Today鈥檚 finance leaders are expected not only to report on business performance, but also to help shape it. They need a foundation that connects financial and operational data, supports end-to-end processes, and helps teams move from insight to action with speed and confidence.

Recognized for cloud ERP for finance

For us, this recognition is about more than technology. In our opinion, it reflects the importance of a strong digital foundation that brings together financial and management accounting, operational context, trusted data, and embedded intelligence to help finance teams work more effectively.

At the heart of is a unified data model and common journal that can connect financial information with the business processes behind it. We believe this approach can help organizations reduce reconciliation effort, gain a more multidimensional view of performance, and give finance teams the context they need to make informed decisions.

As the role of finance continues to evolve, we remain focused on helping customers simplify core processes while creating the foundation for new capabilities in automation and AI. This combination of connected data, processes, and intelligence is central to our vision for the future of finance.

Connecting financial and operational decisions

Finance transformation increasingly depends on connecting processes across the enterprise. By bringing together finance with purchasing, sales, inventory, and other operations, organizations can better understand what鈥檚 driving financial outcomes and address issues in context.

麻豆原创 helps bring financial and operational processes together across the enterprise. We believe this approach can help finance teams improve transparency, strengthen governance, and resolve issues earlier, including during the financial close.

AI is becoming an important part of that connected experience. Joule and 麻豆原创鈥檚 growing portfolio of persona-based assistants are designed to work within business processes and established controls. These capabilities can help finance professionals identify anomalies, automate selected activities, address exceptions, and focus their expertise on higher-value work.

Recent innovations announced at 麻豆原创 Connect in 2026 build on this direction. New Joule Assistants for finance, 麻豆原创 Enterprise Financial Consolidation, and are designed to connect data and workflows more closely while supporting the governance and auditability finance teams require.

Supporting customer transformation

demonstrates how a connected finance foundation can support transformation at scale. Through its 鈥淕roup Finance Backbone鈥� initiative, the company worked with 麻豆原创 to harmonize finance operations across manufacturing, sales, and financial services. BMW Group established one integrated finance template supporting organization-wide reporting, standardized approximately 800 ground-level processes, and improved data comparability and granularity across 15 major source systems.

We believe customer outcomes like these demonstrate the importance of combining standardized processes, trusted data, and central governance to help global organizations improve transparency and prepare for future innovation.

Building the future of finance

Finance leaders are balancing a growing range of priorities, from improving operational performance and managing risk to preparing their organizations for AI-powered innovation. 麻豆原创 remains focused on helping them address those priorities with a unified foundation that connects data, processes, and people across the enterprise.

We are proud to be recognized as a Leader in this Magic Quadrant and remain committed to helping customers build finance organizations that are intelligent, connected, and ready for what comes next.

Learn more

To learn more about 麻豆原创鈥檚 position in the 2026 Gartner Magic Quadrant for Cloud ERP for Finance, .

Lawrence Martin is general manager & chief product officer for 麻豆原创 Finance & Spend.
David Imbert is chief marketing officer for 麻豆原创 Financial Management.

*Gartner, Magic Quadrant for Cloud ERP for Finance, 2026 by , , October 13, 2026

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

Finance leaders today are expected to do far more than report results. They鈥檙e expected to shape business outcomes, guide decisions, and respond quickly to change. Yet many finance processes still operate in isolation rather than in concert. Invoice approval and payment execution happen in separate places.

Planning, consolidation, and reporting often rely on separate systems and workflows. And governance often misses critical information by operating in a silo. The result is delays between identifying an issue, making a decision, and taking action.

This year at 麻豆原创 Connect, 麻豆原创 announced new capabilities designed to help close those gaps. These include , a new payment service embedded in 麻豆原创 Cloud ERP; 麻豆原创 Enterprise Financial Consolidation, a new addition to 麻豆原创鈥檚 enterprise performance management portfolio; and an expanded set of Joule Assistants that bring AI-powered support to finance, governance, and compliance processes.

While each innovation addresses a different challenge, they share a common objective. Finance teams should spend less time moving information between systems and more time acting on insights.

