’re grateful for this recognition and energized by the opportunity to deliver even greater value in the years ahead.

. Read the full Gartner Magic Quadrant for Source-to-Pay Suites report .

Fang Chang is EVP and chief product officer for 麻豆原创 Procurement and External Workforce Solutions.
Baber Farooq is senior vice president and head of Market Strategy for 麻豆原创 Procurement and External Workforce solutions.

*Gartner Magic Quadrant for Source-to-Pay Suites, January 21, 2026 – ID G00833291, by Micky Keck, Kaitlynn Sommers, Lynne Phelan, Magnus Bergfors, Alex Brady

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are a trademark of Gartner, Inc., and/or its affiliates.

That鈥檚 why the latest recognition from two of the industry鈥檚 most respected research firms鈥擜rdent Partners and Staffing Industry Analysts (SIA)鈥攎eans so much to us at 麻豆原创. Their findings reinforce what we see every day: isn鈥檛 just keeping up with the industry, we鈥檙e setting the standard for innovation, execution, and customer value.

Market leadership confirmed by independent analysts

named 麻豆原创 Fieldglass a 鈥淢arket Leader,鈥� a distinction reserved for providers with universal strengths and top-tier execution. The evaluation looked at every aspect of our portfolio, from supporting the full scope of managing the external workforce鈥攔equisitions, candidates, projects, direct sourcing, SOW/services procurement, integrations, and AI鈥攖o our execution ability, client success, references, talent ecosystem, product vision, and future of work readiness.

We鈥檙e especially proud to be recognized as an 鈥淓lite Performer鈥� in four critical categories: AI Innovation, SOW and Services Procurement, Direct Sourcing, and Total Talent Management. Ardent Partners highlights 麻豆原创 Fieldglass鈥� deep integration with 麻豆原创 SuccessFactors solutions, 麻豆原创 Ariba solutions, 麻豆原创 Build Work Zone, and the AI solution Joule, positioning it as a strategic hub for managing external talent and optimizing services procurement. Joule is transforming how users interact with the portfolio, offering conversational, data-driven guidance for requisition creation, SOW intake, job description design, and workflow acceleration.

also confirms 麻豆原创 Fieldglass鈥� global leadership. The portfolio operates in more than 180 countries, supports 22 languages, and enables invoicing in 118 countries, making it the largest footprint in the industry. This extensive reach is further underscored by consistently strong customer retention, reflecting the deep trust and satisfaction 麻豆原创 Fieldglass enjoys among global enterprises.

Customer-validated leadership

Recognition from leading analyst firms is important, but what matters most is the experience of our customers. 麻豆原创 Fieldglass has also been ranked as a category leader by , a respected platform for verified customer reviews. TrustRadius rankings are based on direct feedback from real users, making it a powerful complement to analyst evaluations. While analyst reports validate market position and strategic capabilities, TrustRadius highlights the day-to-day value customers experience, such as ease of use, depth of functionality, and overall satisfaction. This dual endorsement, from both industry experts and actual practitioners, reinforces 麻豆原创 Fieldglass鈥� commitment to delivering solutions that can meet business needs and exceed customer expectations.

Innovation at the core: AI, automation, and analytics

Innovation is at the heart of 麻豆原创 Fieldglass鈥� value proposition. The portfolio鈥檚 embedded AI solution, Joule, helps users initiate job requisitions, design and translate job descriptions, and accelerate approvals through a conversational interface. Rate benchmarking helps ensure customers stay competitive with market rates. Document AI can automate structured SOW creation, while machine learning and skills ontology models can power advanced candidate matching and workforce planning.

SIA notes that 麻豆原创 Fieldglass鈥� 鈥淎I-first鈥� product strategy is embedding intelligence into every workflow, emphasizing seamless data extraction, intuitive user experiences, and impactful use cases across applications. The business intelligence suite delivers real-time and historical analytics, benchmarking tools, and persona-based dashboards, empowering organizations to make data-driven decisions at every stage of the talent lifecycle.

