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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

Get started .

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.

Get started .

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.

Get started .

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.

We believe the 2026 evaluation recognized 麻豆原创 for strengths in areas that are increasingly important to enterprise commerce: using AI to make commerce more intelligent, supporting complexity at global scale, and connecting commerce with operational data.

For 麻豆原创, these areas come to life in how we help customers simplify complex commerce and prepare for what comes next.

Making commerce more intelligent with AI

AI is changing how customers discover products, interact with businesses, and complete purchases. It is also creating opportunities to simplify work for commerce teams.

helps bring AI into customer experiences and commerce workflows while maintaining the business controls required for enterprise transactions.

Ericsson provides an example of what this can look like in practice. Generating a customer quote could once take more than two weeks. Today, after digitizing and streamlining its commerce processes with 麻豆原创 Commerce Cloud, many of those requests can be completed in minutes or even seconds. With that digital foundation in place, Ericsson is now exploring how AI agents could support quote creation while employees remain responsible for oversight and decision-making.

The goal is straightforward: use AI to reduce complexity, make commerce easier for customers, and give employees more time to focus on higher-value work.

Supporting complexity at enterprise scale

For global enterprises, commerce rarely means managing a single storefront or business model. Businesses may need to operate across multiple brands, markets, sites, languages, currencies, channels, and customer types while maintaining consistency and control.

麻豆原创 Commerce Cloud helps enterprises manage that complexity and provides the flexibility to evolve as their businesses grow.

Daimler Truck North America demonstrates the value of building that foundation for the long term. Roughly 30,000 users interact with its commerce platform every day, and DTNA reports approximately 40% compound annual growth over the lifetime of the platform. Those results have been built through years of investment in platform modernization, cloud migration, dealer collaboration, and alignment between business and IT.

As businesses expand and customer expectations evolve, having a commerce platform that can grow and adapt with the organization becomes increasingly important.

Connecting commerce with your operations

A great commerce experience depends on what happens behind the storefront.

Customers expect accurate products, prices, inventory, entitlements, and order information when they need it. Businesses need commerce to work with the operational processes and enterprise data behind every interaction.

麻豆原创 Commerce Cloud connects commerce with those operations, helping businesses deliver more consistent customer experiences while simplifying execution across the enterprise.

, noted in this year鈥檚 report as a recent platform release, extends this approach to mid-sized and growing businesses running 麻豆原创 Cloud ERP. Purpose-built for B2B companies looking to modernize customer engagement without adding complexity, it turns the pricing, availability, order, and invoice data already in their ERP into a direct self-service experience for customers, with faster deployment, lower total cost of ownership, and no middleware required.

That connection is also central to Autonomous Unified Commerce from 麻豆原创: connecting customer, commerce, operational, and financial processes on a common data foundation, while embedding AI into business workflows to help organizations execute more efficiently and drive profitable growth.

Looking ahead

AI is creating new ways for customers to discover products, get answers, and complete purchases through AI-powered interfaces and assistants beyond the traditional storefront. Commerce is evolving to support these new channels via the Universal Commerce Protocol (UCP) and Model Context Protocol (MCP).

The real work is getting AI into the daily operations that actually drive revenue, reduce cost, and help businesses serve their customers better. Our focus is to help customers navigate these changes by making complex commerce simpler, supporting growth at enterprise scale, and creating more connected and intelligent ways of working.

We are proud to mark 麻豆原创’s 12th recognition as a Leader and grateful to the customers, partners, and 麻豆原创 teams who continue to move commerce forward.

To learn more and explore the digital commerce platforms market, access a complimentary copy of the .

Kollen Glynn is global head of 麻豆原创 Commerce Cloud for 麻豆原创 Customer Experience at 麻豆原创.

Gartner, Magic Quadrant for Digital Commerce Platforms, Aditya Vasudevan, Penny Gillespie, Sandy Shen, Jason Daigler, Mike Lowndes, 19 October 2026.
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鈥檚 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 trademarks of Gartner, Inc. and/or its affiliates.
麻豆原创 was recognized as 麻豆原创 Hybris in 2014, 2016 and 2017.

It requires AI that can drive outcomes by executing and coordinating work across the customer journey and the business processes behind it, grounded in trusted data and governed at every step.

With the 麻豆原创 Customer Experience Q3 2026 release, 麻豆原创 is bringing the vision to life as part of the broader . New AI assistants and innovations across sales, service, marketing, and unified commerce help organizations coordinate customer-facing activities with broader business processes across the enterprise.

These capabilities are now available to select customers, providing an opportunity to explore Autonomous CX in action.

Explore the highlights of the Q3 2026 release below. For full sub-solution details, see our recaps for , , , , , and . Additional announcements from 麻豆原创 CX Connect are available here.

Autonomous sales and revenue: from leads to qualified opportunities

Pipeline generation remains one of the most important activities in any organization. Yet, lead qualification often gets bogged down in manual reviews, disconnected systems, and handoffs that slow sales teams down just when prospect interest is at its highest level. To help streamline this process, 麻豆原创 is introducing the , which automates lead qualification, enrichment, routing, and follow-up activities. The assistant leverages specialized agents to move leads efficiently through the qualification process.

Behind the scenes, the coordinates the journey from lead capture to qualified opportunity. The gathers prospect intelligence and company context and the evaluates fit and engagement signals to prioritize opportunities. The then manages the transition from qualified lead to sales opportunity.

Together, these capabilities create a more connected lead-to-opportunity process. Sales teams can then reduce administrative effort and improve lead quality while focusing more time on customer engagement and revenue generation.

Autonomous service: from case resolution to lasting customer relationships

The need for coordinated execution continues after the sale. When customers need help, they want it fast and without being transferred or having to explain the problem twice. The goal is to resolve cases quickly and get customers back to business faster.

The new helps accelerate the entire case life cycle from intake to resolution.

• automates case intake by categorizing requests, gathering context, generating summaries, and routing cases
• continues case execution after preparation, automates processing steps, and prepares customer communications
• evaluates case status continuously and identifies the most effective next step

The brings all these capabilities together into a unified workflow. With routine steps automated, service teams deliver fast and accurate service that keeps customers coming back. The same capabilities are also available for , extending agentic case handling to shared service functions such as HR and finance.

Autonomous marketing and engagement: from campaigns to customer engagement

Marketing teams are under immense pressure to deliver increasingly personalized experiences across more channels, audiences, and content and often with less budget. At the same time, they are expected to demonstrate measurable impact on business outcomes including revenue and growth鈥攏ot just marketing performance metrics such as opens, clicks, and engagement.

To address this complexity, 麻豆原创 is introducing the and . These assistants are designed to support campaign and content planning, execution, and optimization.

At the center is the , which helps translate customer insights and business priorities into coordinated engagement actions across the campaign lifecycle.

• helps marketers create and refine target audiences
• translates campaign goals into executable automation programs
• provides conversational access to performance data and surfaces optimization opportunities

For the Content Assistant, the helps generate tailored copy and creative variants, and coordinate messaging across channels while aligning execution to audience and campaign objectives.

With planning, execution, and performance working together, marketers have more room to focus on strategy, creativity, and the customer experience.

Unified commerce innovations

Autonomous CX depends on more than customer-facing intelligence. Understanding what a customer wants is only the first step. Delivering on that intent requires the operational systems behind the experience, including pricing, inventory, fulfillment, and financial processes, to coordinate and execute in real time. When customer demand is directly connected to business operations, organizations can consistently turn customer intent into promised outcomes.

New innovations across retail operations and ERP-connected commerce help organizations better align customer demand with business execution and deliver seamless experiences across channels.

The latest release of continues to strengthen retailers鈥� capabilities by expanding integration with . Retailers can apply centrally managed promotions consistently across digital and physical channels. New demand and inventory segmentation capabilities better align inventory and demand across retail, e-commerce, and wholesale channels, while operational enhancements improve day-to-day store execution.

With , B2B companies running 麻豆原创 Cloud ERP Public Edition can turn ERP data into a more seamless customer self-service experience. Native 麻豆原创-managed integration provides access to pricing, availability, orders, and invoices across all channels, while a preconfigured, clean core approach accelerates time to value and lowers total cost of ownership.

The next stage of customer experience

This is only the beginning. 麻豆原创 continues to invest in new AI assistants and specialized agents to help organizations work more efficiently, respond more intelligently, and deliver better customer outcomes.

As Autonomous CX evolves, customers can expect even more capabilities that extend automation, orchestration, and decision-making across the end-to-end customer journey.

Learn more about 麻豆原创 CX in Q3 2026鈥�&苍产蝉辫;

Read the 麻豆原创 Help documentation to get started with these new capabilities.

Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience and Consumer Industries at 麻豆原创.

You benchmark features, compare pricing, run a proof of concept, and choose the platform your sales team finds most intuitive. The ERP conversation happens in a different room, with different stakeholders, on a different cycle.

This separation feels logical on an org chart. In practice, it is one of the most costly fictions in enterprise technology planning.

The enterprise reality

doesn鈥檛 operate in a vacuum. It operates inside a business that already has a system of record, one that holds pricing rules, credit limits, inventory positions, order histories, contract terms, and fulfillment status. That system is the ERP. When CRM and ERP are evaluated and deployed independently, the downstream consequence is not just a technical integration challenge. It is a structural gap in the information available to every customer-facing employee, every day, in every interaction that matters.

When CRM and ERP are built on separate data models, integration becomes a permanent overhead. Sales reps without ERP data access spend an estimated 20 to 30% of their selling time on non-selling activities such as sourcing pricing approvals, checking inventory availability, or validating order status–work that should happen automatically in the background of a connected system. The sales motion slows. Forecast accuracy degrades. And the promised ROI of the CRM investment erodes quietly, deal by deal.

According to Gartner庐 Peer Insights™ responses to the question, “What were the key factors that drove your decision?”, based on the response of actual users, 49% of 麻豆原创 Sales Cloud users and 43% of 麻豆原创 Service Cloud users cited pre-existing relationships as a deciding factor in their purchase decision. This is more than double the market average of about 20%.

At first glance, this looks like vendor loyalty. We believe it is something more deliberate than that.

What native integration unlocks

Pre-existing relationships in enterprise software carry specific, quantifiable value. When an organization already runs 麻豆原创 S/4HANA, choosing 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud means working within a known data architecture rather than building a bridge between two foreign systems. The integration patterns are pre-established. The master data such as customers, products, pricing conditions etc is already structured in a way 麻豆原创 products understand natively. The implementation risk profile is fundamentally different from deploying a point-solution CRM and then spending months, and significant budget, building and maintaining connectors to the ERP back-end. This is not loyalty to a logo. It is a rational assessment of where integration risk lives.

The outcomes that follow from this architectural alignment are measurable. Customers running 麻豆原创 S/4HANA and together have achieved a 38% reduction in sales cycle duration, a 12% increase in opportunity win rate, and . These numbers are not the result of superior user experience alone. They are the result of sales processes that can draw on real-time ERP data like current inventory, live pricing, or credit exposure, without requiring the sales rep to leave the CRM, make a phone call, or wait for a nightly data sync. The integration is the mechanism of the outcome, not a supporting feature of it.

Many enterprises rely on disparate point solutions for marketing, sales, commerce, and service. Stitching these together with custom middleware and integrations is not just expensive, it’s fragile. Organizations have entire teams dedicated to building and maintaining integrations, manually transferring data between systems, and firefighting connectivity issues. The result is poor customer experiences, delayed insights, and limited scalability.

According to an , organizations that natively connect 麻豆原创 CX solutions with 麻豆原创 ERP unlock five compelling economic benefits:

1. Native iFlows and prebuilt connectors reduce integration and middleware costs by 25 to 80%, delivering six- to seven-figure savings over five years.
2. Seamless front-to-back connectivity drives revenue growth through higher customer registrations, improved retention, better sales conversion rates, and larger deal sizes; projecting a 30 to 441% revenue uplift over five years.
3. AI-driven automation and elimination of manual handoffs boost productivity by 25 to 90% across every stage of the sales cycle, from marketing to service.
4. Organizations go live and begin realizing customer experience improvements in as few as six months, significantly faster than point-solution alternatives.
5. Built-in compliance support reduces regulatory risk without custom development.

Together, these benefits deliver a validated five-year ROI of 214% to 726%.

Validation from our customers

This director of Customer Service and Support in the consumer goods industry highlights Integration and reliability strengths of 麻豆原创 Sales Cloud:

This marketing manager in the retail industry highlights the seamless integration enabling customer service teams to enhance customer experience:

Suite advantage is a strategic choice, not a convenience

麻豆原创 continues to invest in tighter, event-driven integration between these platforms, moving away from batch replication toward real-time, API-first connectivity. The adoption of 麻豆原创 Integration Suite pre-built integration packages, combined with RISE with 麻豆原创 and 麻豆原创 GROW bundling, is lowering the barrier for customers to activate these scenarios out of the box. For organizations on the path to an intelligent enterprise, the integration between 麻豆原创 S/4 HANA and 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud integration is no longer a project, it is a product.

CRM value is not created in isolation, it is created at the intersection of customer data and operational data, of sales motion and business process, of pipeline intelligence and ERP reality. For organizations already running 麻豆原创, that intersection is not something to build. It is something to activate.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.
GARTNER and PEER INSIGHTS are trademarks and service marks of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.

Ericsson knows this challenge well.

As a global leader in telecommunications technology operating in more than 190 countries, Ericsson manages highly complex commercial relationships involving products, services, customer-specific contracts, pricing agreements, and global fulfillment operations. Behind every quote is a web of business rules, approvals, and dependencies that must be coordinated accurately and efficiently.

For years, that complexity created friction in the quoting process. In some cases, generating a quote could take more than two weeks. Today, many of those same requests can be completed in minutes or even seconds.

The hidden complexity behind every quote

At Ericsson, many customer transactions are tied to multi-year framework agreements that include negotiated pricing, contract-specific products, and customer entitlements. Sometimes customers return months or even years after an agreement is signed and need to understand what products, configurations, and pricing are available to them before placing an order.

Historically, that process often required customers to contact their account representative, who would manually gather information, validate contract terms, and prepare a quote. Depending on the complexity of the request, responses could take days or even weeks.

Ericsson’s objective was straightforward: make it easier for customers to do business with the company.

“We want to be easier to work with as a partner to our customers,” said Aaron Thomas, global product owner at Ericsson.

Using , Ericsson introduced digital commerce capabilities that allow customers to access contract-specific products, generate quotes, and place orders through self-service channels. Customers can now access dedicated storefronts built around their agreements, browse available products, generate quotes, and complete transactions without relying on manual intervention from account teams.

What once took days or weeks can now be completed in minutes or even seconds, helping improve accuracy and reduce rework across commercial operations.

Beyond automation: Exploring the role of AI agents

Once the quoting process became digitized and streamlined, Ericsson began exploring how AI could take commercial execution to the next level.

“We’ve moved from manual processes to digitized and automated processes,” Thomas explained. “Now we’re asking: can an agent perform those tasks?”

Rather than using AI solely as a productivity tool, the company started evaluating how AI agents could actively participate in commercial processes.

One area Ericsson is exploring is quote creation. The company is evaluating how AI agents could analyze customer requests, identify relevant contract information, assemble recommendations, and prepare proposed quotes for human review.

Employees remain responsible for oversight and decision-making, while AI helps reduce administrative work and allows sales and account teams to spend more time advising customers, solving business challenges, and strengthening relationships.

The company is also exploring other AI-driven capabilities, including agents that can improve product descriptions and search experiences across its internal product portfolio, helping employees find information more quickly across a large and complex catalog.

Defining the next chapter of B2B commerce

For Ericsson, AI is becoming more than a productivity tool. It is creating new possibilities for how commercial work gets done.

Reducing quote creation from weeks to seconds delivered immediate value for customers and employees alike. But the company’s exploration of AI agents points to a broader shift, one where intelligent systems help businesses execute complex processes faster, more consistently, and at greater scale.

For Ericsson, the journey from weeks-long quoting cycles to AI-assisted execution is about more than efficiency. It is about creating a simpler experience for customers and helping employees focus on the work that matters most. As the company continues exploring how agents can support commercial operations, it is helping redefine how complex B2B processes can be executed in the age of AI.

To learn more about Ericsson’s approach to AI and the future of agentic commerce, watch the webinar: . You can also explore 麻豆原创’s vision for unified commerce at and learn how organizations are connecting commerce, customer experiences, and operational execution to drive better business outcomes.

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� 

These announcements build on the Autonomous Customer Experience strategy we introduced at 麻豆原创 Sapphire. That premise is straightforward: AI must understand customer intent, make context-aware decisions, and execute within business guardrails. Following through on a customer commitment requires AI grounded in real-time, enterprise-wide business data, with a deep understanding of the processes needed to orchestrate work from intent to outcome.