Embedding payments into finance operations

One of the most persistent sources of friction in finance is the payment process itself.

An invoice may be approved in the ERP system, but completing the payment often still involves banking portals, payment files, middleware, and reconciliation activities. Each additional handoff introduces complexity and can slow execution.

麻豆原创 Pay is designed to simplify that process by embedding payment execution directly into . When an invoice is approved, payment and reconciliation can occur within the same workflow and the same business context.

The launch also marks 麻豆原创鈥檚 entry into payment services through , a wholly owned 麻豆原创 subsidiary established earlier this year. As a regulated payment-service provider, Tereina enables 麻豆原创 to deliver services natively within finance workflows rather than relying solely on third-party providers. This helps keep payment execution, reconciliation, controls, and auditability connected to the underlying business transaction.

The service supports EFT, wire transfers, checks, and stablecoin-based settlement for cross-border transactions. More broadly, 麻豆原创 offers customers a wide range of payment options, spanning bank API integrations, connectivity to more than 10,000 financial institutions through SWIFT, digital-currency payments, and now natively embedded payment services.

For finance organizations, the opportunity is not simply faster payment execution. It is reducing the effort required to connect systems, manage exceptions, and reconcile information after the fact while keeping payment processing, reconciliation, controls, and auditability within a single workflow.

麻豆原创 Pay is now generally available in the United States and the United Kingdom.

Connecting performance management across the enterprise

The same challenge appears across enterprise performance management.

Planning, consolidation, close processes, and external reporting all rely on the same financial reality. Yet they are often managed through separate tools, data models, and processes. Maintaining a consistent view of performance can become increasingly difficult as organizations grow and business conditions change.

At 麻豆原创 Connect, we announced to help address that challenge.

Built on , the solution brings together statutory and management consolidation with close and disclosure capabilities on a common governed data foundation. By connecting these activities more closely, finance teams can spend less time reconciling information and more time understanding performance and responding to change.

The solution also includes new Joule Assistants for consolidation and disclosure processes. These assistants help identify anomalies, resolve exceptions, and support disclosure preparation while maintaining auditability and human oversight.

For organizations operating mixed environments–some 麻豆原创, some non-麻豆原创 environments–麻豆原创 Enterprise Financial Consolidation provides flexibility by supporting data from multiple sources. It also offers a path forward for customers looking to modernize existing consolidation environments and adopt a more AI-enabled approach to performance management.

麻豆原创 Early Adopter Care is planned for the first quarter of 2027.

Expanding AI across finance and governance

AI鈥檚 potential in finance extends beyond answering questions or generating summaries. The larger opportunity is helping teams complete work that has traditionally required significant manual effort, specialist knowledge, and cross-functional coordination.

At 麻豆原创 Connect, 麻豆原创 expanded its portfolio of Joule Assistants to address these opportunities.

The new assistants support areas including accounting, revenue recognition, pricing, disclosure management, project operations, governance, and trade compliance. Embedded directly into existing workflows, they are designed to help teams complete tasks more efficiently while preserving review processes and accountability.

Some focus on helping finance professionals understand complex calculations and reporting requirements. Others support pricing decisions, disclosure preparation, project execution, and continuous monitoring of risks and controls.

Just as important is governance.

As organizations deploy more AI agents across business functions, they need assurance that those agents operate within the same boundaries that govern human users. New governance, security, and compliance assistants are designed to extend existing controls, access management, and monitoring capabilities to AI-driven activities, helping organizations apply consistent oversight across both human and digital actors.

General availability for the Governance Assistant and International Trade Assistant is planned for Q4 2026.

From financial insight to immediate action

Finance has always played a central role in helping organizations understand performance. Increasingly, it is also expected to help organizations respond in real time.

That makes speed, visibility, and execution just as important as reporting accuracy.

The innovations 麻豆原创 announced at 麻豆原创 Connect reflect that shift. Whether simplifying payments, connecting performance management processes, or embedding AI directly into everyday finance work, each reduces the operational distance between identifying an issue and acting on it.