Comprehensive capabilities for a modern workforce

What distinguishes 麻豆原创 Fieldglass is its wide-ranging and comprehensive set of features. The portfolio allows users to effectively manage all kinds of external labor expenses鈥攚hether that’s contingent labor, services procurement, independent contractors, high-volume workers, or field services. It offers streamlined onboarding, advanced and customizable tools for global compliance, AI-driven workflows, and robust skills extraction, helping to ensure depth and efficiency in every area. Additionally, its compliance and audit capabilities, mobile accessibility, and dashboards supporting diversity, equity, and inclusion help create a holistic solution.

SIA鈥檚 research, based on 23 in-depth VMS surveys and 36 customer assessments, found 麻豆原创 Fieldglass excels in supplier management, time/expense/billing, SOW capabilities, candidate sourcing, technical functionality, and customer perceptions. Customers cite the portfolio鈥檚 innovation, influence over roadmap evolution, and ability to scale with their programs. The open API framework, pre-built integrations, and strong MSP partnerships create a connected ecosystem that can support even the most complex workforce strategies.

Customer experience and ecosystem strength

Customer experience is another area where 麻豆原创 Fieldglass shines. SIA鈥檚 customer perception data shows high marks for platform stability, ease of use, integration quality, adaptability, and support. Customers appreciate the portfolio鈥檚 innovation, the ability to influence roadmap evolution, and its capacity to scale with their programs.

Both Ardent Partners and SIA emphasize 麻豆原创 Fieldglass鈥� commitment to continuous innovation. The 鈥淎I-first鈥� and 鈥渟uite-first鈥� strategies focus on embedding intelligence into every workflow and delivering seamless, end-to-end business processes across the 麻豆原创 ecosystem. Key areas of ongoing investment include skills-based hiring, AI, enhanced analytics, and high-volume workforces for SOW management.

A portfolio for the future of work

The latest findings from Ardent Partners and SIA reaffirm 麻豆原创 Fieldglass鈥� leadership, vision, and relentless focus on customer value. Combined with customer-driven recognition from TrustRadius, 麻豆原创 Fieldglass stands as a proven leader in delivering solutions that can empower organizations to unlock the full potential of their extended workforce鈥攖oday and for the future of work.

.

Amber Roth vice president of Global Presales & Strategy for 麻豆原创 Fieldglass.

External talent already represents a significant share of the labor: about 20% in the UK and 40% in the U.S., with projections reaching 50% globally by 2050. This surge reflects the growing demand for specialized skills and flexible cost structures.

A and conducted by Economist Impact captures this shift. Drawing on insights from more than 2,000 C-suite executives across industries, the report, 鈥淔rom Cost to Capability: Redefining External Workforce Strategy in 2025,鈥� reveals how the external workforce is evolving from a transactional resource into a core capability that drives adaptability and resilience in the AI era.

The findings underscore a critical imperative: organizations must rethink how they engage talent, integrate technology, and foster collaboration across procurement, HR, and finance to unlock the full potential of external workers.

From cost to capability: a strategic pivot

In 2023, nearly one-third of organizations cited risk reduction as their primary reason for using contingent labor and service providers. By 2025, that number dropped to just 6%. Today, cost efficiency (74%) and access to specialized skills (62%) dominate the list.

Cost efficiency is no longer about short-term savings. It鈥檚 about managing broader business risks, from financial exposure to talent scarcity. As Professor David Ulrich of the University of Michigan notes in the report, 鈥淭o mitigate risk, businesses need to reduce fixed costs, and labor is often a high fixed cost that needs to be seen as a source of growth.鈥�

Leaders increasingly view external talent as a long-term solution for project-based and technology-driven work, especially in fast-moving fields like AI, automation, and data science. Looking ahead, 64% of executives plan to expand their talent networks within three to five years, up from 54% last year鈥攃lear evidence that external workforce strategies are becoming integral to workforce planning.

Building a holistic talent ecosystem

Economist Impact鈥檚 research points to the rise of holistic talent supply chain management鈥攁 model that unites HR, finance, and procurement to forecast talent needs, close skills gaps, and treat the workforce as a dynamic ecosystem rather than a fixed headcount.