A quote needs accurate pricing and terms. An order depends on inventory, credit, payment, and fulfillment. A service interaction requires information about the customer, the product, and the commitments the company has already made.

Customers generally do not see this operational complexity. They experience whether the company followed through.

Joule Assistants move from providing answers to driving business outcomes

Many AI tools can reason about what should happen next and recommend actions. But executing complex enterprise processes requires more than that.

AI needs to understand the live state of the business, including customer commitments, products, pricing, agreements, orders, payments, policies, and operational constraints. It must then navigate the approvals, exceptions, and cross-application steps required to act. Without that grounding, proposed actions may appear reasonable but can be inappropriate, infeasible, or break down in execution.

This is the direction behind the latest Joule Assistant innovations. New and enhanced combine role-specific intelligence with business and process context across marketing, commerce, sales, service, finance, operations, and compensation, helping users move from understanding what should happen to getting the work done.

The innovations apply this approach to moments where fragmented context, manual steps, and handoffs can slow the customer experience.

• Campaign Assistant and Content Assistant help teams move from campaign ideas to execution faster by reducing the effort required to plan, create, refine, and activate marketing campaigns.
• Shopping Assistant makes product discovery easier and more relevant to what the customer is trying to accomplish.
• Quoting Assistant brings customer, pricing, and commercial context together so sellers can prepare quotes faster and spend less time assembling information across systems.
• Service Planning and Operations Assistant translates service demand into coordinated plans, reducing the manual work required to align people, resources, and customer commitments.
• Incentive Compensation Assistant and Recurring Receivables Assistant bring the same intelligence to critical processes behind the customer experience, helping resolve questions, reduce administrative work, and keep compensation and payment workflows moving.

Together, the new assistants announced at 麻豆原创 Connect extend the approach introduced at 麻豆原创 Sapphire. Sales Assistant brings engagement insights, pipeline signals, and deal context together so sellers can prioritize the right opportunities, prepare for every interaction, and spend more time building relationships and moving deals forward. Deal Closing Assistant unifies customer, product, pricing, and fulfillment intelligence to help teams build optimal quotes, surface relevant products with pricing and availability insights, and shorten the path from negotiation to confirmed orders.

The goal is not to add another conversational interface. It is to move work forward by bringing together the business context, process knowledge, and controls required to act.

Human judgment remains important where decisions require approval, oversight, or exception handling. Within defined guardrails, AI can assemble context, determine and execute appropriate actions, and escalate when human intervention is needed.

麻豆原创 Order Management Services coordinates execution across ERP landscapes

Order management becomes more difficult when an order crosses ERP systems, legal entities, plants, distribution centers, and business units.

Each transition can introduce different information about pricing, inventory, credit, fulfillment status, or ownership of the next action. Consolidating every ERP system before improving order execution is not always a practical starting point.

麻豆原创 is expanding to provide highly scalable, resilient, real-time omnichannel order orchestration across 麻豆原创 S/4HANA and other ERP systems.

Built on a cloud-native, event-driven architecture, 麻豆原创 Order Management Services coordinates order execution across channels, systems, business units, and third-party partners without requiring organizations to consolidate their ERP landscapes first.

The expanded capabilities provide a common layer for validating orders, checking inventory across locations, coordinating transactions between business units, and monitoring progress through fulfillment.

This gives organizations greater flexibility to add, remove, and experiment across channels, while enabling the order management layer to detect disruptions, trigger exception workflows, and keep execution aligned with the original customer commitment.

In complex enterprise landscapes, this combination of scale, flexibility, and reliable integration helps businesses deliver consistent omnichannel experiences while improving operational efficiency.

Complete business context across the customer journey

Customer intent enters the business long before an order reaches a fulfillment system. It begins when a customer discovers a product, engages with a campaign, requests a quote, or starts a purchase journey.

To deliver on that intent, customer-facing interactions must remain connected to the pricing, inventory, ordering, fulfillment, and financial systems that support them.

麻豆原创 Commerce Cloud, cloud ERP edition, is now generally available. First introduced at 麻豆原创 Sapphire, the edition allows midsize and growing companies to extend 麻豆原创 Cloud ERP with commerce capabilities that use ERP data for pricing, availability, orders, invoices, and other transactional processes.

For more than a decade, 麻豆原创 Commerce Cloud has supported some of the world’s largest and most complex digital commerce environments. With 麻豆原创 Commerce Cloud, cloud ERP edition, we are extending that approach to companies that want a more standardized, 麻豆原创-managed path to ERP-connected commerce.

By using 麻豆原创 Cloud ERP as the system of record, organizations can connect digital commerce with pricing, ordering, fulfillment, and financial processes without building and maintaining those connections independently. The result is a faster path to value, lower operational complexity, and an ERP-aligned commerce model designed to scale as the business grows.

The same principle applies as customer interactions move across teams. A new native connection between 麻豆原创 Engagement Cloud, enterprise edition, and 麻豆原创 Sales Cloud synchronizes contact and company information while bringing relevant engagement signals directly into sales workflows. Instead of reconstructing customer context, sellers can continue conversations with a clearer understanding of prior engagement.

Customer experience depends on execution

AI can understand customer intent, make context-aware decisions, and act within defined guardrails. The harder question is whether the business can carry that action across the systems, teams, and processes responsible for delivery.

The innovations announced at 麻豆原创 Connect address different parts of that challenge. Joule Assistants bring business and process context into the flow of work and help execute the next step. 麻豆原创 Order Management Services orchestrates orders across complex ERP landscapes. 麻豆原创 Commerce Cloud, cloud ERP edition, connects digital commerce directly with ERP pricing, availability, ordering, fulfillment, and financial processes.

Together, they advance a common principle: Autonomous Customer Experience depends on connecting customer-facing intelligence with the operational context and processes required to act.

Customers ultimately do not experience recommendations, models, or workflows. They experience whether the business followed through and delivered.

As AI begins to act on behalf of the business, the quality of that follow-through will depend on the business context, process knowledge, and operational reality behind every action.

Additional information on the 麻豆原创 Connect announcements, including availability details and product-specific innovations, is available in the . You can also watch the to see these innovations in action.

Balaji Balasubramanian is president and chief product officer of 麻豆原创 CX.

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.

Visit the . Get 麻豆原创 news via  and .

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’s commitment to helping enterprises deliver intelligent, personalized customer experiences, enhanced by AI, built on trusted data and connected end-to-end business processes, and grounded in real-life business context. Joule Agents, Joule Assistants, , and together provide the agentic marketing backbone to advance this vision of the Autonomous Enterprise.

AI is evolving from assisting marketers to executing and acting on their behalf, creating a fundamental opportunity for organizations to completely rethink how they engage customers. Agentic marketing enables AI agents and assistants to understand context, take action, and help brands deliver more relevant personalized experiences.

Trust is the foundation to this shift. As AI takes on greater responsibility, organizations need solutions built with security, compliance, governance, and trusted business data so customers can confidently rely on AI-driven interactions and decisions.

Why agentic marketing matters

Consumers are becoming more comfortable using AI, reshaping their expectations of the brands they love and the organizations they interact with. 麻豆原创’s indicates that 33% of consumers now trust AI assistants to complete purchases on their behalf, while 31% say interactions with AI assistants make them more loyal to a brand. The trend is even more pronounced across younger consumers. Across generations, we see AI playing an influential role in purchasing decisions and long-term customer loyalty. For example, a consumer might ask an AI assistant to reorder a household product, book a trip, or recommend a new product based on past preferences. For those interactions to succeed, customers must trust that the AI is acting on accurate information, protecting their data, and making decisions within appropriate guardrails.

Organizations from manufacturers to fashion brands are seasoned in using generative AI to create content, build audiences, and improve marketing efficiency. Attention is turning to agentic marketing: moving from AI that recommends to AI that acts.

Marketing context meets business reality

Most marketing platforms understand customer interactions well: clicks, opens, purchases, and browsing behavior. Those signals matter, but they only tell part of the story. The most valuable signals often live elsewhere across the enterprise in inventory availability, order status, fulfillment performance, product profitability, return behavior, and subscription and replenishment cycles.

Marketing often sees customer behavior, while ERP systems see business reality. The greatest opportunity for AI emerges when those worlds come together. Because 麻豆原创 sits at the heart of how many of the world’s leading enterprises operate, marketers can access the operational intelligence that lives within the systems running the business, enabling AI to make decisions based not only on what customers are doing, but on what the business can actually deliver. In an age of agentic marketing, that distinction matters.

Why 麻豆原创 was recognized as a Leader

麻豆原创 was recognized for the following strengths by the IDC MarketScape:

• With the agentic layer of Joule, marketers get real-time, context-aware personalization without having to reconcile 麻豆原创 and non-麻豆原创 data themselves, since Joule Agents and Joule Assistants draw on a harmonized 麻豆原创 Business Data Cloud foundation. Teams receive role-based guidance for navigating complex workflows across Autonomous Domains, while everyday tasks such as retrieving information, connecting enterprise data, and completing multi-step processes happen independently, freeing staff from manual execution. In 麻豆原创 Engagement Cloud, this means personalization and segmentation scale on their own, and the 4Q25 CX release lets customer service issues resolve autonomously, backed by 麻豆原创’s enterprise data layer and 麻豆原创 Knowledge Graph, instead of requiring escalation.
• Because Joule Studio is low code, organizations with reasonable levels of in-house development capabilities can build custom agents themselves, without waiting on IT or writing code, while still connecting to Joule, the 麻豆原创 Customer Experience portfolio, and third-party systems grounded in 麻豆原创 Knowledge Graph and 麻豆原创 Business Data Cloud. For sales and marketing teams, this means faster deal cycles and higher conversion: a sales-assistant agent, for example, automatically surfaces buying patterns and product recommendations from purchase history, sparing reps the manual analysis.
• Marketers running campaigns across email, digital ads, wallet, conversational, and web can manage them all from a single platform instead of stitching together point solutions. Because the engagement events service connects external data sources into real-time segmentation, campaigns stay relevant as customer behavior shifts. AI-assisted tools such as natural-language product search, AI-generated consumption summaries, and an AI-assisted report builder cut down the manual work of building segments and reporting, enabling marketers to focus on strategy while AI handles recommendations, translation, prediction, and reporting at scale.

AI that drives business results

Organizations around the world are using 麻豆原创’s AI-powered customer engagement capabilities to create more connected experiences and drive business results. For example:

• uses an AI-powered data foundation to boost fan subscriptions by 50% and improve pre-game fan engagement by 80%.
• Molton Brown combines AI-enhanced commerce and personalization to create seamless customer experiences that strengthen loyalty while maintaining 100% uptime during peak periods.
• connects AI-driven shopping experiences, such as virtual beauty tools, and personalized product recommendations to deliver engaging and elevated customer experiences.

Advancing toward the Autonomous Enterprise

Being named a Leader in the IDC MarketScape: Worldwide Agentic Marketing for B2C Enterprises 2026 Vendor Assessment is an important milestone for 麻豆原创 and the evolution of agentic marketing as a category.  As organizations move from experimenting with AI to deploying agents that can reason, recommend, and act, we believe this recognition validates 麻豆原创’s approach of combining AI, trusted business data, and connected business processes to help customers navigate this next phase of transformation.

The vision of the Autonomous Enterprise is one in which AI-powered agents help organizations connect customer, operational, and business data to make and execute decisions with greater speed, relevance, and scale. In this future, AI doesn’t replace human expertise; it amplifies it. Intelligent assistants and agents can help teams act faster and more effectively, with governance and guardrails that keep people responsible for the strategy, creativity, and judgment that build lasting customer relationships.

Ultimately, the success of agentic AI will depend not only on what it can do, but on how much customers and businesses trust it to act on their behalf.

Learn more

Download the .

Sara Richter is chief marketing officer for 麻豆原创 Engagement Cloud at 麻豆原创.

Well-run RFPs get right to the heart of requirements, match enterprises with the right solutions, and build solid plans to launch those solutions. Disorganized RFPs significantly lengthen the decision-making process without adding the intended value. 

So what separates an effective RFP from the rest?  

Let鈥檚 delve into a systems integrator鈥檚 perspective of what can go right, what can go wrong, and what purposes the whole process can actually serve. 

Throughout this article, 鈥淩FP鈥� means the document a business creates to solicit responses from vendors. 鈥淩FP response鈥� means the reply to an RFP from a vendor.

Why do organizations still run RFPs? 

According to Michael Andrion, account executive and partnerships for BASE1, 鈥淭here are really three different reasons for running an RFP: to select a vendor, to describe the problem that needs to be solved, and to end up with a well-scoped plan for solving it.鈥�  

Let鈥檚 break that down in a bit more detail:  

• Select a vendor: You鈥檇 be surprised at how many RFPs end without a vendor decision being made. An RFP may fail to result in a decision for multiple reasons, including an unexpected business acquisition, a change in budget, timeline issues, and more. Thorough preparation and buy-in from stakeholders can help ensure a successful outcome.  
• Describe the problem that needs to be solved: An RFP can be a highly effective tool for internal interrogation, so you can clarify what problems stakeholders truly want to address.  
• Build a well-scoped plan for solving it: When done well, the RFP process will give you answers to questions about timelines, pricing, and staffing. 

RFPs can be driven by compliance requirements or internal governance, but can also act as useful early discovery activities. Organizations use RFPs as a sort of first gate, or first hurdle, to see which vendors are worth having deeper discussions with.  

What makes a good RFP, and what makes a bad one? 

What often separates a good RFP from a bad one is finding the right level of detail.  

If the RFP questions are completely generic, the business might not learn what it actually needs to know. Simultaneously, too many questions can easily result in counter-productive situations where RFPs written by AI receive responses written by AI, which are then summarized by AI.  

You need well-crafted questions that are unique to your business. That way vendors can supply responses that your experts will actually read, helping them reach the right decision for your business.  

How can an RFP achieve a level of open communication? 

Open communication between a business and prospective vendors creates better outcomes. 

However, there鈥檚 a challenge that comes with finding the right level of detail: highly detailed, nuanced questions often invite exactly the kind of deeper, looser discussion that a formalized RFP process precludes. To whatever degree possible (and that degree varies for good reasons, especially for regulated industries), think about whether the formality of the process is preventing the kind of honest dialogue that ultimately leads to better decisions. 

Two tips when it comes to RFPs:  

1. Ask yourself and your stakeholders whether you really need to go through the complete RFP process or whether 1:1 meetings and demos may be the right route.  
2. If you do go through the RFP process, plan on doing a follow-up question and answer session afterward, and/or down-select to vendors from which you鈥檇 like a more personalized demonstration.   

What does a business need to know before starting an RFP process? 

RFPs run more smoothly when the issuing company has already done some data gathering.  

Come to the table with: 

• Key metrics 
• Expectations around SLAs 
• A ballpark sense of budget 

With this information, you鈥檒l be able to qualify (and disqualify) the right candidates more efficiently and effectively.  

Before starting an RFP, be sure you have time to run one. It鈥檚 a time-consuming process, so start with your preferred go-live date and work backwards from there. Here鈥檚 a general timeline from the :  

Do all RFPs have to follow the traditional format? 

In the past couple of years, there鈥檚 been an interesting trend in the field: namely reversing or inverting the traditional RFP process. Rather than beginning with a detailed spreadsheet of requirements before moving to a demo phase, organizations first hold short demonstrations, introductory meetings, and high-level pricing discussions.  

This demo-first approach allows both sides to evaluate cultural fit, communication style, technical dealbreakers, and business understanding before investing significant time in detailed documentation. 

What distinguishes a strong vendor? 

After the RFP response comes the evaluation. The right vendor isn鈥檛 just the one who ticks all the functional and technical boxes and the lowest price tag, important as those factors are.  

The right vendor is willing to become a student of your business and dig into a day in the life of your stakeholders. A well-structured RFP can show you which vendors are willing to do exactly that and to step outside of their comfort zones when and where you鈥檒l need them to. 

Ask yourself if the vendor just went through the motions, or if they actually understand your unique business needs.

What distinguishes a strong implementation partner? 

The strongest implementation partners demonstrate a clear understanding of the customer’s business before tying those requirements to any specific technology.  

Here are the green flags to look for in a strong implementation partner. The partner should:

• Arrive prepared 
• Ask thoughtful questions 
• Share relevant customer stories 
• Focus on being true partners rather than rote order-takers 

If you鈥檙e involving partners in your RFP process, challenge them to do some real analysis. Challenging questions are a good indicator that the business is truly invested in making the implementation a success. 

Final takeaway: it never hurts to ask a question! 