For finance teams, the ultimate goal is not simply faster processes or faster reports. It is making better decisions with greater confidence and executing those decisions with less friction.

Learn more about how 麻豆原创 is helping organizations modernize finance operations across ERP, planning, treasury, payments, and accounting .

Lawrence Martin is general manager and chief product officer for 麻豆原创 Finance & Spend.
David Imbert is chief marketing officer for 麻豆原创 Financial Management.

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

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

A new UI for AI 

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

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

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

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

麻豆原创 Autonomous Suite 

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

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

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

麻豆原创 Business AI Platform 

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

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

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

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

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

Jerseys from Jersey 

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

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

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

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

Security and governance 

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

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

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

A clear shift among customers 

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

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

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

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

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

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

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

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

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

麻豆原创’s Autonomous Enterprise Scales Up

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

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

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

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

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

The Platform Powering Business AI You Can Trust

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

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

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

麻豆原创 Brings Embedded Payments to Cloud ERP

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

Partners Expand the Reach of Autonomous Business

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

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

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

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Generative AI is moving quickly from experimentation to essential infrastructure. Employees have woven it into routine daily tasks, and teams and applications are leaning on it more heavily every quarter. That growth comes with a new cost category finance wasn鈥檛 designed to handle: AI token consumption.

When AI is writing code and powering agents, token consumption can outpace traditional planning processes. Finance leaders suddenly find themselves in unfamiliar territory, needing to bring discipline to AI spending without becoming the function that kills adoption. Getting the balance right means treating tokens as an enterprise resource that must generate returns commensurate with its cost.

At 麻豆原创, we have been working through these questions firsthand. Here鈥檚 what we鈥檝e learned from building, testing, and adjusting that framework.

You can鈥檛 manage what you can鈥檛 see

Most finance leaders wouldn鈥檛 manage a major cost category from a single line item, yet that鈥檚 exactly where companies start with AI. Total spend matters, but it won鈥檛 tell you which teams, workloads, or usage patterns are driving that spend, or whether the consumption is actually producing useful outcomes.

Getting that visibility requires real collaboration across commercial, engineering, finance, and product teams. Finance brings the forecasting questions and accountability framework. Other functions bring the operational context that makes the numbers meaningful.

In our experience, repeated forecasting cycles generally improved our financial models as we layered in more operational detail around usage. The broader lesson: when a cost category is new and fast-moving, don鈥檛 wait for precision before acting. Start with enough transparency to make better decisions, then sharpen the model as patterns emerge.

Someone has to own it

Our most consequential insight was philosophical rather than financial. We learned that visibility alone isn鈥檛 enough and that consumption needs an owner.

A centralized AI budget makes early experimentation easy, but it also disconnects the people spending tokens from any financial accountability for them. As AI becomes more deeply embedded in business processes, that model breaks down.

This isn鈥檛 an argument for immediately charging back every LLM call with forensic accuracy. It鈥檚 an argument for managing AI consumption the same way companies manage other enterprise resources such as software, external services, and labor. Give decision-makers a clear picture of what their teams are consuming and what outcomes that consumption is expected to produce.

At 麻豆原创, allocating token costs to business areas has shifted the conversation from 鈥淗ow much are we spending?鈥� to 鈥淲hat are we getting for this?” and “Is this the right place to invest more?鈥� That鈥檚 a healthier conversation.

Token spend is not a technology line item. It is a strategic and operational investment decision. The goal is to make AI spending intentional, not just cheap.

Cost per token is the wrong scorecard

A large AI bill draws attention, but optimizing purely on cost can lead to exactly the wrong decisions.

The more useful question is the relationship between consumption and business impact. An AI tool that meaningfully accelerates software development, reduces repetitive work, or improves customer service will carry real token costs. Cutting that usage simply because the line item is visible could destroy more value than it saves.

We saw this firsthand. After rolling out AI developer tools at 麻豆原创, we recorded a mid-double-digit percentage increase in pull-request merge rates, a clear signal that development work was moving faster. The consumption was worth it.

Finance needs a paired view: cost metrics alongside value metrics. Governance without that view risks optimizing for cost at the expense of value creation.