This evolution builds on the themes from previous Economist Impact reports, where procurement shifted from cost controller to strategic orchestrator of risk, sustainability, and innovation. Forward-looking organizations now embrace 鈥渢otal talent management鈥� models that integrate people, platforms, and partners while aligning culture and communication across internal and external teams.

Breaking down silos and embedding digital tools for real-time visibility will be essential to managing an integrated, skills-based workforce that adapts quickly to business needs鈥攚hile ensuring external workers feel valued and connected to enterprise goals.

Procurement at the center of workforce transformation

Procurement leaders are moving beyond sourcing and compliance to orchestrate entire talent ecosystems built on governance and collaboration. Yet the function faces challenges: confidence in procurement鈥檚 workforce management skills fell from 51% in 2024 to 43% in 2025, reflecting the complexity of its expanded scope.

CFOs and COOs are also taking a more active role, linking external workforce oversight to financial, compliance, and ESG performance. This trend echoes Economist Impact鈥檚 鈥溾�� report, which showed procurement鈥檚 remit expanding as risk management becomes central to business strategy.

To succeed, procurement must double down on visibility, digital integration, and strategic alignment. Platforms that unify workforce data鈥攆rom contingent contracts to skills mapping鈥攚ill enable teams to balance cost optimization with agility and governance.

AI: driving agility and accountability

Artificial intelligence is reshaping how organizations manage external talent. According to the report, 68% of procurement leaders cite AI proficiency and ethics as their top development priority over the next 18 months. AI can predict workforce needs, automate sourcing, and fill gaps in emerging fields like agentic AI and automation. The payoff: higher productivity and faster decision-making.

But AI also raises a critical questions: who benefits from efficiency gains? How should productivity improvements be measured? Answering these will require new cost models and shared accountability across the enterprise.

As seen in earlier Economist Impact research, AI is once again a catalyst for transformation, redefining how organizations manage, measure, and mobilize external talent.

The external workforce of the future

The evolution of the external workforce underscores a broader truth: agility, capability, and collaboration now define competitive advantage.

Organizations that treat external talent as a strategic asset鈥攊ntegrating governance, data, and culture across internal and external teams鈥攚ill be best positioned to respond to technological change and seize new opportunities.

To meet these demands, companies are turning to solutions like and , which help connect procurement and external workforce management. These solutions can deliver the visibility, intelligence, and agility needed to manage today鈥檚 dynamic workforce ecosystem.

Gordon Donovan is global vice president of Research, Procurement, and External Workforce at 麻豆原创.

Against the backdrop of the event theme鈥�”Connect Everything, Achieve Anything: Agents, Data, and the Business Suite鈥濃�攖hey set out 麻豆原创鈥檚 vision for how 麻豆原创 Business Suite, which combines AI, data, and applications, can transform enterprises and deliver unprecedented business value to customers despite global macroeconomic uncertainties. 

Unique times

鈥淲e are living in very unique times,鈥� Alam said at the start of the keynote. 鈥淯nique in terms of the unpredictability we face from a geopolitical and macroeconomic perspective,鈥痑nd also unique in terms of the advancement in AI and the potential that carries for all of us.鈥�  

He added, 鈥淓ven the most significant challenges can be responded to, navigated, and鈥攚hen approached the right way鈥攖urned into opportunities. And that鈥檚 exactly where 麻豆原创 comes in. Because the best way to face uncertainty is with confidence that you can see what鈥檚 happening across your business and your network.鈥� 

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The CFO perspective 

His comments were echoed by CFO and Executive Board Member Dominik Asam in an onstage discussion with economist and Berkeley professor Ulrike Malmendier, during which they likened the role of the CFO in navigating today鈥檚 business uncertainties to that of co-piloting a modern jet. 