At its best, an enterprise CX RFP is an opportunity to improve communication, surface assumptions, encourage honest dialogue, and make better-informed decisions before implementation begins. Enter the process with eyes open and schedule planned, gather your key data and metrics before kicking off, and build a core set of meaningful questions. 

Have deeper discussions as soon as they鈥檙e relevant. Don鈥檛 be afraid to ask for numbers. And to whatever degree you鈥檙e able, shape the process to your organization鈥檚 specific priorities. Asking the right questions goes a long way toward getting the best possible answers. 

For even more insights, , and for more RFP process best practices and sample questions, .  

is partnerships and alliance manager for 麻豆原创 Engagement Cloud.
is head of Sales at BASE1.

What makes G2 recognition uniquely meaningful? Every ranking is driven entirely by verified customer reviews; there鈥檚 no analyst scoring and no vendor submissions. When 麻豆原创 CX earns a Leader badge, it’s because our customers said so.

鈥淓arning a Leader position in a G2 Report is highly competitive and rooted in verified customer reviews,鈥� Godard Abel, co-founder and CEO, G2, said. 鈥淐ongratulations to 麻豆原创 Customer Experience for achieving this distinction. Buyers can be confident this ranking reflects the authentic experiences of real users.鈥�

Shining stars: the standout achievements

delivered one of the strongest performances this season. It holds the #1 position in the Enterprise segment for Order Management and #2 Overall. 麻豆原创 Commerce Cloud also made impressive gains in two other critical categories: climbing from #3 to #2 in the Enterprise E-Commerce Platforms segment and from #3 to #2 Overall in Omnichannel Commerce. The trajectory is clear: 麻豆原创 Commerce Cloud is accelerating.

reinforced its standing as a powerhouse in enterprise sales technology, ranking #3 among 54 players in the CRM Enterprise market and #4 among 35 global players in Enterprise Sales Analytics. In one of software’s most fiercely competitive arenas, these rankings are a compelling statement of trust and performance.

earned its place as a Leader in the Global Enterprise Help Desk Grid, a recognition that reflects the consistent, high-quality service experiences our customers deliver to their own customers, every single day.

holds strong to its continued leadership position in both email deliverability and personalization engines. These both prove the solution鈥檚 ability to support the most dynamic and high-volume enterprise senders this upcoming holiday season.

What’s next: AI at the heart of 麻豆原创 CX

Being recognized today is one thing. Building for what’s next is another. is pushing the boundaries of AI-driven customer experience, with Q2 2026 delivering transformative innovations across sales, service, marketing, and commerce.

In 麻豆原创 Sales Cloud, the new agentic opportunity summary helps transform how sales teams manage their pipeline, aggregating engagement signals and activity data into a real-time deal view that can surface risks early and sharpen prioritization. Complementing this, 麻豆原创 Incentive Management now leverages AI-supported recommendations to help sales leaders optimize compensation plans and unlock performance insights that were previously buried in data.

In 麻豆原创 Service Cloud, the AI-first email editor equips agents with intelligent tools to help compose and manage customer interactions with greater speed and precision. Agents can now also create sales leads and opportunities directly from the service workspace, helping to turn every support interaction into a potential revenue moment.

In 麻豆原创 Engagement Cloud, the new AI-assisted content composer can generate and adapt images and copy directly within block-based emails using natural language prompts. Outputs stay grounded in the context of your audience and related products or campaigns for more accurate and engaging results. Teams can move from idea to campaign in minutes without leaving their workflows.

In 麻豆原创 Commerce Cloud, the standout innovation is conversational AI shopping via Model Context Protocol (MCP) integration. This enables AI agents to query live product catalogs, provide inventory updates, manage carts, and complete transactions, all within natural chat or voice interfaces.

Together, these capabilities mark a decisive shift, from AI as a passive insight tool to AI as an active participant in every customer interaction.

The voice of the customer

G2 rankings are earned, not assigned. Every badge is the result of real users sharing their honest experience. Here鈥檚 what some of our customers have to say about our products:

麻豆原创 Commerce Cloud
鈥淚 also appreciate the AI-driven intelligence features such as personalized recommendations, smarter search, predictive insights, and automated merchandising optimization, which significantly enhance customer experience and business efficiency.鈥� 鈥� Vedant G, Manager EWS |

鈥淲hat sets 麻豆原创 Commerce Cloud apart is its unmatched stability and depth when handling complex, enterprise-grade operations. While lighter platforms might look sleeker out of the box, 麻豆原创 Commerce Cloud shines where stakes are high and business models are complicated.鈥� 鈥� Hitesh Mistry, Accounting Specialist Vodafone |

麻豆原创 Sales Cloud
鈥淥ut of all the CRMs I have worked with, 麻豆原创 has proven to be the most mature of all of them. As being a customer service representative you often need access to records that other teams handle, 麻豆原创 eases my access to contact records, sales proposals and quotes, account creation requests, escalations, and regular CSR tickets.鈥� 鈥� Hitesh Mistry, Senior Customer Service Rep |

鈥淚 find it most useful for providing real-time visibility into the sales pipeline. I also appreciate the strong forecasting capabilities, as they give us a better sense of expected revenue.鈥� 鈥� Verified User, Enterprise |

麻豆原创 Service Cloud
鈥淚 like how 麻豆原创 Service Cloud is becoming more AI-driven, especially with features like automatic case classification, sentiment analysis, and AI recommendations that reduce manual effort.鈥� 鈥� Carl N, CSIS Web Design Instructor |

鈥淚 use 麻豆原创 Service Cloud for managing my customer support team, solving case management issues, automating customer responses, and reporting and analyzing team performance. It’s fast and easy to manage, saving me time.鈥� 鈥� Olatunji O, Support Associate, Hospital & Healthcare |

麻豆原创 Engagement Cloud
鈥淚 like the stock tracking dashboards and direct revenue attribution, which help me a lot to understand performance. Additionally, the predefined campaigns aimed at the online channel make our work easier, as we only need to develop them according to our business, which ultimately benefits internal developments. The focus on automations that we are implementing internally is also a positive point for us.鈥� 鈥� Food & Beverage Enterprise User |

As one of North America’s largest commercial vehicle manufacturers, DTNA supports a vast ecosystem of dealers, fleets, service providers, and customers. Over the last decade, the company has steadily transformed its digital commerce capabilities, creating a foundation that has enabled growth, improved , and positioned the business for the next generation of innovation.

Building a platform for growth

DTNA’s digital commerce transformation wasn’t driven by a single project or technology investment. It was the result of a long-term, strategic commitment to improving how dealers and customers interact with the business.

The company’s journey began with a basic digital parts-ordering platform to help customers and dealers purchase parts online. While the experience was relatively simple, it helped DTNA establish digital adoption, connect key systems, and build relationships with its dealer ecosystem. Most importantly, it created the foundation for what came next.

As customer expectations evolved, DTNA recognized the need for a more modern and scalable commerce experience. The company invested in the 麻豆原创 Commerce solution, expanded digital capabilities, entered new markets, migrated to , and continuously enhanced the platform over time.

Rather than treating commerce as a one-time project, DTNA embraced a mindset of continuous improvement.

Transformation is about people as much as technology

Technology may enable transformation, but adoption determines whether transformation succeeds.

For DTNA, one of the biggest challenges wasn’t implementing new capabilities. It was helping a large network of dealers and customers embrace new ways of working.

“We spent those years pursuing adoption of the tool, really educating our dealer body and getting their buy-in to start using the tool and introducing it to their customers,” said Brenda King, IT manager for eCommerce and Catalog at DTNA.

That approach remains a cornerstone of DTNA’s strategy today. The company works closely with dealers, gathers regular feedback, and maintains strong collaboration between business and IT teams. According to King, that alignment has been critical to ensuring digital investments translate into business value.

The partnership extends well beyond project delivery. King emphasized the importance of working closely with business stakeholders to identify priorities, evaluate opportunities, and ensure technology investments align with business objectives. Rather than operating in silos, business and IT teams work together to shape priorities, guide investments, and continuously improve the customer experience.

Preparing for what’s next

Today, DTNA is exploring how AI can improve commerce experiences through capabilities like product recommendations, customer assistance, and guided buying experiences. But the company’s approach remains grounded in business value.

“We really look at how AI can help us achieve our business goals,” King said. “It’s not AI for the sake of AI.”

That perspective aligns with a broader trend highlighted in the . As organizations accelerate AI investments, many are discovering that successful innovation depends on strong foundations, clear business objectives, and the ability to connect technology investments to measurable outcomes.

The road ahead: Success built on a strong foundation

The company鈥檚 digital commerce business has achieved approximately 40% compound annual growth over the lifetime of the platform, while digital adoption and customer engagement continue to increase. Today, roughly 30,000 users interact with the platform every day.

Those results were not driven by a single initiative. They were built on years of investment in platform modernization, cloud migration, dealer collaboration, and close alignment between business and IT teams. These foundational investments created the flexibility needed to continue growing while preparing for future innovation.

“Our decision to move to 麻豆原创 Commerce Cloud was critical for us to continue growing,” said King. “It stabilized our infrastructure, gave us access to new capabilities, and created the flexibility we needed to keep evolving.”

DTNA’s experience offers an important reminder for organizations navigating their own transformation journeys: long-term success comes from combining innovation with the right foundation.

To learn more about DTNA’s transformation journey and how the company is preparing for the next phase of AI innovation, watch the webinar

For commerce teams, delivering that experience is anything but simple. Behind every purchase sit catalogs, promotions, customer accounts, inventory, payments, orders, and fulfillment. A seemingly straightforward storefront change can quickly become part of a much larger release.

麻豆原创 Commerce Cloud and Vercel are working together to give teams a more flexible way forward. continues to manage the commerce data and processes behind the transaction. Vercel runs the customer-facing experience and gives teams the infrastructure and workflow to build, review, release, and operate it.

The tools to transform customer experience are here, but tools alone don鈥檛 win. You must consider operating models too. Our partnership with 麻豆原创 Commerce Cloud pairs Vercel鈥檚 web stack鈥攊ncluding world-class performance, faster iteration, and scale that holds up under peak demand鈥攚ith 麻豆原创鈥檚 trusted data and processes, and governance built in from the start.

Jeanne DeWitt Grosser, Chief Operating Officer, Vercel

The operating model is straightforward. Teams can change the storefront without having to change everything behind it at the same time.

A faster starting point for cutting-edge storefronts

Consider a commerce team preparing to enter a new market. It needs a localized storefront, a different customer journey, and a campaign built for that audience. In a tightly connected architecture, those changes can become dependent on a broader release involving pricing, inventory, orders, payments, and fulfillment.

Separating the storefront gives the team more freedom to work. It can design and release the experience for that market while 麻豆原创 Commerce Cloud continues to provide consistent product data, prices, availability, customer information, and order processes.

Vercel is developing Next.js storefront templates for 麻豆原创 Commerce Cloud to help teams get started. The templates connect to core capabilities such as product discovery, content, cart, checkout, and customer journeys.

The templates are backed by Vercel global delivery, managed scaling, deployment workflow, and observability, which improve engineering velocity and faster performance yielding more conversions.聽

As a result, frontend teams gain room to move, while commerce teams retain control of the rules that protect revenue and customer commitments.

What this changes for commerce teams

Campaigns and customer expectations move quickly. Vercel creates a preview deployment for each change, giving developers, designers, marketers, and business teams a working version to review before it reaches production. Teams can test the experience against 麻豆原创 Commerce Cloud services, gather feedback, and release approved storefront changes with fewer dependencies on a larger backend release.

The same approach helps organizations manage different brands, regions, languages, and buying models. A consumer placing a quick order has different expectations from a business buyer working with negotiated prices, an account-specific catalog, or complex purchasing rules. Teams can build a distinct experience for each audience with 麻豆原创 Commerce Cloud powering the operations behind it.

In addition, Vercel鈥檚 global network, edge routing, and caching bring storefront content closer to customers. Its managed infrastructure is built to scale with demand, including the traffic associated with major campaigns and peak shopping periods. Built-in observability gives teams visibility into traffic, errors, latency, and calls to external services, helping them identify problems that could affect the shopping experience.

Making AI impactful in commerce

AI-assisted development can dramatically accelerate the path from idea to experience. But speed without trusted context can simply produce more low-value experiences, faster and at greater cost.

Connected to 麻豆原创 Commerce Cloud, AI experiences can draw on trusted commerce data and processes. This gives teams a stronger foundation for building impactful customer journeys that are accurate, brand-aligned, and connected to how the business actually operates.

Leveraging Vercel鈥檚 AI SDK, development teams get a common toolkit for building great commerce applications using the AI model provider of your choice.

For teams starting with an idea for a new interface, Vercel鈥檚 v0 offering can help marketers and developers design, iterate, and turn that idea into an experience they can review and refine.

The right storefront strategy depends on the business

There is no single storefront approach that fits every commerce operation.

麻豆原创 Commerce Cloud, composable storefront, is available for organizations that want a closely integrated, 麻豆原创-managed experience. Vercel provides the new 麻豆原创 templates on the Vercel Frontend Cloud for teams building highly differentiated experiences on their own cadence using storefront technology used by millions of developers.聽 Both options are backed by 麻豆原创 Commerce Cloud with market-leading commerce capabilities to drive profitability for growing companies and the world鈥檚 largest enterprises.

Your customers expect storefronts to be fast and easy to use. They also expect accurate prices, reliable availability, and an order that arrives as promised. 麻豆原创 Commerce Cloud and Vercel bring those two sides of commerce together: an experience that can keep changing, backed by the data and processes that keep the business running.

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To earn a TrustRadius Top Rated badge, a product must meet three stringent criteria:

1. At least 10 verified reviews submitted within the past year
2. A trScore of 7.5 or higher
3. A meaningful presence in its category

Every submission passes through TrustRadius鈥� fraud-protection verification process. That means these three wins are a direct reflection of what 麻豆原创 customers experience every day, and they said it loudly and clearly.

What customers are saying

Across sales, service, and commerce, 麻豆原创 customers consistently highlighted the depth of integration, reliability at enterprise scale, and the tangible business impact of AI-powered workflows. These aren鈥檛 features on a road map; they鈥檙e capabilities customers are using today, and the reviews prove it.

麻豆原创 Sales Cloud

鈥淲e had a comparison between 麻豆原创 Sales Cloud, Microsoft Dynamics, and Sales Force Optimization. We selected 麻豆原创 Sales Cloud for three main reasons: 麻豆原创 Sales Cloud meets our requirements the best, especially in comparison with Sales Force; pricing is more attractive; and as we use multiple 麻豆原创 products, the integration between different 麻豆原创 products works well.鈥� 鈥� Verified Reviewer

鈥溌槎乖� Sales Cloud is well suited for predictive analysis and strong integration with ERP, 麻豆原创 S/4HANA. AI-driven recommendations for next best actions.鈥�

麻豆原创 Service Cloud

鈥淭he enhanced service efficiency has unified agents’ workspace by consolidating emails, phone, and chats into a single control panel, eliminating the need for our agents to switch between systems. Also, AI-powered automation has reduced agents’ workload and increased efficiency by initiating automated ticket categorization and “next best action” recommendations.鈥� 鈥� Chris Kithinji, Business Systems Analyst

鈥淲e can add an AI-based model for resolving customer queries without any human effort or intervention. It streamlines the customer support process by simply adding the automation.鈥� 鈥� Kanika Rajpal, Technical Architect

麻豆原创 Commerce Cloud

鈥淚t is actually a cloud-based commerce platform, which is a package with lots of features, and we are using that for our Business to Business, Business to Customer, and even for Business to Business to Customer (kind of selling your product to another business and they are going to connect with the end customer) model. As it is very good to customize the features as per your need, and also we can integrate with other 麻豆原创 products like 麻豆原创 ERP, which is very beneficial to support the described model operation from anywhere.鈥� 鈥� Ajay Thakkar, Software Engineer

鈥淲e switched to 麻豆原创 Commerce Cloud four years ago. The software has simplified our e-commerce processes, particularly inventory management, payment processing, marketing, and general order management. Use of the software has enabled us to ensure a good customer experience across various channels. It has also enabled unlimited interaction between the customer service team and our clients, as well as third-party integration.鈥� 鈥� Denis Bacarella, Digital Marketing Assistant

What’s next: agentic AI across the customer experience portfolio

Winning today鈥檚 recognition motivates us to keep pushing forward. 麻豆原创鈥檚 approach to AI goes beyond chatbots and dashboards.