Guardrails should target waste, not adoption

As usage scales, controls become necessary, but the right controls are surgical, not sweeping. When we examined consumption patterns in detail, three root causes of disproportionate spend emerged: power-user and automated-agent concentration, model misalignment, and tool proliferation.

Those are the areas where guardrails earn their keep. Controls matter because they focus on the sources of avoidable spend rather than putting a blanket brake on usage. At 麻豆原创, our response centered on three levers: token capping to prevent runaway consumption, model routing to better match capability and cost to the task, and tool rationalization to eliminate redundancy and concentrate investment where utilization justified it. That work helped contain a triple-digit-million-dollar financial risk while keeping adoption moving forward.

The principle is simple: remove waste, preserve productive demand.

From cost control to value governance

AI isn鈥檛 going to pause for the next planning cycle. Its capabilities, usage patterns, and economics will keep shifting, and the governance model around it needs to keep pace.

The work is not complete. The next step is to embed these practices into regular planning and reporting, assign clearer ownership, improve allocation, and build forecasting capabilities that can anticipate where costs are heading before they arrive.

Companies that do this well will still have an AI bill to pay. But they will have the transparency and accountability to tell the difference between consumption that is creating value and consumption that isn’t and direct investment accordingly.

Lukas Deutsch is chief controlling officer at 麻豆原创.
David Imbert is chief marketing officer for 麻豆原创 Financial Management.

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.

That was the takeaway from a between 麻豆原创 CFO Dominik Asam and 麻豆原创 Fioneer CEO Matthias Tomann. Their conversation touched on topics like the future of , the role of AI, and the growing importance of integrated enterprise platforms.

For decades, banks and insurers have built operating models around regulatory fragmentation, country-specific requirements, layered systems, and continuous workaround solutions. As a result, the industry is running on patchwork architecture that is expensive to maintain, slow to change, and fundamentally misaligned with how AI works.

Partnership built for financial services innovation

Since joining forces in 2021, 麻豆原创 and 麻豆原创 Fioneer have significantly expanded their joint capabilities for the financial services sector. As Tomann highlighted in the conversation, the partnership has already delivered substantial momentum for 麻豆原创 Fioneer:

• R&D investment increased by 120%
• Annual software sales more than doubled
• Major customers successfully transitioned to 麻豆原创 Cloud ERP
• The platform evolved into a richer, more scalable, and highly capable ecosystem

Together, the companies are combining 麻豆原创鈥檚 trusted cloud and data infrastructure with 麻豆原创 Fioneer鈥檚 deep financial services expertise to help institutions simplify operations, modernize core systems, and prepare for the AI-driven future.聽

Executives from both companies will be exploring these critical topics further at their annual which is now open for registration.

AI is not the starting point, data integration is

Everyone wants AI, but AI can only create value from integrated data, real-time access, and standardized processes. But most financial institutions still operate on the opposite: fragmented foundations. That reality will define the winners over the next five years.

The organizations that succeed will not be the ones experimenting with the most models. They will be the ones that establish unified, trusted, real-time enterprise data with strong governance. That is the real competitive advantage.

But even that is only part of the story. The next phase is not just about using AI to analyze better; it is about AI executing work.

We are now seeing a fundamental shift: from systems that store and report information to systems that act on that information in real time, orchestrating end-to-end processes across the business. This marks the transition to the Autonomous Enterprise, 麻豆原创鈥檚 vision for the future of business where AI does not just support decisions but increasingly drives execution, within clearly defined guardrails.

Financial services can no longer afford 鈥減atchwork architecture鈥�

This shift makes one thing clear: The traditional approach to building IT landscapes is no longer viable.

For years, many financial institutions solved problems incrementally鈥攁nother point solution, another integration layer, another workaround. But eventually every workaround becomes technical debt and integration is the single largest IT cost category.

Tomann made clear during the conversation that the emphasis must be on simplification rather than adding more complexity.

What 麻豆原创 and 麻豆原创 Fioneer are driving is not another modernization cycle. It is a structural shift toward comprehensive, integrated platforms and AI driven processes that replace fragmentation, not sit on top of it.