鈥淎I is the next supercycle and has what it takes to create an effective cockpit for the CFO, including recommendations about real actionable options all the way to autopilot for less critical tasks,鈥� Asam said. He also warned that standing on the sidelines of technological progress is 鈥渁 sure recipe for falling behind in competition.鈥�  

鈥淎s in prior tech supercycles, the entire competitive playing field is being completely reshuffled,鈥� he said.

New products

Building on this theme, Alam noted that while uncertainty is real and isn鈥檛 going away, it can be successfully navigated and turned into opportunities. To help customers achieve this, he announced several new and enhanced 麻豆原创 products. 

Among them, 麻豆原创 Supply Chain Orchestration is an AI-native application that uses a network knowledge graph to analyze real-time signals across a company鈥檚 multi-tier supply chain to detect risks and issues, such as extreme weather or tariffs. It then helps customers minimize disruptions by assessing the impact of these risks and suggesting alternatives to help minimize disruptions.   鈥淭hink of it like a GPS in your car which selects an alternative route to avoid traffic or tolls,鈥� Alam explained. In addition, he announced a major update to the 麻豆原创 Ariba solutions for source-to-pay suite, positioning 麻豆原创 Ariba as the most modern platform in the industry and the only truly AI-native source-to-pay solution鈥攁ll built on 麻豆原创 Business Technology Platform.

The human-AI partnership opportunity 

Turning to uncertainties over the future of work itself, 麻豆原创 Chief People Officer and Executive Board Member Gina Vargiu-Breuer, who is leading 麻豆原创鈥檚 own workforce transformation, was joined on stage by Ian Beacraft, founder and chief futurist of Signal and Cipher. 

鈥淎t 麻豆原创, we know firsthand that AI is everywhere, shattering norms and transforming the workforce,鈥� Vargiu-Breuer said. 鈥淎nd we as 麻豆原创 are boldly steering this shift, as requirements for our workforce are simply changing really fast.鈥�  

Vargiu-Breuer emphasized that a successful business AI strategy also depends on leveraging AI alongside human expertise and fostering what she described as 鈥渁 collaborative human + AI approach, rather than just automation.鈥�  鈥淏y integrating AI’s capabilities with human creativity, empathy, and judgment, we move beyond simple automation into real human-AI power couples,鈥� she said. 鈥淯ltimately, the future will favor those who embrace orchestration, delegation, and creative problem-solving.鈥�

Role-based AI assistants

Echoing this human-centric approach, Alam noted, 鈥淓veryone is talking about agents鈥攂illions of agents鈥攁nd agents taking over everything. We want to talk about people, the roles they play, and the tried-and-tested organizational constructs in place today that are running complex businesses across industries.鈥� 

Reflecting this, he said 麻豆原创 is making Joule deeply aware of the workstyle of the person it partners with, their role, and the business process context in which they operate. 鈥淭oday, we are introducing AI assistants, including a receivables assistant for your accounts receivable colleagues, a controlling assistant for your controlling colleagues, a demand Planning assistant for your demand planning colleagues, and an AI assistant for every role.鈥�   Each assistant has agents and AI tools at their disposal to help make the person it partners with smarter and more efficient, Alam explained.

Taking the receivables assistant as an example, he said it can handle collections and dispute resolution today and will soon also be able to help with fraud detection, invoice processing, and payment scheduling. 鈥淥ver time, we will continue to make this assistant even smarter by adding more agents and capabilities鈥攁ll designed to make the person in this role more effective.鈥�

Deep research and new intelligent applications

鈥淲hile efficiency and automation carry potential for great value for the organization, AI鈥檚 greatest strength is in unlocking insights and recommendations based on its ability to do deep research across vast amounts of internal and external data, which is hard for people to do,鈥� said Alam. To address this challenge, 麻豆原创 is introducing deep research in Joule, a capability that can research and analyze complex problems and deliver findings quickly and efficiently. 