As Balaji Balasubramanian, president and CPO of 麻豆原创 Customer Experience, put it: 鈥淎I alone is no longer a differentiator. What matters is where intelligence operates inside of a business.鈥�

That philosophy is driving a new wave of agentic AI innovation across all three award-winning products.

now features an email-to-quote capability through a Microsoft Outlook add-in that can automatically populate SKUs from deal and email context, helping to turn a seller鈥檚 inbox into a quoting engine. A new deep research feature can synthesize 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud data with external market intelligence to help supercharge account planning.

is rolling out an Agent Inbox that can consolidate cases, tasks, and service orders into a unified command center with live workload insights. A new Digital Service Agent handoff can intelligently summarize customer intent before ticket creation, helping to reduce friction and improve resolution speed.

is embedding AI directly into catalog management, extracting product details from uploaded documents to help auto-enrich descriptions and improve product discoverability at scale.

Trust as a foundation for innovation

These TrustRadius Top Rated awards confirm what 麻豆原创 has always believed: that great software earns trust through outcomes, not marketing. As we continue to evolve 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Commerce Cloud with agentic AI capabilities, the voice of our customers remains our most important compass.

Learn more about 麻豆原创 Customer Experience innovations . Explore the TrustRadius Top Rated methodology at .

Complexity is not always the problem. Unmanaged, and often self-created, complexity is.

A practitioner鈥檚 perspective

Some years ago, after helping turn around a large global 麻豆原创 Customer Experience program, I was contacted by several customers asking a surprisingly simple question: what did you do differently?

My answer was a set of practical keys to success, which I refined through discussions and workshops with customers over the years. The pattern was remarkably consistent. Weak governance and pragmatic change management were recurring problems. But an even more fundamental issue appeared again and again: many organizations had invested in a solution without a sufficiently clear and shared understanding of what they wanted to achieve with it.

Once that first question is unclear, everything downstream becomes harder.

Start with the outcome, not the transformation

Transformation programs tend to generate activity very quickly.

Workstreams are created. Governance boards appear. Solution workshops are scheduled. Backlogs grow. Training plans are developed. New roles and responsibilities are defined.

All of that may be necessary. But before asking how to transform, organizations need to be able to answer a much simpler question: what are we actually trying to improve?

Is the ambition profitable growth? A fundamentally different customer experience? A new service or business model? Greater operational resilience? Better use of data across the enterprise? The ability to scale into new markets?

Without that shared value intent, different teams start optimizing different things. Business stakeholders describe desired outcomes, implementation teams focus on solution scope, and users are eventually trained on functionality without always understanding what should change in their daily work. The result can be a very busy transformation program with surprisingly little transformation.

Keep the business, solution, and people connected

Over time, I started using a simple model to explain this: the transformation triangle.

A transformation needs three perspectives to stay connected:

• Business: Why are we changing? What outcomes, value drivers, processes, and measures matter?
• Solution: What capabilities, technology, integrations, data, and implementation choices are needed?
• People: Who needs to work differently, what support do they need, and how will adoption be sustained?

None of these works well in isolation. A technically excellent solution with weak business alignment becomes an expensive implementation. A strong strategy without a workable solution remains a presentation. And a well-designed process that people do not understand or adopt remains, at best, another PowerPoint slide.

This thinking later became part of the Cloud Mindset Workshop, available to order in the Advanced Success Plan version for 麻豆原创 Customer Experience solutions. The workshop helps translate the business, solution, and people perspectives into practical topics covering business outcomes, governance, processes, rollout, change management, enablement, and adoption.

A skills gap is not always a training gap

This also changes how we should think about skills gaps.

The immediate reaction is often to provide more training or bring in more technical specialists. Sometimes that is exactly what is needed, but many transformation gaps are broader capability gaps.

A project may have excellent product experts but still struggle because nobody can translate business objectives into process priorities. A strong implementation team may still fail if decision rights are unclear. End users may know how to navigate a solution but not understand why their role has changed.

Skills therefore span all three sides of the triangle: business judgement, process knowledge, product expertise, data and integration capability, governance, change leadership, and adoption.

The objective should not be to create experts in everything. It should be to make sure the organization has the right capabilities at the right moment, and that those capabilities work together.

Governance should reduce complexity, not add to it

Governance is another area where organizations can accidentally create more complexity than they remove.

Good governance does not mean more meetings, more steering committees, or larger RACI matrices. It should make a few things very clear: Who decides? What needs to be decided? Based on which outcomes and measures? How are dependencies and risks escalated? And when should the plan change?

Governance needs to come early because execution becomes difficult when ownership, priorities, and decision-making remain ambiguous. The purpose of transformation governance is not to manage complexity for its own sake. The purpose is to make complexity manageable.

Autonomous CX raises the stakes

AI and autonomous capabilities add another dimension.

Autonomous CX can increasingly use AI agents across 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, 麻豆原创 Commerce Cloud, and 麻豆原创 Engagement Cloud to help interpret context, recommend actions, and execute parts of customer-facing processes.

That can remove effort and accelerate execution. But, increasing autonomy does not remove the need for business clarity, governance, process alignment, or people enablement. It increases it. This is also why AI initiatives need to stay connected to business outcomes, process design, governance, and adoption. The more autonomous the technology becomes, the less ambiguity the organization can afford. An AI agent can execute a process faster. It cannot decide what the organization should value, resolve unclear ownership, repair a broken operating model, or create trust by itself.

Most transformation challenges can be traced to a few recurring themes.

Organizations struggle to maintain alignment on outcomes, connect strategy with execution, close the right capability and adoption gaps, and establish governance that supports decisions rather than slowing them down.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions can help address these challenges in a structured way. The Cloud Mindset Workshop can provide a holistic overview of the key dimensions of transformation across business, solution, and people, using practical concepts and customer examples to help teams reflect on their own approach. From there, more focused expert-led services can go deeper where needed.

Clarify the value intent

Use value management, for example through the value management expert session, to align business stakeholders on outcomes, value drivers, and meaningful measures.

Understand the operating reality

Examine end-to-end processes, ownership, dependencies, and the capabilities required to deliver the intended outcomes. Services such as business process best practices can help teams assess and improve the way processes are designed and executed.

Close the relevant capability and adoption gaps

Bring in targeted expertise where it is needed. This can include services such as the time to value accelerator, technical expert services, AI-focused guidance to identify and apply relevant use cases, and focused support for organizational change and adoption to close both capability and adoption gaps.

Govern and adapt

Use the engagement plan and recurring checkpoints to review progress, make decisions, and adjust priorities as the transformation evolves.

Transformation will never become simple. But it can become understandable, governable, and executable. And in our experience, that is usually where success starts.

Raf Dille is product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

The recognition reflects 麻豆原创鈥檚 continued commitment to innovation in order management and the confidence customers place in 麻豆原创 Order Management Services to run some of the world鈥檚 most complex, high-volume order operations.

Every order is an opportunity to keep a customer promise, earn lasting loyalty, and increase profitability. However, as customer expectations rise and supply chains grow more distributed, order management has become a complicated, siloed process. This is where 麻豆原创 Order Management Services comes in and turns order management into enterprise execution intelligence.

empowers businesses to run orders with intelligence, connecting demand, inventory, fulfillment, and financials in real time. With its cloud-native, API-first, composable architecture, the solution helps organizations to maintain operational efficiency and adapt to the changing business needs continuously.

Seamless, cohesive order management processes

Siloed, fragmented data and workflows are bottlenecks to delivering on customer promises. Organizations need connected order management processes throughout the entire order life cycle. 麻豆原创 Order Management Services unifies data and processes into a single, cohesive experience with an intuitive interface that enables teams to monitor order processes, resolve exceptions, and tailor business flows to fit the way each organization operates.

From order capture to inventory visibility, fulfillment execution, and exception handling, every step of the order life cycle needs to be connected in real time. With 麻豆原创 Order Management Services, every team member involved in order management can access a single source of truth, reducing errors and accelerating data-driven decision-making.

In addition, the empowers businesses with AI-driven actions and recommendations across the order life cycle. The assistant surfaces potential risks and gaps before they become customer issues and recommends next-best actions. Team members interact with the assistant in natural language, turning real-time order data into faster business decisions.

Composable order management

麻豆原创 Order Management Services integrates with 麻豆原创 S/4HANA, 麻豆原创 Customer Experience (麻豆原创 CX) solutions, the broader 麻豆原创 ecosystem and partner solutions, connecting orders, inventory, pricing, and financials across the enterprise. Moreover, its cloud-native, API-first architecture enables businesses to easily adapt and expand new order channels, fulfillment nodes, and business rules as needed.

This modular approach gives organizations the flexibility to support a wide range of business models, from B2C to complex B2B, and to evolve their order management capabilities incrementally as the business changes.

Looking ahead

Being recognized as a Leader by IDC reflects the continued innovation of 麻豆原创 Order Management Services. 麻豆原创 is investing in expanded order management capabilities and deeper AI innovation to help businesses stay ahead of customer expectations and drive profitable growth.

To learn more, and explore the .

Emilie Fournelle is head of Product Management for 麻豆原创 Order Management Services at 麻豆原创.

IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of technology and suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each supplier鈥檚 position within a given market. The Capabilities score measures supplier product, go-to-market and business execution in the short-term. The Strategy score measures alignment of supplier strategies with customer requirements in a 3-5-year timeframe. Supplier market share is represented by the size of the icons.

Service-led growth is one of the seven macro trends within Autonomous CX, 麻豆原创’s vision introduced at 麻豆原创 Sapphire in 2026. At its core is a simple principle: every customer promise must be backed by the operational reality needed to deliver it.

highlights a growing gap between customer expectations and the operational reality of service delivery. Disconnected handoffs, fragmented information, and pressure to adopt AI make service a business-model question, not just a technology one.

A practitioner鈥檚 perspective

More than twenty years ago, I was part of a global service organization that wanted to move beyond viewing service as a cost of doing business.

At the time, our primary objective was cost recovery. Service was necessary to support the product business, but it was not really seen as a business in its own right.

We started changing that by introducing new professional services and finding ways to monetize expertise we already had. Remote device monitoring, for example, allowed us to organize support across time zones and offer profitable after-hours services to customers who depended on continuous operations.

A few years later, I was asked a more fundamental question: could the service organization survive as an independent business?

To explore that question, we used an early version of what later became widely known as the business model canvas. We looked beyond service operations and examined the complete picture: our value proposition, customer segments, channels, activities, resources, costs, and revenue streams.

Looking back, that exercise taught me something that is still relevant today.

Service as a revenue driver is not a new idea. And it does not start with technology. It starts with a business model.

What do we mean by service-led revenue?

“Service as a revenue driver” is often used as if it means one thing. In reality, service can contribute to revenue in several different ways.

First, it can protect revenue. A customer whose issue is resolved quickly and professionally is more likely to renew, continue buying, and remain loyal.

Second, it can influence revenue. Service professionals often understand a customer’s operational reality better than anyone else. They may identify a need for additional services, training, upgrades, or new solutions, creating opportunities for commercial teams.

Third, it can generate revenue directly through premium support, professional services, subscriptions, remote monitoring, advisory services, or outcome-based offerings.

These ambitions are related, but they are not the same. Each requires different processes, skills, measures, and sometimes different operating models.

Not every service interaction should become a sales conversation. But every service organization should understand whether it is expected to protect, influence, or directly generate revenue.

Technology creates possibilities, not the business model

Technology has always played an important role in service innovation.

Remote monitoring reduced the need for on-site visits. Connected solutions made global support models possible. Customer and service platforms improved access to account, contract, equipment, and interaction data.

Today, Autonomous CX capabilities can expand these possibilities even further. Joule, embedded AI agents in 麻豆原创 Service Cloud, and AI-assisted opportunity detection in 麻豆原创 Sales Cloud can classify and route cases, summarize interactions, surface relevant knowledge, identify patterns, detect revenue opportunities, and increasingly automate routine requests. All within the context of a connected service and sales operating model.

Yet adoption does not equal usage, and usage does not equal value. Technology does not answer the most important questions:

• What value are customers willing to pay for?
• Which customers should we serve?
• How will the service be sold, delivered, and measured?
• Can it be delivered consistently, profitably, and adopted by employees and customers?

Customers are already drawing their own conclusions. In 2026, believe AI in service is deployed primarily to save money, not to improve service. Seventy-nine percent still strongly prefer human support. These perceptions are not only a trust problem, they point to a business model problem. When the technology decision precedes the value decision, customers notice.

From ambition to execution

This is where many service-led growth initiatives struggle.

Ambition may be clear in the boardroom, while the organization underneath continues to operate as before. Service is still measured primarily on cost and case closure. Sales and service pursue different objectives. Customer information remains fragmented. Opportunities identified by service disappear during handovers. Employees are expected to adopt new behaviors without understanding why.

Turning service into a measurable revenue driver therefore requires more than enabling a new feature or deploying a new technology. It requires alignment between business objectives, processes, people, data, and technology.

The Advanced Success Plan version for 麻豆原创 Customer Experience solutions is an expert-led engagement model that helps translate Autonomous CX into an executable plan. It does not replace business strategy or determine which services should be brought to market. Instead, it helps connect a chosen ambition to the processes, capabilities, and governance needed to make service-led growth measurable and repeatable.

1. Clarify the value intent

The first step is to define what service-led growth actually means for the organization.

Is the priority to improve retention? Increase renewals? Create opportunities through service interactions? Launch paid services? Improve profitability?

Through the value management expert session within the Advanced Success Plan for 麻豆原创 Customer Experience solutions, stakeholders can align on business priorities, value drivers, and success measures.

The objective is not to create a long list of KPIs, but a shared understanding of the outcomes that matter most.

2. Connect the end-to-end process

Once the ambition is clear, the next question is how value will actually be created.

If a service professional identifies an opportunity, what happens next? Who owns the follow-up? Is the customer experience consistent from the initial interaction through fulfilment and invoicing?

Business process best practices and expert guidance can help identify gaps in ownership, handovers, and process alignment across service, sales, commerce, and supporting operations.

This matters beyond operational efficiency. shows that 45% of revenue leaders cite improving collaboration and handoffs across marketing, sales, and service as a current priority, and 39% are actively pursuing expansion, cross-selling, and upselling within existing accounts. Service teams often have valuable customer context, but the real question is whether the process exists to act on it.

3. Close capability and adoption gaps

Organizations can then assess which capabilities are needed to support the process. This could involve better access to customer information, improved knowledge management, analytics, automation, AI-supported recommendations, or opportunity management capabilities. Targeted expert services within the Advanced Success Plan for 麻豆原创 Customer Experience solutions help connect 麻豆原创 CX capabilities to desired business outcomes.

At the same time, employees need the right enablement, incentives, and confidence to adopt new ways of working. Without that, even the best-designed process remains a PowerPoint slide.

The cost of getting this wrong is real. Employees asked to adopt new behaviors without understanding why, or without the tools to support them, do not persist. When experienced people leave, they take institutional knowledge and customer relationships with them. Enablement is not a training exercise; it is a retention and continuity investment.

4. Measure, learn, and improve

Service-led growth is not delivered through a single project.

highlights several recurring priorities for service leaders: efficiency and time to resolution (48%), collaboration and handoffs across marketing, sales, and service (46%), first-contact resolution (40%), and AI-driven predictive insights (33%). These figures provide useful context, but the measures that matter for service-led growth depend on the value intent defined at the start. Progress should therefore be tracked against the agreed outcomes, whether these involve retention, renewals, service-generated opportunities, paid-service revenue, or profitability.

Ongoing governance and engagement planning helps organizations review progress, address gaps, and scale successful approaches over time.

Service-led growth is a business-model choice

Moving from cost recovery to service-led growth is not simply a matter of asking service employees to sell more. It is a business-model choice with implications for the value proposition, customer experience, processes, organization, skills, technology, and measures of success.

Twenty years ago, remote monitoring changed what service organizations could deliver. Today, AI is expanding those possibilities again. But the underlying challenge has not changed. Technology changes possibilities. Business models determine how that value is captured.

First, decide where service should create value. Then, build the operating model required to deliver that value consistently and profitably.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions can support that journey by helping organizations connect business outcomes with the processes, capabilities, adoption, and governance needed to turn ambition into measurable results.

Raf Dille is product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

While the power of technology is at its apex, so are expectations. And the gap between the two is where revenue goes to die.

Brands using AI to deepen human connection by freeing up sellers, marketers, and service agents to focus on empathy, trust, and complex problem-solving are pulling ahead. Those still bolting AI onto fragmented systems, or using it purely as a cost-cutting measure, risk falling further behind.

To help organizations address the rapidly changing factors impacting CX, we鈥檙e proud to debut our first quarterly 鈥淪tate of鈥� reports. Designed to pull together the latest research and data across sales, service, marketing, and e-commerce, these thought leadership pieces are available without a gate because we know that experience and expertise matter, and we want to share ours with you. (You can find links to the reports at the end of this post.)

The key takeaways from each report demonstrate that CX is no longer a single department’s job. Rather, it’s a cross-functional discipline, and the stakes for getting it wrong are higher than ever.