The result is a scalable financial services platform where core banking, lending, reporting, insurance, and analytics operate within an integrated architecture instead of disconnected silos.

Real-time finance is becoming a strategic requirement

Real-time capability is becoming foundational to competitiveness鈥攚hether it鈥檚 risk management, regulatory reporting, customer experience, fraud prevention, treasury operations, or AI-driven decision making.

Institutions that can act on integrated data instantly will have a major advantage over those still moving information between disconnected systems overnight. With integrated data and AI embedded in core processes, finance is moving toward continuous financial intelligence:

• Forecasting becomes dynamic and always up to date
• Risk is detected and assessed in real time
• Closing processes become increasingly automated
• Decisions are guided by AI based on live business context

Increasingly, AI assistants and agents take over execution of finance processes, from planning and risk management to invoicing and financial close, under strict governance. The role of finance shifts from reporting on the business to steering the business in real time.

AI will reward those who simplify

One of the most striking statements from Asam during the discussion is that 麻豆原创 is already seeing 10x performance improvements from AI-driven process improvements. But it also highlights something many organizations still underestimate: just how much AI rewards those who standardize.

The more fragmented the processes and data structures are, the harder it becomes to operationalize AI at scale. In contrast, organizations with standardized platforms, harmonized data, and integrated workflows will accelerate much faster.

That is why modernization conversations today are no longer simply 鈥淚T projects.鈥� They are business strategy discussions.

Future of financial services will be built on trust, scale, and intelligence

Financial services organizations are operating in an increasingly complex geopolitical and regulatory environment. Infrastructure decisions are no longer just about performance and cost, they are about compliance, security, operational resilience, and national requirements.

This is why scalable, enterprise-grade cloud platforms are becoming so critical.

The institutions that thrive in the next era of financial services will be the ones that can combine trusted data, integrated operations, AI-enabled processes, scalable infrastructure, and regulatory resilience into a single operating model.

The future of financial services will not be defined by isolated AI experiments. It will be defined by who can build the most intelligent, connected, and adaptable enterprise foundation for what comes next.

.

Kris Kowal, Banking Industry Leader at 麻豆原创.
Falk Rieker, Financial Services Industry Leader at 麻豆原创.

At the center of that effort sits a deceptively complex challenge: understanding, in real time, which revenue and cost factors are driving or eroding gross margin across a highly diversified business. For years, answering that question meant manual reporting, delayed insights, and finance teams spending valuable time on data gathering rather than analysis.

That鈥檚 now changing, thanks to a co-innovation initiative developed together with 麻豆原创 and Numen, a global 麻豆原创 partner specializing in digital transformation and enterprise software implementation.

From manual reporting to proactive decision intelligence

The project鈥檚 goal was to replace a labor-intensive gross margin analysis process with a generative AI application embedded directly into Natura &Co’s financial workflows. Built on 麻豆原创 Business AI Platform, 麻豆原创’s unified foundation integrating business technology, data, and AI capabilities, the application connects directly to data in 麻豆原创 S/4HANA to provide finance teams with automated insights and narrative recommendations in real time, without the need for manual data pulls or offline reporting.

The application enables users to explore revenue, cost, and margin drivers interactively, identifying at a glance which elements are protecting or eroding margin performance across markets and product lines. Crucially, human oversight remains central to the design: the AI application generates insights, while finance professionals retain full control over interpretation and decisions.

鈥淭he implementation of gross margin analysis using AI in 麻豆原创 S/4HANA marked an inflection point in the analytical capability of our finance area,” said Rog茅rio Dias Garcia, tech manager, ERP Latam, Natura &Co. “We overcame delays and raised the standard of insights by integrating margin analysis from 麻豆原创 S/4HANA with a large language model connected via the 麻豆原创 AI Core layer. This architecture allowed us to provide, in an agile, secure, and completely anonymous manner, a stratified and precise view of gross margin offenders and protectors鈥攄iscriminating exactly which revenue or cost elements were driving market performance.鈥�

A collaborative architecture for scalable AI adoption

Natura &Co鈥檚 application derived from a prototype 麻豆原创 partner Numen created in early 2024 at 麻豆原创鈥檚 global on business AI, leveraging the generative AI capabilities of聽麻豆原创 Business AI Platform. The solution was designed and developed through close collaboration between Natura &Co, Numen, and 麻豆原创. From the outset, the approach was to align AI adoption with concrete business priorities, ensuring the application would be scalable and production-ready rather than a standalone prototype.