Following additional announcements, including an expanded set of 麻豆原创 Business Data Cloud Intelligent Applications and an enterprise wide, multi-stakeholder engagement orchestration solution in 麻豆原创 Engagement Cloud, Alam closed out the keynote by reminding his audience: 鈥淭o create exponential value and to reach the global maxima for your enterprise, the whole matters.  And that鈥檚 what we are focused on: providing you with best-in-class applications, seamlessly and natively integrated across the breadth of finance, spend, supply chain, HCM, and customer experience.鈥� 

麻豆原创 Business Suite delivers unprecedented value

The unmatched value of 麻豆原创 Business Suite is that it brings together the power of best-in-class applications with a semantically rich harmonized data layer to power high-value AI in a singular seamless experience. It helps you manage uncertainty, evolve your workforce, and break down silos to create amazing customer value.

In times of uncertainty, spend is one of the levers companies can actively control. Leaders are expected to deliver not only savings, but also resilience, sustainability, and risk-aware processes.

At , we introduced several new AI capabilities to bring more automation, deeper integration, and smarter insights across our . These innovations are focused on improving outcomes: reducing risk, strengthening supply chains, increasing productivity, and freeing people to focus on higher-value work. They ensure spend continues to fulfill its elevated role as a driver of business growth and resilience while helping customers stay ahead of evolving demands.

Next-gen 麻豆原创 Ariba brings deeper AI integrations and new AI assistants in Joule

麻豆原创 is rolling out the next generation of solutions for source-to-pay to automate the power of procurement, improve cost savings, ensure compliance, and build supply chain resilience. Next-gen 麻豆原创 Ariba is the world’s most modern source-to-pay solution designed to power the future of procurement.

The first AI-native source-to-pay suite redefines what is possible today and sets the course for the future. With businesses facing ever-present change, our customers can take advantage of a solution that’s built to endure — intelligent, integrated, and scalable. The capabilities delivered as part of next-gen 麻豆原创 Ariba will empower businesses to tackle today’s challenges and excel in the future.

Next-gen 麻豆原创 Ariba is built on 麻豆原创 Business Technology Platform (麻豆原创 BTP), and has a completely redesigned user experience, embedded analytics, and seamless integration with 麻豆原创 Cloud ERP public and private applications.

The advanced capabilities and benefits of next-gen 麻豆原创 Ariba solutions offer the following:

• Accelerated value realization: Faster adoption with intuitive interfaces and guided workflows
• Deeper insights: Actionable analytics that translate into tangible savings and operational improvements
• Empowered teams: A more productive experience for procurement professionals

Next-gen 麻豆原创 Ariba capabilities will be rolled out starting in Q1 2026.

麻豆原创 also introduced new agentic and generative AI capabilities across the 麻豆原创 Ariba suite to help organizations optimize productivity and decision-making. Planned for release in Q1 2026, a new Joule Agent for bid analysis automatically compares supplier bid data, including total cost, to give procurement teams insight and recommendations for award decisions.

麻豆原创 also added three AI features to the 麻豆原创 Ariba procurement portfolio:

• AI supplier-response summary will use to help review, analyze, and summarize supplier responses to questionnaires, helping procurement specialists make more informed decisions. This is planned to be generally available in Q1 2026.聽
• Joule for simplified invoice creation will help employees outside accounts payable to easily submit invoices from for processing with minimal input. The feature will be generally available in Q4 2025.
• Intelligent contracting will use Joule to automatically extract key information, generate summaries, and search historical contracts for discrepancies and compliance issues. This feature will be generally available in Q4 2025.

As part of the broader 麻豆原创 Ariba portfolio, is now globally available. The solution provides a single-entry point for all procurement requests to simplify intake, improve compliance, and give employees full visibility into status updates. With Joule, users can easily submit requests or follow guided steps, while the system orchestrates processes across 麻豆原创 and non-麻豆原创 systems.

Learn more at our upcoming webinar, on November 20, 2025. to see how organizations can streamline intake processes and drive meaningful results.

New AI enhancements in 麻豆原创 Fieldglass connect the right external talent to the right roles

New AI enhancements to the portfolio make it easier to match potential external workers with available positions by automatically analyzing role descriptions to determine the skills required for each position.