The cost of bad experiences is staggering

Let鈥檚 start with the number that should be pinned at the top of every leadership deck this quarter: bad customer experiences have put $3 trillion in global sales at risk in 2026, with consumers actively cutting back $2.1 trillion in spending and walking away entirely from $865 billion worth of it, according to Forbes data cited in our . That’s not churn. That’s customers voting with their wallets in real time.

And they’re not quiet about why. A striking 82% of consumers say a brand has disappointed them, and 60% admit they don’t pay attention to brands even when their product needs are being met. Translation: satisfying the transaction isn’t enough anymore. If the experience around it feels disorganized or impersonal, customers check out.

On the service side specifically, 75% of consumers say they’re put off by disorganized brands that pass them between multiple people or teams just to solve one problem, and 46% say service flat-out feels too impersonal.

The combustible combination of sky-high financial risk and low tolerance for friction is forcing every customer-facing function to rethink how it operates.

That’s the backdrop. Now let’s talk about what’s actually happening in each corner of the customer journey.

AI is everywhere, trust in it is not

Every function is racing to embed AI, with good reason. AI-driven traffic to U.S. retail sites is up 4,700% year over year, and almost 60% of consumers have already used AI to shop, as cited in our .

On the marketing side, notes that AI has moved from experiment to foundational: 33% of marketing leaders are using it for hyper-personalized engagement, and 31% say predictive insights and personalization have been a top priority all year.

Meanwhile the report points out that digitized suppliers leveraging AI are outperforming their peers on sales goals by a jaw-dropping 110%.

But here’s the twist that ties it all together: customers don’t actually love the AI they’re interacting with.

report underscores that point specifically:

• 79% of Americans say they strongly prefer human support over an AI agent
• 63% don’t believe AI can replace humans in service roles at all
• 89% believe brands should always offer the option to talk to a real person
• 81% believe AI is primarily being used to save the company money, not to improve their experience.

That’s the tension every function needs to sit with. AI is delivering real, measurable business value, but if customers believe that AI was deployed to cut costs rather than serve them, you might win efficiency but lose the relationship.

Trust is the currency, and it’s getting harder to earn

Each one of the reports circles back to the same word: trust. In marketing, 61% of B2B buyers say trust and credibility are the most important thing content can deliver, ranking above lead generation. Consumers are leaning on peer and creator trust more than brand messaging, too: 76% of brands report that sponsored content with creators now outperforms traditional advertising, and in social commerce, 45% of Gen Z shoppers say they’re more likely to trust a product once it goes viral.

On the e-commerce side, trust shows up as transparency. With tariffs pushing import costs up 15-30% across major categories, brands that explain price increases rather than quietly passing them along are building goodwill that pays off in retention. And in sales, the numbers show that your existing customers–the ones who already trust you–are an underused asset: 45% of revenue leaders are now focused on improving handoffs across marketing, sales, and service, while 39% are chasing expansion and upsell revenue instead of only hunting net-new logos.

The people problem behind the technology story

There’s one more thread that doesn’t get enough attention in the AI headlines: the humans delivering these experiences are stretched thin. Call center turnover is running 40-45% in 2026, spiking to 55-60% in high-stress sectors, while replacing a single agent can cost up to $46,000 when accounting for lost productivity.

As AI absorbs the easy tickets, what lands on human agents is the hard stuff: it鈥檚 complex, is emotional with high-stakes, and burnout is quietly eating away at CSAT and first-contact resolution scores. This matters because customer experience isn’t just a technology stack or a personalization engine. It’s fundamentally delivered by people; whether that’s a service agent handling an escalation, a seller navigating a buying committee, or a marketer trying to sound human in an AI-saturated feed. Protecting the people doing that work isn’t a wellbeing initiative separate from CX strategy. It is CX strategy.

Fragmentation is the enemy of good CX

If you weave every stream together, the state of CX has one clear directive: stop treating AI, data, and channels as separate initiatives owned by separate teams, and start treating the customer experience as the single thread that runs through all of them.

That means AI-shopping agents that use clean, structured product data. It means first-party data strategies that replace the crumbling third-party targeting most marketing was built on. It means service and sales teams that know exactly when to step back and let self-service work, and exactly when to step in and be human. And it means service organizations that give their people better tools instead of just more automation.

Future success won鈥檛 be shaped by the brands with the most AI; it will favor the brands that make customers feel understood, even as the experience becomes more automated. That’s not a technology bet. That’s a trust bet, and right now, trust is in short supply.

You can find our 鈥淪tate of鈥� reports here:

Jessica Keehn is chief marketing officer of 麻豆原创 Customer Experience.

To meet these expectations, organizations must move beyond siloed commerce channels toward a connected autonomous CX model that integrates commerce, order sourcing, fulfilment, and customer support across the full customer journey.

The Advanced Success Plan for 麻豆原创 Customer Experience supports this shift by accelerating omnichannel capability build-out through outcome-based governance. It helps organizations move from fragmented execution to coordinated operations鈥攅nabling consistent cross-channel experiences, aligning commerce with sales and service execution, and improving conversion and repeat purchase through end-to-end alignment.

Reality of B2B buying

B2B buying is rarely linear anymore. Buyers often switch between digital channels, sales teams, and service touchpoints before deciding. Research from McKinsey鈥檚 B2B Pulse highlights that buyers typically engage across multiple interaction channels before making a decision.**

This reflects how digital commerce has become a core part of the buying process, with 58% of global B2B retailers selling on at least three e-commerce platforms.鈥�

At the same time, expectations have shifted. Consistency across channels is assumed, digital self-service is often the starting point, and speed and transparency are baseline requirements.

That creates a dual challenge for organizations: improving customer experience while also managing increasing operational complexity behind the scenes.

Convergence of commerce, sales, and service

B2B environments combine complex operations with tightly connected customer journeys. Organizations must manage customer-specific pricing and contracts, large and dynamic product catalogs, multi-step approvals, distributed fulfilment models, and integrations across commerce, ERP, sales, and service systems. Customers, however, do not see this complexity. They experience a single journey, from discovery to purchase, fulfilment, and support, and expect it to feel seamless.

A typical journey may include discovering products online, aligning pricing with sales, placing orders through self-service channels, and resolving issues via service interactions. Expectations for real-time visibility, fast issue resolution, and consistent engagement continue to rise, as highlighted in research such as DHL鈥檚 B2B E-Commerce Trends. The same DHL report also shows that 78% of B2B retailers expect website sales to grow over the next three to five years, which reinforces how central digital channels have become.鈥�

Yet many organizations still operate in silos. The result is often inconsistent data, limited visibility across interactions, slower issue resolution, and disconnected customer experiences. This is why omnichannel transformation is not about adding channels, but about connecting commerce, sales, and service into a unified operating flow.

Process excellence as a foundation

As complexity increases, end-to-end process alignment becomes critical. Customers experience outcomes, not systems, and those outcomes depend on how well processes are connected across commerce, sales, service, and fulfilment.

Breakdowns typically occur when sales agreements are not reflected in commerce systems, fulfilment is not aligned with order promises, service teams lack customer context, or data differs across channels. These are not isolated system issues, but symptoms of disconnected processes.

In practice, that can mean a customer sees one price in the portal, a different one in the quote, and another one in the order confirmation. It can also mean service teams have to ask customers to repeat information that already exists elsewhere in the organization. Even when the underlying technology is in place, the experience still feels broken if the process is not connected.

Organizations that invest in process excellence are better positioned to deliver consistent experiences, reduce friction, improve operational efficiency, and scale complex B2B models. Process alignment also makes it easier to respond to change, because teams can adapt faster when the underlying journey is not held together by disconnected handoffs.

Why omnichannel matters

Omnichannel in B2B is not just about offering more ways to buy. It is about making those ways work together. When the customer starts on one channel and finishes on another, the handoff needs to feel natural. If not, the customer experience becomes fragmented very quickly.

That matters because B2B buyers are increasingly comparing their business purchasing experience to the consumer experiences they already know. They expect simple navigation, transparent pricing, reliable order updates, and a service team that understands the full context of the account. In other words, they want the convenience of digital commerce without losing the support and complexity that B2B purchasing often requires.

This is also where many companies struggle. They may have a strong storefront, but weak back-end coordination. Or they may have good sales support, but poor visibility once the order has been placed. Omnichannel transformation closes that gap by connecting the customer-facing experience with the operational processes behind it.

Accelerating outcomes with the Advanced Success Plan for 麻豆原创 Customer Experience

The Advanced Success Plan for 麻豆原创 Customer Experience helps organizations accelerate omnichannel capability build-out through outcome-based governance and alignment between strategy and execution.

Rather than focusing only on implementation, the emphasis is on measurable outcomes across the full customer journey. This includes enabling consistent omnichannel experiences, aligning 麻豆原创 Commerce Cloud, 麻豆原创 Sales and Service Cloud, and fulfilment processes, managing catalog and contract complexity at scale, improving order sourcing and fulfilment coordination, and strengthening end-to-end process alignment.

The goal is to ensure all capabilities operate as one connected system rather than separate functions.

That becomes especially important in B2B environments where a single transaction can involve multiple stakeholders, custom pricing rules, approval steps, and several systems working together at once. Without clear governance and alignment, even well-designed digital tools can create confusion instead of clarity.

With the right operating model, however, organizations can turn complexity into a strength. They can reduce friction for customers, improve efficiency internally, and create a more reliable buying experience across every channel.

The Advanced Success Plan for 麻豆原创 Customer Experience includes access to Business Process Best Practices which helps customers understand the end-to-end process flow and best practices for executing business processes across 麻豆原创 Sales and Service. This service showcases reference processes and bridges the gaps that can occur during rapid implementations of solutions in complex landscapes or when implementation of multiple solutions creates fragmented processes without taking into consideration unique end-to-end view.

Conclusion

Omnichannel commerce and B2B digital transformation are reshaping how organizations engage customers and deliver value. Success depends on connecting commerce, sales, service, and fulfilment into a unified operating model supported by strong end-to-end processes.

Organizations that focus on process excellence and outcome-based governance are better positioned to scale effectively and meet rising customer expectations. The Advanced Success Plan for 麻豆原创 Customer Experience enables this by connecting strategy to execution and supporting consistent outcomes across the customer journey.

For B2B companies, the real shift is not just digital adoption. It is building a model where channels, processes, and teams work together in a way that feels simple to the customer, even when the operation behind it is complex.

Nikola Stojanovski is a product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

*Digital Commerce 360, 2025
**McKinsey, 2021
鈥燚HL, 2025

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.

As customer journeys become more connected and complex, gaps in execution can lead to inconsistent experiences, slower response times, and missed opportunities. The next frontier of customer experience is not generating more insights, but turning insight into coordinated action at scale.

This latest release of the solution portfolio helps organizations strengthen that foundation by connecting workflows across marketing, commerce, sales, and service鈥攅nabling more consistent, scalable execution across every customer interaction.

Explore the highlights of the Q2 2026 release. For full sub-solution details, see our recaps for the , , , , and solutions.

Turning customer intent into action

Customer interactions are becoming more conversational, connected, and immediate across channels and touchpoints. At the same time, organizations need faster access to information and simpler ways to take action鈥攚hether engaging customers, managing campaigns, or responding to changing business needs.

• Conversational AI shopping through a model context protocol (MCP) server: Enable secure integration between and AI agents that can guide or act on behalf of customers. AI assistants can query real-time product information, provide inventory updates, manage shopping carts, and complete transactions directly within chat or voice interfaces鈥攃reating more intelligent, conversational buying experiences beyond the traditional storefront. 

• Joule in 麻豆原创 Engagement Cloud (麻豆原创 Early Adopter Care): Bring 麻豆原创’s conversational AI directly into campaign workflows. Teams can ask product or campaign questions in natural language and get accurate answers without searching across multiple systems. They can also duplicate successful campaigns without starting from scratch, freeing more time for strategic thinking, creativity, and customer engagement.

• Rich communication services (RCS) in 麻豆原创 Engagement Cloud: Engage customers with rich, interactive messages supported by Google and featuring media, carousels, and action buttons within native mobile messaging experiences. Branded, verified messages help build trust and guide customers smoothly from discovery to purchase without requiring an additional application.

Scaling personalized engagement

Recognizing customer intent is only the beginning. As engagement channels expand, marketing teams need to respond quickly while delivering relevant, personalized experiences at scale. This requires frictionless campaign execution, timely insights, and the ability to tailor every interaction to each customer’s needs and preferences.

• AI-assisted content composer (pilot): Generate high-quality, on-brand campaign content in . Using Gemini models informed by audience, product, and campaign context, teams can quickly create and refine content variations so they can launch personalized campaigns faster and spend less time on manual content creation.

• Embedded audience builder: Enable marketers to access and activate rich data from directly within 麻豆原创 Engagement Cloud. With this capability, they can build advanced segments themselves without switching systems or waiting on data analysts. The precision and relevancy of omnichannel campaigns can be improved by combining behavioral, transactional, account, and profile data with operational data across the business.

Enabling consistent sales execution at scale

Success depends on turning insight into disciplined, repeatable actions that drive predictable revenue outcomes. As sales environments grow more complex, even small inconsistencies in data, priorities, or execution can undermine forecasts and cause opportunities to slip away. Acting with greater consistency and confidence calls for stronger data integrity, aligned behaviors, and clearer guidance.

• Agentic opportunity summary overview: Give sales teams the tools they need to quickly assess deal health. This capability in aggregates engagement signals, activity levels, and progress indicators into a real-time view, allowing teams to identify risks early, prioritize effectively, and maintain deal momentum.
• : Optimize sales velocity and help ensure the right product placement with retail execution enabled by intelligent, AI-enhanced processes that maximize revenue. Teams can improve visit planning and execution, harness insights to improve sales performance, and optimize interactions. For consumer products companies, this helps drive shelf availability, promotion compliance, and merchandising effectiveness across retail locations. Field teams gain greater visibility into store-level execution, enabling more consistent brand presence and stronger sell-through performance.

• 麻豆原创 Incentive Management: Improve sales team effectiveness by using the solution, which is part of solutions. It helps drive profitable behaviors that increase revenue and support business growth while providing real-time performance insights, dispute management, and motivating rewards. Teams can use flexible tools to streamline incentive compensation and quickly design, test, and launch sales plans. AI-supported recommendations are also available to guide organizations in optimizing plans, maximizing outcomes, and uncovering actionable insights.

• Consumer Products Intelligence (麻豆原创 Early Adopter Care program): Enable consumer product companies to turn the enormous amount of sales and trade data they generate into better decisions. It uses analytics and AI to help improve trade spend performance, increase sales revenue and margins, and reduce manual effort.
   

Standardizing service execution across the enterprise

Service teams are increasingly expected to deliver faster, more reliable support while managing growing complexity across channels and requests. Achieving this objective requires simplifying how services are accessed and helping ensure consistent processes across the organization.

• Self-service catalog: Allow employees to quickly find what they need without understanding backend processes. Through this guided, intuitive catalog for requests are automatically routed with the right context, reducing delays and improving resolution times.

• Content package framework: Leverage the framework for 麻豆原创 Enterprise Service Management to deliver rapid, scalable value across lines of business. Prebuilt, reusable configurations for case types, workflows, and catalogs help organizations deploy services more quickly while simplifying implementation across the business. With this approach, organizations can eliminate complexity, empower partners, and speed adoption. Content packages for HR service delivery will be coming soon. 
• Email editor in 麻豆原创 Service Cloud and 麻豆原创 Enterprise Service Management: Compose, edit, and manage customer communications more efficiently while maintaining high-quality service interactions. The modern, user-friendly email editor is built for an AI-first world.

• Creation of sales objects from customer hub: Let agents fully manage leads, opportunities, appointments, and sales orders directly from the service agent workspace of 麻豆原创 Service Cloud. This capability helps turn each customer interaction into an opportunity to deliver more value.

Accelerating connected order management

Turning insight into action requires connected systems that can adapt as the business evolves. As organizations expand order channels, fulfillment networks, and technology landscapes, they need integration and order management that can keep pace so teams can respond faster to change.

• Flow connector: Enables smooth data flow between the solution, other 麻豆原创 solutions, and third-party products. This predefined capability allows business users to configure custom business flows and integrations with minimal IT involvement, creating connected order management processes across the enterprise.

Execution at scale: the next customer experience advantage

As AI becomes embedded in daily operations, the differentiator shifts from insight generation to execution.

Our recently announced strategic partnerships with and Google Cloud help extend this execution-first approach by connecting AI-powered service, commerce, and engagement experiences directly to operational systems and business data. As a result, organizations can move from isolated interactions and insights to coordinated actions that drive faster resolutions, better customer experiences, and greater business impact.

Learn more about 麻豆原创 CX in Q2鈥�2026 

Read the 麻豆原创 Help documentation to get started with these new capabilities:

Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience and Consumer Industries at 麻豆原创.

But what if more or better software is not necessarily the answer? Each solution solves a real problem for a specific team. But collectively, they create a consequence no one planned for: every new tool builds its own data world, without a common language or context across them.

In a 2026 study by Oxford Economics, . Twenty-nine percent remain highly fragmented. Organizations with siloed CX tech are more likely to face an inability to connect customer needs to actionable data insights. Fifty-eight percent reported this challenge, compared to 47% among those with harmonized environments.

More than what tools businesses choose to add to their CX landscape, how they connect and interact with each other becomes even more important.

A unified data strategy sounds straightforward in principle. In practice, most businesses find that the obstacle is not ambition but rather execution. Every integration decision made without a clear data architecture becomes a future campaign mired in manual reconciliation, a customer journey that breaks at the handoff, or a personalization promise the disparate data sources cannot support. Implementation without a validated strategy creates new fragmentation inside the solution meant to eliminate the old. And without a structured way to pressure-test decisions before any commitment is made, even well-resourced organizations find themselves repeating the same cycle: invest, integrate, fragment, repeat.

The real barrier is not budget or technology

A Forrester study of more than 1,000 senior executives found that , ahead of budget, technology maturity, and talent. The same study found that 56% of respondents struggle with poor data quality; 55% face persistent data silos. This is not for lack of investment in technology, but because the connections between systems were not designed or maintained effectively.

What bridges that gap is not another platform, it is the expertise to think through data connectivity decisions before they are made and the ongoing guidance to ensure those decisions compound into measurable gains over time. The for solutions provides that guidance along every step of the journey.

Define what success actually looks like

The most common reason data unification projects fall short of expectations is not technical failure, it is a failure to define and measure what success looks like for the entire business before the work begins.

The value management session from the Advanced Success Plan for 麻豆原创 Customer Experience establishes that definition at the outset: What does a fully unified data strategy actually enable? It means running the next marketing campaign without manual data reconciliation, and presenting an AI readiness road map without caveats. Stakeholders will know, at every checkpoint, whether the investment is moving the business forward, not just moving the project forward.

Design the strategy before building the integrations

A robust data strategy starts with good design. Product guidance from the Advanced Success Plan for 麻豆原创 Customer Experience covers available out-of-the-box integrations, common usage scenarios, and pitfalls and how to avoid them鈥攁ll delivered in a live remote session by an 麻豆原创 expert who can answer questions in real time. The data strategy can be conceptualized and pressure-tested before any budget or technical commitments are made.

Validate every critical decision with expert guidance

With the technical assistance and functional assistance from the Advanced Success Plan for 麻豆原创 Customer Experience, businesses have continuous access to expert guidance at every critical decision point. Beyond resolving immediate questions, the ongoing access also shares insight on how 麻豆原创 thinks through problems, strengthening in-house expertise with every interaction. The result is an organization that makes better decisions not just now but for the future.

Measure whether the strategy is delivering

Adoption and innovation checkpoints conducted on a quarterly or semi-annual basis bring the measurement back to where it started: the business outcomes defined at the outset. Do campaigns run without manual reconciliation? Is the AI use case performing against its stated goals? A clear throughline from the value management success KPIs to the adoption and innovation checkpoints proves the benefits of the investment.

Where cycles and silos break

The cycle of invest, integrate, fragment, repeat is not inevitable. It is the predictable result of making technical decisions without an anchoring business imperative, and integration decisions without strategic expert guidance. Organizations that break the cycle do not necessarily have better technology than their competitors鈥攖hey have better judgment about how to use it.

The Advanced Success Plan for 麻豆原创 Customer Experience exists for exactly that reason: to put proactive and prescriptive guidance at every decision point where that judgment matters most. When data strategy is designed before it is built, validated before it is committed, and measured against real business outcomes, the technology investment already made starts working harder.

The stack was never the problem. When the thinking behind the technology finally matches its ambition, the personalized, seamless experience becomes a reality.

Tara Tracey, global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.
Ella De Torres, product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.

While cash is losing importance, contactless payments and mobile wallets are becoming dominant, with many providers enabling customers to make payments directly from their smartphones. These shifts put constant pressure on retailers to update their systems. The demand for mobile, contactless, and secure payment solutions keeps growing.

Market trends determine success

As a globally operating company, 麻豆原创 keeps a close eye on the latest trends, looking not just to adapt its solutions but to rethink them. 麻豆原创 Customer Checkout, the intelligent integrated point of sale (POS) solution for retail, merchandising, and catering, relies on strong partnerships to stay ahead and keep pace with those trends. Speed and the right collaborations make the difference.

“The POS market is in constant flux. Competitors are alert and customers arrive every day with new demands and ideas,” said Harald Tebbe, development lead for 麻豆原创 Customer Checkout, speaking to the scale of change in the market. “Ultimately, market trends decide who stays successful and who doesn’t. Even though payment processes aren’t our direct business, our focus is on offering customers a wide range of payment options to make the end customer’s shopping experience as simple and convenient as possible. Payment infrastructure is a critical component of that experience. Our trusted technology partnerships are what make it possible, which is why we’re continuously looking for new collaborations.”

End-to-end solution through 麻豆原创 Customer Checkout and Viva.com

As the first tech bank in Europe for businesses and being active in 29 countries, Viva.com offers customers an integrated omnichannel payments, banking, and financing platform.

Viva.com’s leading Tap on Any Device payment technology takes centre stage. It turns any Android device鈥攆rom fixed and mobile payment terminals to self-checkout tills and handheld devices鈥攐r iPhone into a secure card terminal, optimizing the customer experience and helping customers pay wherever they are. Tap on Any Device by Viva.com supports over 40 payment methods, DCC, surcharge, offline payments, while the tech bank delivers real-time settlement 365 days a year and a cashback scheme reducing transaction fees to zero percent.

Since 麻豆原创 Customer Checkout was built to be hardware-independent, works across a variety of devices, and integrates with different payment terminals, the partnership creates an ideal end-to-end solution for customers in retail, consumer goods, food service, and other sectors. Open API interfaces in 麻豆原创 Customer Checkout and Viva.com’s Terminal Integration Hub made the integration straightforward and quick to implement.

鈥淲ith Viva.com, 麻豆原创 Customer Checkout customers in 29 European countries get integrated payments and banking in one platform: From omnichannel payments and our leading Tap on Any Device technology to fast, seamless access to tailored financing options -no need to switch providers,” Harry Xenophontos, Chief Business Officer at Viva.com, said. “That’s the unique proposition we’ve built with 麻豆原创.”

Secure data communication for customers

Integrating 麻豆原创 Customer Checkout with Viva.com requires a dedicated plug-in, which simplifies the connection between the POS system and the payment terminal by ensuring data is transferred via the HTTPS protocol, keeping communication secure. This matters in a period where both innovation and the protection of sensitive data are front of mind.

Customers also benefit from a particularly fast setup process and the freedom to choose from a wide range of devices to use as payment terminals. With three possible integration options, the system offers flexibility so merchants can find the right fit for their specific needs.

The joint project supports the development and delivery of forward-looking POS solutions that work well for both merchants and end customers.

Elena Vavitsa is a senior solution specialist for 麻豆原创 Customer Checkout.

Top image courtesy of Viva.com

The Advanced Success Plan version for 麻豆原创 Customer Experience solutions is designed precisely for this moment. It offers utilities customers a structured, continuously guided approach to maximizing value from 麻豆原创 Customer Experience (麻豆原创 CX) investments, from first adoption through to AI-enabled optimization.

A sector in motion: why customer experience has become strategic for utilities

Four converging forces are making customer experience a boardroom priority for utilities and increasing the demand for structured, expert-led adoption support.

• Energy transition and service complexity: Renewables, EV charging, solar feed-in tariffs, and demand response programs are adding new service dimensions. Customers expect their energy provider to manage this complexity seamlessly.
• Deregulation and customer switching: In liberalized energy markets, customers can, and do, switch providers. The cost of acquisition consistently exceeds the cost of retention. Superior service experience is a measurable retention lever.
• Smart metering and data volume: Smart meter rollouts generate billions of interval readings daily. This data can fuel proactive outreach and personalized billing, but only if the customer experience platform is correctly configured to act on it.
• Regulatory intensity: Billing accuracy mandates, complaint resolution timelines, and outage notification requirements are intensifying. The 麻豆原创 CX portfolio can support compliance when features are correctly activated and configured.

麻豆原创 in utilities: a significant and growing market

Utilities are not a niche segment for 麻豆原创. Understanding the scale of the 麻豆原创 utilities community frames why the Advanced Success Plan for 麻豆原创 Customer Experience solutions matters for this industry in particular. 麻豆原创 is trusted by hundreds of utilities customers globally and is . The investment in the relationship has been made. The question is whether customers are fully using what they have.

With the Advanced Success Plan, customers can turn the 麻豆原创 Customer Experience portfolio into a driver of growth and innovation. They can gain the confidence to act decisively, supported by unlimited expert guidance and intelligent insights that help ensure every feature can deliver measurable business value.

The business case: customer retention over acquisition

For utilities operating in competitive markets, retaining customers has become as strategically important as acquiring new ones. Customers can switch suppliers in minutes via a digital platform, complaint escalation processes are visible on social media, and billing errors in a smart meter world are harder to excuse and faster to escalate.

The 麻豆原创 CX portfolio can address these challenges across the full customer lifecycle. 麻豆原创 Engagement Cloud enables targeted, segmented communications. 麻豆原创 Service Cloud can centralize complaint management and agent interactions. 麻豆原创 Sales Cloud helps manage accounts, contacts, leads, and opportunities. 麻豆原创 Commerce Cloud is the聽digital sales and self-service storefront layer and can handle how customers discover, compare, purchase, and manage energy products online. What the Advanced Success Plan for 麻豆原创 Customer Experience solutions does is help to ensure these capabilities are not just purchased but adopted, optimized, and continuously improved.

How the Advanced Success Plan structures the adoption journey

The Advanced Success Plan for 麻豆原创 Customer Experience solutions helps organize the adoption journey across four distinct phases, each with a defined purpose and a set of targeted services:

1. Introducing innovation: Identify and prioritize the right innovations for your business. Services here include product guidance sessions covering areas such as utilities session and agent desktop, intelligent selling, campaigns and segmentation, and AI foundation and use case navigator.
2. Adopting innovation: Plan and prepare for structured adoption with minimal risk. Services include the AI process fit-gap analysis, key feature advisory, release guidance, success expert engagement, service planning, value management, innovation checkpoints, and adoption checkpoints.
3. Activation and optimization: Enable hands-on activation of AI and customer experience capabilities in your environment. Services include activation sessions across 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, 麻豆原创 Marketing Cloud, and 麻豆原创 Engagement Cloud; AI agent activation; embedded AI activation; Joule activation; and technical and functional assistance.
4. Success measurement and continuous improvement: Measure outcomes and sustain momentum through ongoing engagement. Services include a continuous engagement model, release guidance, success expert, value management, innovation checkpoints, and adoption checkpoints.

8 services available to utilities customers鈥攁nd how each one helps

The Advanced Success Plan for 麻豆原创 Customer Experience solutions comprises eight distinct services. Each has a defined scope, a specific business need it addresses, and measurable benefits. For utilities customers, each service connects to a characteristic operational or strategic challenge.

Product guidance
Structured sessions introduce utilities teams to the capabilities most relevant to their context, from meter-read-driven billing notifications in 麻豆原创 Service Cloud to segmented communications in 麻豆原创 Engagement Cloud. The goal is to accelerate time-to-awareness, so teams know what exists before they have to find it.

Key feature advisory
This is curated, customer-specific guidance on which features to activate and in what sequence. For utilities, this means filtering a broad 麻豆原创 CX road map down to the capabilities that matter for smart metering, complaints management, and outage communications then discarding what does not apply.

Release guidance
Every 麻豆原创 CX quarterly release brings dozens of updates. Release guidance helps ensure utilities teams receive a focused brief on what is relevant to their industry context, not a generic list of all product changes, so adoption decisions can be faster and better informed.

Solution review
This is a structured review of the current solution configuration against best practice. For utilities, this commonly surfaces configuration gaps in complaint workflows, billing notification templates, or service agent desktop layouts that erode the customer experience over time if left unaddressed.

Transformation advisory
Strategic guidance connects 麻豆原创 CX capabilities to the outcomes utilities care about most: cost-to-serve reduction, complaint resolution improvement, and regulatory compliance. Transformation advisory helps move the conversation from features to business impact.

Activation
This is hands-on, expert-assisted activation of specific capabilities in the customer’s live environment. For utilities, this includes activating Joule for service agents and enabling AI-driven case categorization in 麻豆原创 Service Cloud. Many autonomous agents or assistants announced at 麻豆原创 Sapphire, once available, can be activated to help guide service agents to process cases.

Technical assistance
Direct technical support across all phases of the engagement covers integration architecture, performance guidance, load testing guidance for mass billing cycles, and resolution of complex system behavior. This service is especially critical for utilities given the high-volume, time-sensitive nature of meter-read processing and month-end billing runs.

Functional assistance
This comprises business process and functional configuration support throughout the engagement. Utilities-specific coverage includes complaint handling workflows, outage notification sequences, and the configuration of billing determinant displays in the service agent desktop, helping to ensure what is built serves the way utilities actually operate.

A practitioner’s perspective

Working directly with utilities customers shows first-hand how the Advanced Success Plan for 麻豆原创 Customer Experience solutions can drive meaningful business value, especially for organizations without deep technical or functional 麻豆原创 CX expertise in-house. Utility companies face a specific set of challenges: complex billing architectures, regulatory requirements, and high customer expectations for responsive service. The right services delivered at the right moment can be genuinely transformational. Four patterns stand out consistently:

1. Bridging the knowledge gap for business users: Unlike system integrators or technical consultants, many utility business teams may not fully grasp the functional nuances required to maximize their 麻豆原创 investment. Value-driven customer success manager engagement becomes critical here by translating technical possibilities into business outcomes, advising on feature adoption, and ensuring cross-team alignment across operations, IT, and customer management. The customer success manager acts as connective tissue between what 麻豆原创 CX can do and what the business needs to achieve.
2. Go-live checks are the foundation for seamless launches: Utilities cannot rely on generic go-live checklists. Are meter reads flowing end-to-end? Are billing determinants and rate structures properly mapped? Are customer service orders integrated with billing and meter management systems? Comprehensive go-live checks built for the utilities context eliminate the guesswork, giving teams confidence that key processes are functioning correctly from day one, not discovered to be broken three months into the first billing cycle.
3. Road map guidance enabling better design decisions: Each 麻豆原创 CX release brings dozens of new features, but only subsets are relevant for a given customer in a given market. Regular, curated road map guidance helps utilities focus on the features and design decisions that will most efficiently drive their specific outcomes, whether that is improving customer satisfaction, streamlining operations, or enriching digital self-service. This targeted approach prevents costly missteps and empowers business stakeholders to make confident decisions without requiring deep product expertise of their own.
4. Early-phase support building for reliability and performance: The early project phase is often when performance issues and integration risks are best addressed, but it is also where internal teams feel least certain. Proactive support that includes performance reviews spanning interconnected billing and service systems, high-level architecture guidance, and utilities-specific load testing helps ensure reliable operation under real-world peak conditions. Utilities processing millions of daily meter reads, running mass billing cycles at month-end, and handling seasonal demand spikes need to know their system will hold before the peak arrives, not after it has passed.

What stronger 麻豆原创 CX adoption looks like in practice

When utilities customers engage with the full Advanced Success Plan for 麻豆原创 Customer experience solutions at the right moments, the outcomes can be concrete and measurable:

• Adoption confidence: Teams understand the 麻豆原创 CX capabilities that matter for their industry and know how to use them. Adoption is driven by guided enablement, not trial and error.
• Configuration quality: Solution reviews and functional assistance can ensure complaint workflows, billing notifications, and service processes are correctly configured, reducing workarounds and support volume.
• Release relevance: Every quarterly 麻豆原创 CX update is assessed for utilities relevance. Teams receive a focused brief on what to adopt, not a generic list of all product changes.
• AI activation at pace: Joule, embedded AI agents, and 麻豆原创 Engagement Cloud intelligence features are activated with expert assistance, helping to move from available-in-catalogue to live-in-production for utilities use cases.
• Integration reliability: Standard integrations between 麻豆原创 Service Cloud and 麻豆原创 S/4HANA Utilities are correctly configured, giving agents real-time access to meter history, billing data, and service orders.
• Transformation clarity: Transformation advisory connects the 麻豆原创 CX road map to the outcomes utilities care about most: cost-to-serve reduction, complaint resolution improvement, and regulatory compliance.