The new skills-based hiring feature automatically analyzes role descriptions to determine the skills required for each position, reducing the time to source potential candidates by up to 50 percent. By accelerating candidate matching, organizations can count on a faster, more cohesive, and efficient hiring process.

This feature is generally available and works in conjunction with existing AI features, including resume analysis and job description creation.

麻豆原创 Concur and American Express Global Business Travel make business travel easier, smarter and more impactful for organizations everywhere

with American Express Global Business Travel (Amex GBT). Complete by 麻豆原创 Concur and Amex GBT is a co-developed solution that will combine booking, servicing, payments, and expensing into one experience with AI at its core. The solution will benefit from 麻豆原创 Concur and Amex GBT technology and business travel expertise. Complete customers will also have access to Amex GBT鈥檚 unmatched marketplace, software, and services via Complete, offering greater incentives and cost savings.

Smarter AI for travel and expense management in 麻豆原创 Concur

We also introduced new AI capabilities for that will streamline reporting, strengthen fraud prevention, and simplify travel planning, helping organizations better control costs while improving the employee experience.

These innovations include:

• The Receipt Analysis Agent, a newly embedded Joule Agent in , will go beyond basic receipt scanning, automatically completing missing information, determining expense types, and verifying vendor details, even for unknown vendors, to deliver accurate results without iterative learning. General availability for these features is expected for Q4 2025.
• , the automated expense-audit service from 麻豆原创 Concur now features an AI-generated receipt checker that flags receipts likely created by AI or online generators, helping auditors stop fraudulent claims. The service is currently generally available.
• A new pre-spend planner tool uses generative AI to estimate trip budgets based on policies and itineraries, while also providing relevant travel advisories to keep travelers informed. General availability is planned for early 2026 and Concur Expense customers can take advantage of the pre-spend planner at no extra cost. It will be available to Concur Travel customers later in the first half of 2026

Partnerships and integrations

麻豆原创 Concur has enhanced its integration with to further automate how ride and meal expenses are processed, providing better visibility to executives and business users. Ride and meal receipts are automatically routed into , with detailed tax, tip, and itemization data, resulting in cleaner data, fewer manual corrections, and faster reimbursement.

Looking ahead

麻豆原创鈥檚 latest innovations across procurement, external workforce, and travel and expense help customers achieve positive outcomes such as cost savings, reducing risk, strengthening supply chains, creating value, improving productivity, and freeing people up to do higher-value work.

Etosha Thurman is chief marketing officer for 麻豆原创 Finance and Spend Management.

麻豆原创 reimagines enterprise AI with role-based assistants in
Joule that coordinate agents across lines of business

LAS VEGAS 鈥� At its inaugural 麻豆原创 Connect event, (NYSE: 麻豆原创) showcases how the integration of AI, data and applications creates unparalleled business value.

These breakthroughs 鈥� including a new network of role-based assistants in Joule that partner with humans to elevate performance, an expanding data ecosystem that drives deeper insights and supply chain software that anticipates disruptions 鈥� once again revolutionize how business gets done.聽

“To thrive when volatility is the new normal, businesses need more than a patchwork of disparate best-of-breed applications,” said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering. “Our announcements today demonstrate the power of 麻豆原创 Business Suite, where AI, data and applications come together in an experience to propel smarter decisions, faster execution and scalable transformation.”

AI that Partners with People

麻豆原创 unveils Joule鈥檚 next stage as the AI force at the center of 麻豆原创 Business Suite鈥檚 value creation. Drawing on the applications and data from across 麻豆原创 Business Suite, 麻豆原创 is introducing a new generation of role-aware assistants in Joule. Each assistant is designed to partner with a human being in their specific business role. Assistants in Joule tap into the right agents for the job, configuring, orchestrating and managing them so humans can focus on unlocking new levels of insight and productivity.

Supporting the assistants in Joule is a , designed to help execute complex workflows within a specific function. For instance, a People Manager Assistant coordinates a team of specialized agents — including the new People Intelligence Agent, which helps spot and resolve issues like compensation anomalies — to support managers as they drive performance. A new Financial Planning Assistant will be aided by a group of expert agents — including the new Cash Management Agent, which optimizes cash flow and improves interest yields — to help finance professionals drive efficiencies. This new roster of role-aware AI assistants not only partner with people to elevate performance in their lines of business but also work together across business functions to solve complex enterprise-wide problems.