A service portfolio built for the complexity of utilities

The 麻豆原创 investment in the utilities sector is substantial and well-established. Utilities organizations around the world have already deployed the 麻豆原创 CX portfolio and are running their customer operations on it. The question is not whether they have access to world-class customer experience capabilities, but whether they are fully using what they have.

The eight services described above, combined with customer success manager-led perspectives on go-live quality, road map relevance, and performance assurance, represent a comprehensive framework for utilities organizations to realize the full potential of their 麻豆原创 CX investment.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions is not a generic offering. When applied with industry focus and customer success manager expertise, it becomes a strategic asset, one that helps utilities deliver on the promise of a superior customer experience in an increasingly competitive, regulated, and technically complex market.

Rajeev Ranjan is product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

For many 麻豆原创 Commerce Cloud and 麻豆原创 Engagement Cloud customers, this gap shows up in familiar ways: recommendation engines that surface generic results because behavioral data is not connected, e-mail campaigns timed by calendar rather than by individual habit, loyalty programs that reward transactions rather than relationships, and personalization rules that require significant manual effort to maintain at scale.

The ambition is there; the infrastructure to fulfill it, however, is often partial. Clean data is siloed, AI capabilities are underutilized, and the organizational discipline to run ongoing experimentation does not yet exist.

This is precisely the problem the version for is designed to solve.

What hyper-personalization actually requires

True hyper-personalization is not a feature you switch on. It is a capability you build systematically across three interdependent layers: data, decisioning, and delivery.

Data is the foundation. Hyper-personalization requires unified, consent-aware, real-time customer profiles consolidated across commerce transactions, engagement history, browsing behavior, service interactions, and loyalty activity. Without this foundation, even the most sophisticated AI models are operating on incomplete signals.

Decisioning聽is where AI translates those signals into action鈥攖he next best product to surface, the right offer to present, or the optimal moment to reach out. This layer requires not just model accuracy but governance, knowing when to trust the algorithm and when human judgment should override it.

Delivery聽is where the personalized experience reaches the customer, at the storefront, in the inbox, through a mobile push, or across a loyalty interaction. This layer requires orchestration across channels, consistent with the customer’s current context.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions helps address all three layers simultaneously, providing the expert guidance, governance frameworks, and adoption acceleration needed to move from point capabilities to an integrated operating model.

Hyper-personalization in 麻豆原创 Commerce Cloud

麻豆原创 Commerce Cloud can provide the storefront execution layer for personalization at scale. The solution’s AI-assisted product recommendations capability enables organizations to show the most relevant products to each visitor at the right point in their shopping journey, from trending products and related items to complimentary products that support cross-sell and upsell motions. This can go beyond manual merchandising rules; it can respond dynamically to real-time behavioral signals, helping to improve conversion performance and drive product discovery at a scale no merchandising team could replicate manually.

Yet many 麻豆原创 Commerce Cloud customers have not yet activated the full depth of these capabilities. The blockers are predictable: data quality gaps that limit recommendation model performance, integration complexity between the commerce layer and upstream profile data, and an absence of the experimentation discipline needed to tune and improve models over time.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions can bring targeted guidance to help address these barriers. Data readiness assessments can establish the quality baselines and integration patterns required to feed reliable signals into 麻豆原创 Commerce Cloud’s personalization engine. Adoption accelerators help teams operationalize experimentation, defining hypotheses, running A/B tests, and translating results into durable configuration changes. The outcome is a storefront that can continuously learn and improve, rather than one frozen at the point of initial configuration.

Hyper-personalization in 麻豆原创 Engagement Cloud

麻豆原创 Engagement Cloud, powered by 麻豆原创 Emarsys, can extend personalization beyond the storefront and into the full lifecycle of the customer relationship. This is where 麻豆原创 Commerce Cloud’s transactional signals combine with engagement history to help power cross-channel personalization that is individual rather than segment-based.

The solution’s AI-assisted send time optimization capability is a direct example of this philosophy in practice. Rather than sending campaigns on a fixed schedule, the capability can analyze each contact’s behavioral patterns鈥攊ndependently of time zone, language, or region鈥攁nd deliver messages at the precise time each individual is most likely to engage. This is not personalization as a concept; it is personalization as an automated, scalable operational process.

Paired with the 麻豆原创 Emarsys, AI-assisted campaign translator capability and omnichannel orchestration, 麻豆原创 Engagement Cloud enables marketing teams to move from building campaigns to orchestrating journeys where the system is continuously learning which signals should trigger which interactions and adapting those interactions based on what drives response.

The native integration between 麻豆原创 Commerce Cloud and 麻豆原创 Engagement Cloud is a critical accelerator here. By unifying commerce behavior and engagement data, organizations can drive increases in conversion rate, purchase frequency, and average order value in ways that neither system could achieve independently. The Advanced Success Plan for 麻豆原创 Customer Experience solutions helps customers realize this joint value by aligning integration architecture, data governance, and adoption milestones across both products within a single, coordinated engagement model.

How the Advanced Success Plan enables continuous improvement

Hyper-personalization projects are often treated as one-time implementations. The Advanced Success Plan for 麻豆原创 Customer Experience solutions is designed to make them repeatable, continuously improving programs. This means:

• Outcome-based governance: Co-defining the KPIs that matter, such as conversion rate lift, repeat purchase rate, engagement open rates, and average order value, and building work streams aligned to move them measurably.
• Prescriptive adoption patterns: Structured playbooks for activating AI-assisted recommendations, send time optimization, and next-best action logic, with clear milestones and measurable gates.
• Continuous enablement: Role-based coaching for the teams responsible for data, product ownership, and campaign operations, closing skills gaps that otherwise cause personalization programs to plateau or regress.
• Proactive telemetry: Regular adoption checks that surface underperforming configurations before they impact business outcomes, and AI-guided best practices that inform ongoing tuning.

Making the business case concrete

For 麻豆原创 Commerce Cloud customers, the value of operationalized hyper-personalization can be seen in storefront metrics: higher conversion from AI-surfaced recommendations, increased average order value through intelligent cross-sell, and improved product discovery that reduces bounce and exit rates.

For 麻豆原创 Engagement Cloud customers, the value can be seen in engagement quality: open rates and click-through rates that reflect individual relevance rather than list-wide broadcast, improved campaign ROI through AI-optimized delivery, and loyalty program engagement that reflects relationship depth rather than transaction volume.

Across both, the compounding effect of unified data and orchestrated decisioning is what transforms hyper-personalization from a POC into a sustained growth mechanism, one that gets measurably better over time.

Payal Sachdev is product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

Travelers nowadays have very strict requirements. From booking a ticket to arriving at a destination, they expect fast, convenient, and prompt assistance when they face issues along the way. No matter how perfectly a system is designed to meet needs, there will always be situations that can鈥檛 be avoided. Delays, confusing booking systems, long customer service wait times, and much more create frustration for travelers.

AI is reshaping how the travel and transportation industry is doing business. It delivers more avenues to provide customer care aside from the typical communication channels such as e-mail, short messaging services, and social media. AI provides smarter, faster, and more personalized customer experiences, leading to happier customers and therefore growth in revenue for the business. AI is no longer optional, it is now becoming a necessity.

Beyond tickets and timetables: how AI orchestrates the customer journey

Previously, travelers preferred travel agents over booking apps, relied on printed tickets, and valued personal service and human interaction. However, mobile apps for bookings, check-ins, and payments are now widely used. Travelers also expect real-time updates and personalized recommendations that provide seamless, end-to-end experiences.

麻豆原创 delivers an ecosystem that can provide the tools needed to meet these expectations. Using 麻豆原创 Service Cloud, organizations can manage cases, complaints, and information requests efficiently. AI can respond within seconds, unlike traditional customer service processes that rely on manual handling of support tickets. AI-powered chatbots can also manage a high volume of customer conversations simultaneously. Here is an example integration strategy:

Intelligent selling services for 麻豆原创 Commerce Cloud

Intelligent selling services for 麻豆原创 Commerce Cloud are AI-powered services that leverage machine learning and artificial intelligence to help deliver personalized customer experiences and optimize booking strategies. These services help analyze customer booking patterns, provide contextual data across customer touchpoints, and offer recommendations that can lead to increases in revenue.

Travel accelerator

The travel accelerator for the 麻豆原创 Commerce solution is an industry-specific solution designed to enable travel companies to deliver omnichannel digital traveler engagement through 麻豆原创 Commerce Cloud. For customers that already have an existing 麻豆原创 Commerce Cloud solution, they can use the travel accelerator to help tailor it for travel business demand. It can provide real-time information to offer personalized customer experiences and reinforce customer loyalty.

Loyalty management program through integration

Organizations may need a system to reward customers for coming back, like earning points, perks, or special treatment when you repeatedly book with the same travel company. For this, an integration to a loyalty management program, either 麻豆原创 Customer Loyalty Management or a third-party solution, can be used.

The way forward

can help you achieve your business goals. As a starting point, we can create a service engagement plan that provides a tailored approach to meeting your KPIs. During this phase, we also deliver sessions to help you set up 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Commerce Cloud while working to ensure that travel and transportation industry best practices are followed.

With AI capabilities available across every solution, you can now categorize your customer base based on travel behaviors and patterns, as well as perform sentiment analysis on customer reviews and support tickets. The Advanced Success Plan can serve as a strategic partner in helping achieve AI objectives. Our experts, backed by deep industry knowledge, can provide guidance on the most effective path forward.

To deliver services that are aligned with each customer’s specific goals, we have organized our offerings into four phases: implementation, pre-go-live, post-go-live, and continuous improvement.

Implementation

During the implementation phase, our focus is on providing adoption guidance to help set up the solutions, from front-end applications to back-end systems. We work alongside the team to establish core capabilities needed for a successful implementation and to help ensure the solution is aligned with business requirements.

This includes, but is not limited to, application user management, key user extensibility, the 麻豆原创 CX AI Toolkit, integrations, security considerations, and other essential platform capabilities. Our goal is to help build a solid foundation that supports scalability, maintainability, and future growth while enabling teams to get the most value from the platform.

Pre-go-live

In the pre-go-live phase, our focus is to validate and safeguard the solutions that have been built throughout the implementation. The goal is to make sure systems are configured correctly, performing as expected, and ready at go-live.

This includes conducting detailed reviews of business configuration settings, evaluating system performance, validating integrations, reviewing security and user access setups, and assessing analytics and reporting capabilities. We also help identify potential risks, gaps, or areas for optimization before launch, working to ensure issues are addressed proactively.

In addition, we work with teams to confirm readiness across key functional and technical areas, helping ensure that testing has been completed successfully, critical business scenarios have been validated, and the solution is aligned with operational requirements. Performing an adoption checkpoint during this phase helps reduce risk, improve system stability, and support a smoother go-live experience.

Post-go-live

During the post-go-live phase, we work closely with the team to help ensure that recommendations and best practices identified throughout the implementation have been properly configured and are delivering the intended results.

As users begin working in the production environment, new questions, opportunities for optimization, and minor challenges often emerge. During this stage, we provide continued guidance and support to help address those items, whether they are related to business processes, system configuration, integrations, extensibility, analytics, or overall solution adoption.

Our functional and technical experts remain available to review issues, provide recommendations, and help navigate any areas that require additional attention. We also help identify opportunities for further improvements and knowledge transfer, working to ensure the organization is well-positioned to maintain, enhance, and scale the solution over time.

Continuous improvement

Finally, as part of the continuous improvement phase, we help stakeholders remain informed about new innovations and enhancements introduced through 麻豆原创 release cycles. By staying up-to-date with the latest capabilities, the team can continue to maximize the value of the solutions and drive ongoing business success.

Tara Tracey is global product owner of the Advanced Success Plan at 麻豆原创.
Geoffrey Arado is product manager for 麻豆原创 Customer Experience.

They see a personalized ad, explore a product through an AI assistant, respond to an offer in their inbox, and open your app expecting everything to simply work. They expect relevance, continuity, and immediacy across every interaction in their entire journey.

Instead, they hit friction:

• They鈥檙e asked to repeat information they already shared.
• Promotions don鈥檛 reflect inventory availability.
• Fulfillment updates arrive late or not at all.
• And when support is needed, no one has the complete picture.

These are not isolated breakdowns. They are systemic customer experience failures.

In the era of agentic AI, those moments compound quickly. Customers now assume brands understand who they are, what they need, and what is happening in real time, and they expect businesses to act on that intelligence instantly across the entire customer journey.

The agentic era is accelerating this shift dramatically.

This goes beyond surfacing insights or recommendations. Agentic AI systems are increasingly capable of planning, reasoning, decision-making, and coordinated action across networks of agents. AI is fundamentally reshaping how customer experiences are created, delivered, and optimized in real time.

But it also exposes a fundamental truth: when AI moves faster than your data, systems, and processes, it reveals everything that鈥檚 broken.

That tension鈥攂etween rising customer expectations and disconnected CX reality鈥攊s exactly what 麻豆原创 and Google Cloud are solving together with .

The customer experience reality: ambition outpacing execution

Most organizations want to deliver seamless, connected experiences, but they struggle to operationalize those moments.

According to 麻豆原创 research, 78% of businesses say AI will be essential for retaining customers in 2026. Yet fewer than two in five share their customer data across CX (37%) or CRM (39%) platforms.

This is an enterprise operations problem, and its impact is felt across the entire CX business. And when these touchpoints are disconnected, the customer feels it first. And by the time the company feels that friction, it may be too late to win that customer back.

A new model for customer experience built on trusted enterprise data

麻豆原创 Customer Experience plays a key role in the expansion of 麻豆原创 and Google Cloud’s partnership to enable a fundamentally new approach to CX鈥攐ne that connects data, AI, engagement, and commerce in real time.

鈥淎gentic customer experience starts deeper in the stack than the customer ever sees鈥攊n the quality of the data, the way processes run, and the strength of the platform underneath. With 麻豆原创 and Google Cloud, we鈥檙e building that foundation together, so AI moves beyond insight to action. This delivers continuous, intelligent experiences with the control, context, and execution required at enterprise scale.鈥�

Muhammad Alam, Member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering

麻豆原创 Commerce Cloud endorses Universal Commerce Protocol: powering the next era of agentic commerce

As digital commerce enters the age of AI-driven experiences, a new standard is emerging to reshape how consumers discover and purchase products.

The Universal Commerce Protocol is an open standard designed to enable AI agents to manage the entire shopping journey, from product discovery to checkout and even post-purchase support.

Unlike traditional commerce integrations that rely on fragmented APIs and bespoke connections, UCP creates a shared language for retailers, payment providers, and AI systems. This allows intelligent agents to interact directly with commerce platforms, dramatically simplifying how products are surfaced, evaluated, and purchased.

In essence, UCP transforms commerce from a series of disconnected steps into a unified, agent-driven experience.

, long known for powering enterprise-grade digital commerce for global brands, is now aligning with this new paradigm. As part of the growing UCP ecosystem, 麻豆原创 plans to work with Google to enable its customers to participate in AI-native commerce experiences that extend beyond traditional storefronts and help its merchants鈥� products be discovered and purchased across the Gemini app and Google Search, including AI Mode.

鈥淥ur goal with UCP is to build an open, trusted standard for the future of AI-driven commerce,鈥� said Ashish Gupta, VP/GM, merchant shopping at Google. 鈥淗aving a leader like 麻豆原创 endorse the protocol is critical as we work toward bringing seamless, secure agentic commerce to everyone.鈥�

For merchants, this means dramatically lower integration costs, faster onboarding into new AI-driven channels, and the ability to reach new customers beyond their storefront experiences.

In addition, 麻豆原创 Commerce Cloud will leverage Google Gemini capabilities to power a Shopping Assistant that brands can deploy directly to their own customers. This enables organizations to offer a real-time, AI-driven shopping experience across their digital touchpoints. The Shopping Assistant engages shoppers through chat, voice, and text to help them discover products, answer questions, and turn intent into transactions.

Unlike traditional storefront interactions, the Shopping Assistant can create a persistent, conversational experience that follows the customer across the shopping journey, continuously refining recommendations and guiding decisions in real time.

It can also curate creative ideas鈥攕uch as themed outfits or complete event concepts鈥攂y intelligently combining products based on customer requests. By unifying behavioral signals, real-time inventory, and promotional intelligence, it can increase conversion rates, improve average order value, and ensure every recommendation is both relevant and fulfillable.

麻豆原创 Engagement Cloud and Google Cloud: how agents work together for marketing

This new expanded partnership comes alongside another historic milestone for 麻豆原创 and Google Cloud, announced earlier this year. A fundamentally different approach to marketing execution is now offering marketers a new model for engagement that is built on trusted enterprise data.

By combining customer data and real-time signals like inventory, orders, and fulfillment status with operational truth, marketing teams can now build, launch, and optimize personalized customer engagements, grounded in business context and executed at scale through an autonomous multi-agent framework.

At the heart of this partnership:

• 麻豆原创 Business Data Cloud聽(麻豆原创 BDC) connects semantically rich data across the enterprise with AI to help enable real-time insights and drive personalized interactions grounded in business context. This includes 麻豆原创 Business Data Cloud Connect for Google BigQuery with bidirectional, zero-copy data access and built-in enterprise-grade governance and security.

• Google BigQuery聽unlocks real-time signals across the Google ecosystem, such as geolocation, weather, ad engagement, and rich analytics, for AI-driven segmentation, personalization, activation and analytics.

• 麻豆原创 Customer Experience聽solutions can provide the real-time behavioral context: customer profiles, transactions, orders, service interactions, and consented engagement data.

• 麻豆原创 Engagement Cloud can activate enterprise data, AI insights, and predictions through intuitive tools and AI agents to help securely orchestrate real-time, personalized interactions across the entire customer life cycle.

Why this partnership matters

The collaboration between 麻豆原创 and Google reflects a broader shift in how commerce and marketing teams operate.

For commerce leaders:

• From search to agents: Consumers are no longer just searching. AI agents are acting on their behalf, making decisions, and completing purchases.
• From channels to ecosystems: Commerce is moving beyond owned channels into distributed, AI-powered environments like search, assistants, and chat interfaces.
• From integration to interoperability: Open standards like UCP eliminate the need for one-off integrations, enabling scalable participation in the AI economy.

With UCP, AI agents can seamlessly access product catalogs, manage carts, process payments, and handle post-purchase workflows, all without forcing retailers to rebuild their infrastructure.

For marketing leaders:

• From prompt to performance: Agentic intelligence becomes operational where business goals, enterprise signals, and marketing processes direct AI agents, translating into real customer interactions and automated lifecycle journeys.
• From manual to generative: Advanced generative capabilities powered by Google Gemini models, such as Nano Banana 2, introduce new agentic skills that help marketing teams dynamically generate messaging, imagery, and campaign variations.
• From dark data to unified data context: With every interaction grounded in business context and continuous engagement signals, messages become truly dynamic. Text messages can turn into immersive conversations with Google Rich Communication Services (RCS) and advertising creative and offers can continuously evolve.

Agents collaborate across 麻豆原创 and Google Cloud to personalize, activate, and continuously optimize campaigns in real time across engagement channels and media networks.

鈥淲ith this partnership, 麻豆原创 and Google Cloud bring together connected AI and a unified data foundation to create real-time understanding of the customer and business context. This enables organizations and CX teams to move from fragmented interactions to continuous, intelligent execution鈥攅mbedding AI into end-to-end processes and unlocking meaningful gains in productivity, speed, and business impact.鈥�

Jan Gilg, Global President Customer Success & Americas, Member of the Extended Board

Unlocking new value for enterprises

For 麻豆原创 Commerce Cloud customers, this partnership can unlock several strategic advantages:

• Increased discoverability in AI-driven shopping experiences
• Faster time-to-market through standardized integrations
• Ownership of customer relationships, even in third-party AI environments
• Future-proof architecture aligned with emerging commerce standards

As AI continues to compress the distance between intent and transaction, accessibility to agents becomes just as important as visibility in search results. In this new model, success in commerce is no longer defined by storefront experience alone鈥攊t鈥檚 defined by how effectively your products, data, and systems can be accessed, interpreted, and transacted on by AI agents.

For customers, this partnership unveils a new network of interoperable AI agents, grounded in enterprise data and shared context聽across 麻豆原创 and Google. Organizations can achieve measurable outcomes, including:

• Faster speed-to-market through autonomous campaign and content generation
• Lower operational overhead by eliminating manual execution steps
• Always鈥憃n optimization that continuously improves performance
• Higher ROI through relevant, timely, and consistent engagement at scale

Marketers can spend less time managing workflows and more time shaping strategy, creative direction, and customer value.

鈥淲hat matters is delivering real value to our customers. As customer experience becomes more agentic, organizations need to move faster, stay connected, and operate consistently across every interaction. By bringing commerce, marketing, and service together, we help our customers reduce complexity, respond more quickly, and deliver more relevant experiences that strengthen engagement and drive sustainable growth.鈥�

Manos Raptopoulos, Global President Customer Success Europe, APAC, Middle East & Africa and Member of the Extended Board 麻豆原创 SE

The future of AI-driven commerce and marketing: what this means for your CX strategy

The partnership between 麻豆原创 and Google for marketing and commerce聽marks a foundational shift toward what many are calling an agentic revolution鈥攁 world where AI doesn鈥檛 just assist CX teams and shoppers but actively participates in the buying process and shapes their customer experience.

For enterprise CX leaders, the message is clear: success in this new era will depend on how well your commerce and marketing platform can communicate with AI agents. With 麻豆原创 and Google Cloud, 麻豆原创 Customer Experience is positioning itself鈥攁nd its customers鈥攖o thrive in that future.

Across industries, customers are moving from pilots to production. They are investing in systems that can run pricing, orders, fulfillment, and service end-to-end, with AI actively driving decisions and outcomes. They are not looking for more tools. They are looking for results.

This is where 麻豆原创 has a clear point of view. Customer experience (CX) only works when it is connected directly to execution.

麻豆原创 Customer Experience is integrated with 麻豆原创 Cloud ERP across pricing, order management, fulfillment, billing, and service. AI operates inside these processes using real business data. That means every customer interaction can reflect what the business can deliver.

This is the shift now underway, and it is creating immediate opportunities for partners.

麻豆原创 partners are the ones who bring this to life. They take product capabilities and turn them into working solutions that improve conversion, increase fulfillment accuracy, and reduce service cost.

The next wave of growth will be led by partners who move quickly and build on this foundation.

Customer experience is now measured by what gets done

Customer experience is no longer judged by engagement metrics alone. It is judged by outcomes. Customers expect:

• Accurate pricing at the moment of purchase
• Real product availability, not estimates
• Orders that are fulfilled as promised
• Service that resolves issues without repetition or delay

When these things work, the experience works. When they fail, the problem is immediately visible.

AI is increasing the speed of every interaction. It is also exposing execution gaps faster than ever before. If pricing, inventory, or order data are inconsistent, customers see it instantly.

This is why customer experience and execution can no longer be separated.

AI is now driving actions, not just insights

AI is already acting inside key business processes. As seen in the , in 麻豆原创 CX today:

• Marketing, content, and campaign assistants can orchestrate segmentation, content creation, and optimization based on live performance signals.
• Commerce, merchandising, shopping, and order management assistants can connect discovery, conversion, and fulfillment to real-time inventory and pricing.
• Sales assistants help guide deal qualification and deal execution by linking pipeline signals to pricing, availability, and fulfillment data.
• Case and service management assistants help automate routine interactions while maintaining full context across orders, entitlements, and history.

These are not future scenarios. These capabilities are available and in use. But they only work when they are connected to trusted business data.

Without that, AI creates errors at scale. With it, AI drives measurable improvement.

Autonomous CX connects experience to execution

Autonomous CX connects core products and processes across the business, operating on a shared business context. It brings together 麻豆原创 Commerce Cloud, 麻豆原创 Sales Cloud, 麻豆原创 CPQ, 麻豆原创 Service Cloud, 麻豆原创 Field Service, 麻豆原创 Engagement Cloud for marketing, and 麻豆原创 Cloud ERP across finance, supply chain, and order management.

This is not a set of disconnected applications. It is a unified system where customer interactions and operational processes run on the same data foundation. Pricing, inventory, orders, and service are consistent across every touchpoint.

As a result, AI can move from recommendation to execution, working to ensure that every interaction is grounded in what the business can deliver. It can also remove the integration gaps that slow down CX execution.

麻豆原创 CX partners are moving faster from projects to outcomes

This shift is changing what customers expect from partners. Customers are not asking for system implementations. They are asking for outcomes such as:

• Faster time to deploy
• Higher conversion rates
• Improved order accuracy
• Lower cost to serve

麻豆原创 provides a strong starting point with embedded assistants, standard integrations, and prebuilt industry scenarios. Partners are building on this to deliver complete solutions. This is where differentiation happens.

Where partners are creating value today

The opportunity is not theoretical. It is already visible in active partner work.

Across 麻豆原创 CX:

• The cloud ERP edition of 麻豆原创 Commerce Cloud can connect storefront, pricing, ordering, and fulfillment in one model.
• 麻豆原创 Revenue Growth Management and 麻豆原创 Retail Execution support trade planning and in-store performance.
• Intelligent applications help package AI use cases across marketing, sales, and service.
• 麻豆原创 Service Cloud with partner integrations such as Parloa enables automated, context-aware service interactions.

麻豆原创 CX partners are turning these capabilities into repeatable offerings. Examples include:

• Industry packages for retail and CPG combining commerce, pricing, and fulfillment
• Preconfigured deployments of 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud that reduce time to go-live
• Integration connectors linking 麻豆原创 CX with existing commerce, loyalty, and service platforms
• Extensions to CPQ and sales workflows that improve deal margin and approval speed
• Service automation scenarios that reduce manual case handling using real order and entitlement data
• AI-driven discovery connected directly to 麻豆原创 Commerce and 麻豆原创 Commerce, order management

These are practical, deployable solutions that can deliver measurable results.

The ecosystem is expanding what鈥檚 possible

麻豆原创 is strengthening this model through partnerships. Recently announced partnerships with companies such as Amazon Web Services, Google Cloud, Parloa, and Vercel enable new interaction models like conversational commerce, AI-driven search, and composable digital experiences.

What matters is that these experiences connect back to 麻豆原创 for execution. Orders, pricing, fulfillment, and service remain consistent across every channel. This gives partners the freedom to innovate on the experience layer while relying on 麻豆原创 for reliable execution.

A new economic model for partners

The economics for partners are changing. With Autonomous CX, partners can build:

• Industry solutions that can be reused and scaled
• Implementation packages that shorten delivery timelines
• Extensions and integrations that apply across customers
• Ongoing services for AI optimization and governance
• New offerings built around AI assistants, AI agents, and orchestration
• Higher-value transformation programs that combine AI, data, and process design

This creates a more predictable and repeatable revenue model. It also strengthens long-term customer relationships.

Now is the time to act with 麻豆原创 CX

Customers are making decisions now. They are selecting platforms and partners that can deliver AI-driven execution across customer experience. They are looking for partners who can:

• Connect CX to ERP processes
• Deliver solutions that work out-of-the-box and scale
• Improve measurable business outcomes

麻豆原创 provides the foundation. The platform is in place. The capabilities are real. The next step is execution.

Partners who move now can define the use cases, build the offerings, and lead in their industries. The momentum is already building. This is the moment to accelerate it.

What partners should do next

To move from opportunity to execution, partners can act now.

• See what is available today. Explore the latest AI-driven CX capabilities and partner opportunities in the .
• Understand the foundation for Autonomous CX. Learn how 麻豆原创 connects experience to execution with the .
• Deepen expertise and accelerate readiness. Gain a stronger understanding of Autonomous CX and partner use cases:
• Engage now and shape the next wave. Join the upcoming executive briefing to understand how to position and deliver these solutions:
  • June 18:
• Strengthen credibility and scale impact. Advance your go-to-market and positioning through certification:

麻豆原创 is not asking partners to start from scratch. The platform, capabilities, and ecosystem are already in place. The opportunity now is to build, differentiate, and lead.

Karl Fahrbach is chief partner officer at 麻豆原创.
Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience.

The results, which are driven by authentic customer feedback, highlight the strength of 麻豆原创 CX, particularly in the enterprise segment.

鈥淓arning a Leader position in a G2 Report is highly competitive and rooted in verified customer reviews,鈥� said Godard Abel, co-founder and CEO, G2. 鈥淐ongratulations to 麻豆原创 Customer Experience for achieving this distinction. Buyers can be confident this ranking reflects the authentic experiences of real users.鈥�

Following are a few standout achievements:

• : Reigned supreme in the commerce space, achieving the coveted #1 Rank in the Enterprise Grid庐 Report for Order Management. It was also recognized as a Leader for E-Commerce Platforms and Omnichannel Commerce.
• : Showcased its versatility and power, earning a Leader position in multiple reports for CRM and Sales Analytics. It secured an impressive #2 Rank in the Enterprise Grid庐 Report for CRM.
• : Named a Leader in the highly competitive Enterprise Grid庐 Report for Help Desk, solidifying its position as a top-tier solution for customer service excellence.
• : Dominated the marketing and engagement space, earning a whopping 51 G2 Summer 2026 badges, including #1 in the Momentum Grid庐 for Location-Based Marketing and #1 in the Enterprise Grid庐 for Email Deliverability, up from #3. Also recognized as a Leader in Marketing Automation, Personalization Engines, Customer Journey Analytics, SMS Marketing, and Loyalty Management, with new country reports added across Spain, Italy, and France.

This verified success is powered by pioneering work in agentic AI with Joule, 麻豆原创鈥檚 generative AI copilot. They鈥檝e moved beyond simple automation to deploy autonomous AI agents that can reason, plan, and act across the entire customer journey.

Customers are enthusiastically adopting these intelligently automated capabilities. The is transforming keyword searches into conversational discovery and guided purchases, while the is autonomously resolving inquiries, improving satisfaction, and reducing contact center load.

The shift to AI-driven engagement is delivering real-world value and simplifying complex processes. Using , customers can embed AI-powered guidance directly into workflows, transforming employees into experts. By enabling teams to work smarter and faster across 麻豆原创 Commerce Cloud, 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Engagement Cloud, unlocking the full potential of your 麻豆原创 investment and delivering the exceptional outcomes that customers demand has never been easier. By automating routine and complex workflows with out-of-the-box AI agents, teams are freed to focus on strategic, high-value customer interactions, driving unprecedented efficiency and loyalty.

G2 SUMMER 2026 Grid庐 Reports: What 麻豆原创 CX customers are saying

麻豆原创鈥檚 commitment to delivering exceptional customer experiences is reflected in the high praise for 麻豆原创 Commerce Cloud, 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Engagement Cloud. This recognition is a true testament to the trust our customers place in 麻豆原创 CX when it comes to powering their most critical business functions.

Here鈥檚 what users said about 麻豆原创 Commerce Cloud:

鈥淚 like 麻豆原创 Commerce Cloud’s ability to handle extreme complexity while unifying diverse business models (B2B, B2C, B2B2C) on a single platform. Its unified approach allows us to manage operations on a single technology stack, eliminating data silos, reducing operation costs, and giving us total visibility into our entire supply chain.鈥�

– Suryakant G, Managing Director ()

鈥淭he onboarding resources and 麻豆原创 support ecosystem make implementation easier, while its AI-driven recommendations, search optimization, and customer behavior insights help deliver more personalized experiences and smarter business decisions.鈥�

– Vedant G., Manager ()

Here鈥檚 what users said about 麻豆原创 Sales Cloud:

鈥淲hat really clicks for me with 麻豆原创 Sales Cloud is how effortlessly it syncs sales and marketing data in one smart dashboard. It gives me crystal-clear insights into customer behavior and pipeline status, which is pure gold for crafting targeted, automated email campaigns that feel timely and personal.鈥�

– Grecia L., Marketing Automation Specialist ()

鈥淚t addresses inefficiency and lack of visibility in the sales cycle. The main benefit is a faster, more predictable revenue stream, supported by AI and real-time data integration.鈥�

– Kuldeep D., Senior Technical Specialist ()

Here鈥檚 what users said about 麻豆原创 Service Cloud:

鈥淚 appreciate its automated processes and great SLA tracking, which helps the team to keep track of time and not miss deadlines. I also like the AI replies that enable quick responses by searching through the knowledge base. The automatic ticket routing is a valuable feature as it aids in queue management, ensuring efficient handling of customer issues.鈥�

– Kelvin E., Support Engineer ()

鈥溌槎乖� Service Cloud addresses the challenge of fragmented customer service data by consolidating all interactions and cases into one platform. This gives me clearer visibility into customer history, helps resolve issues more quickly, and supports consistent omnichannel service.鈥�

– Rekha S, Content Creator ()

Here鈥檚 what users said about 麻豆原创 Engagement Cloud:

鈥淚 love how 麻豆原创 Engagement Cloud personalizes campaigns at scale and automates customer journeys using real-time data and AI insights.鈥� ()

Shaping the future of customer experience, together

The G2 leadership awards validate the 麻豆原创 CX strategy, while investment in agentic AI defines the future.

Users can share feedback on G2 for聽, for聽麻豆原创 Sales Cloud, and聽for 麻豆原创 Service Cloud.聽