Data that Defies Boundaries

Data fuels AI鈥檚 transformative power but it鈥檚 often siloed in different systems. At 麻豆原创 Connect, we are removing those barriers with 麻豆原创 Business Data Cloud Connect. 麻豆原创 BDC Connect securely links 麻豆原创 BDC with partner platforms to enable a bidirectional flow of business-ready data products across organizational and technological boundaries.

With zero-copy sharing, data stays securely in 麻豆原创 systems yet remains instantly accessible in customers鈥� existing data platforms, preserving business context without costly copies. The result: fewer silos, simpler pipelines, no duplication — just trusted data products where and when they鈥檙e needed.

麻豆原创 also announced that Databricks and Google Cloud are the first partners enabled for 麻豆原创 BDC Connect, with more to follow. As announced in February 2025, 麻豆原创 Databricks remains a data service within 麻豆原创 Business Data Cloud, and 麻豆原创 BDC Connect extends its benefits across an open data ecosystem. These partnerships give customers faster access to data products for analytics and AI, helping teams move from raw data to real-time business outcomes with greater speed and simplicity.

Applications that Turn Data into Action

At the heart of 麻豆原创鈥檚 unique value proposition are enterprise applications where data is created and AI-driven insights are experienced. 麻豆原创 Supply Chain Orchestration is a new AI-native solution that combines the power of Joule with a live knowledge graph to detect real-time risks several suppliers deep and orchestrate a coordinated response, helping customers cut costs and keep supply chains moving. 麻豆原创 Engagement Cloud, a new customer experience solution, uses business-critical context to personalize interactions across customers, suppliers and other stakeholders. And our next-generation 麻豆原创 Ariba procurement suite stands out as an AI-native solution, bringing intelligence to every stage of spend management, from sourcing through supplier engagement.

Altogether, these 麻豆原创 Business Suite innovations mark the beginning of a new era powered by self-reinforcing AI, data and applications that drive intelligence, speed and resilience.

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About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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Yet a curious trend has emerged: many companies are pursuing environmental, social, and governance (ESG) goals more discreetly, a phenomenon known as 鈥済reen-hushing.鈥� In fact, found that while some companies have pulled back, about 84% are either maintaining or strengthening their climate commitments, even as they become less vocal about them. This demonstrates that despite shifting political winds, businesses recognize that sustainability initiatives remain fundamentally good for business.

A comprehensive and conducted by Economist Impact confirms this shifting landscape, revealing growing confidence in procurement鈥檚 capacity to deliver on ESG performance indicators. While risk factors are rising, so is readiness鈥攁 shift that is especially encouraging for procurement executives overseeing ESG performance. The data shows approximately 90% of respondents report strong internal collaboration between procurement and other departments, up from 75% last year.

The external picture is equally promising: over 90% of executives report that deeper supplier partnerships are yielding benefits, particularly in driving innovation and sustainability outcomes. For procurement leaders, the data tells a clear story: collaboration may be the new currency of success.

Collaboration: The silent engine of progress

Procurement鈥檚 strategic influence is expanding through tighter integration with internal stakeholders. As teams become more embedded across business functions, they鈥檙e unlocking new value through improved orchestration and proactive planning. Specialized business partner groups that bridge strategy and execution are helping meet cross-functional needs.

This enhanced collaboration has yielded key benefits, including cost savings (74%) and improved digital adoption (61%). However, a gap remains between these immediate gains and longer-term value drivers like innovation and agility. As procurement operations continue to digitize, talent development must adapt. AI proficiency and ethical technology have emerged as the top skill priorities for procurement hires over the next 12-18 months (68%), followed by sustainability expertise (55%).

Enabled by digital tools that deliver 鈥�360-degree visibility,鈥� procurement executives are seeing alignment between category strategies and broader enterprise objectives, helping them spot risks, understand market shifts, and manage supplier performance more effectively.

Supplier partnerships: Accelerating ESG progress and innovation

Forward-thinking procurement organizations are now partnering with suppliers to introduce solutions like recyclable packaging, greener materials, and low-emission logistics. These collaborations are accelerating decarbonization efforts while helping future-proof supply chains.

Despite decreased governmental prioritization of sustainability in some regions, smart companies continue pursuing ESG goals when they align with business objectives. Nearly half (48%) of executives report improved sustainability performance from closer supplier ties, and 38% cite more supplier-driven innovation.

Many procurement teams are navigating uncertainty around their ESG approach, questioning whether to lead sustainability initiatives boldly or keep their efforts under the radar amid regulatory shifts. Regardless of this strategic tension, the value of supplier collaboration remains undeniable, particularly as these efforts often improve operational efficiency and reduce costs.

Procurement can strengthen this momentum by investing in platforms that track emissions, monitor regulatory developments, and support compliance. Many organizations are expanding efforts to address Scope 3 emissions鈥攖ypically the most challenging to measure鈥攚ith increasing sophistication around supplier data collection and performance expectations. This shift reflects increased accountability across the value chain and reinforces procurement鈥檚 pivotal role in advancing ESG objectives.    

While public sentiment and political consensus may fluctuate, regulators continue to advance mandatory disclosure requirements and climate accountability measures. Preparation is especially urgent as new policies鈥攆rom the EU Corporate Sustainability Reporting Directive to Germany鈥檚 Supply Chain Due Diligence Act鈥攊mpose stricter requirements on businesses.

Even if some regulations face delays or deprioritization, they are not disappearing. Organizations that act now will be best positioned to lead when enforcement inevitably intensifies.

Meeting elevated standards through supplier collaboration

The evolution of procurement鈥檚 influence rests on one fundamental capability: collaboration.

Internally, effective collaboration drives digital adoption, reduces costs, and breaks down organizational silos. Externally, it powers sustainability initiatives and fosters innovation. Without it, even the most sophisticated ESG strategies risk faltering. Poorly structured collaboration leads to critical data gaps, duplicated effort, and supplier fatigue. Today鈥檚 procurement executives must ensure their ESG and risk management approaches don鈥檛 overburden suppliers, or they risk disengagement precisely when long-term innovation is needed most.

Increased collaboration is consistently cited as the primary driver of accelerated ESG progress. Maintaining this momentum requires balance: bold action tempered by operational pragmatism, and clear expectations paired with tools that support supplier compliance and success.

Procurement鈥檚 moment of opportunity

Procurement is uniquely positioned to translate sustainability ambitions into measurable, scalable outcomes. By fostering collaboration across business units and with suppliers, it can shape the future of responsible business.

Even when ESG ambitions are pursued discreetly, the business case for action remains compelling鈥攂oth for organizational performance and broader societal benefit. Companies that maintain sustainability initiatives despite changing political priorities position themselves advantageously for the future while capturing immediate operational benefits. Procurement鈥檚 ability to embed sustainability considerations into core decisions consistently and collaboratively will define its impact in the years ahead.

solutions can integrate seamlessly with and to help businesses better track, manage, and reduce their environmental impact across the supply chain. These solutions can enable organizations to incorporate sustainability metrics into procurement decisions, improve transparency with trading partners, and help ensure compliance with evolving environmental regulations.

Many of the insights above were explored during the following Economist Impact webinar, 鈥淢easuring up: Balancing risks and goals for strategic procurement.鈥� To delve further into these topics and hear expert perspectives, . For those eager to continue the conversation and engage directly with leaders in the field, the upcoming 麻豆原创 Connect event鈥擮ctober 6-8 in Las Vegas, Nevada鈥攐ffers an excellent opportunity to join on-site and gain deeper insights into strategic procurement, ESG initiatives, and innovation. .

Baber Farooq is senior vice president and head of Market Strategy for 麻豆原创 Ariba.