Numen brought deep 麻豆原创 implementation expertise to the project, combining knowledge of 麻豆原创 S/4HANA architecture with hands-on experience in building solutions on 麻豆原创 Business AI Platform. The technology stack鈥斅槎乖� S/4HANA, 麻豆原创 AI Core, 麻豆原创 Fiori, and 麻豆原创 Business Technology Platform鈥攑rovided the secure, integrated foundation needed to connect financial data with generative AI capabilities in an enterprise context.

鈥溌槎乖� enabled the transformation by providing the technological foundation and expert support,鈥� said Carlos Aravechia, head of Data Design & Intelligence at Numen.

The success of the project has validated a broader conviction at Natura &Co: that generative AI, embedded directly in ERP workflows, can fundamentally reposition finance from a transactional function to a strategic business partner.

A blueprint for other businesses

The Natura &Co project demonstrates a pattern that other organizations can replicate, particularly those running 麻豆原创 S/4HANA. The combination of structured ERP data with the contextual reasoning capabilities of large language models creates a foundation for decision intelligence that goes well beyond traditional business intelligence tools.

The project was built within a six-month co-innovation sprint and went live in August 2025. It is currently in use across Natura &Co鈥檚 Equador operations.

Looking ahead, Natura &Co is already planning the next phase: integrating Joule Agents to further automate the extraction of standard analytical content and deepen the AI-driven optimization of financial processes.

鈥淭he success of this initiative validates the transformative potential of embedded AI within our ERP,鈥� Dias Garcia noted. 鈥淲e are now ready to move forward鈥攄eepening these insights and integrating the capability of Joule Agents to maximize the extraction of standard content and further optimize our business decisions.鈥�

For 麻豆原创 customers evaluating how to move from AI experimentation to AI in production, the Natura &Co project offers a concrete, replicable model: start with a high-value, well-defined business process, embed AI directly into existing workflows, and build in human oversight from the start.

Companies need to quantify those emissions, recognize liabilities, withstand audit scrutiny, and, ultimately, prepare to settle carbon costs through certificates linked to the EU Emissions Trading System.

In practical terms, carbon becomes a priced input, one that directly affects margins, cash flow, and sourcing decisions in a way that is comparable to other cost drivers.

Why CBAM matters now

CBAM cuts across business functions. Finance teams manage liabilities and forecast costs. Procurement teams need emissions data from suppliers and must incorporate it into sourcing decisions. Trade and compliance teams ensure imports are correctly classified and reported.

No single function can manage this in isolation. The financial implications depend on operational data, and operational decisions increasingly depend on carbon cost exposure.

The regulation initially applies to imports of iron and steel, aluminum, cement, fertilizers, hydrogen, and electricity, with a threshold of more than 50 tonnes annually for declarants. While this concentrates formal obligations on larger importers, the effects extend into supply chains as companies request more detailed and verified emissions data from suppliers and adjust sourcing decisions accordingly.

From reporting to financial exposure

What changes in 2026 is not only what companies must report, but what follows from it. Imports of CBAM-covered goods into the EU create a carbon liability for authorized declarants based on their verified embedded emissions. Those liabilities accumulate throughout the year and must be reported, audited, and settled through the purchase and surrender of CBAM certificates in 2027.

This creates an ongoing compliance cycle with clear financial implications:

• Emissions must be tracked and translated into certificate requirements.
• Liabilities accumulate throughout the year.
• Certificates must be purchased, managed, and surrendered to cover those liabilities.

For finance, this means ongoing exposure that needs active management. For procurement and supply chain teams, costs now hinge on supplier emissions data.

An integrated approach to CBAM

Many organizations are currently managing CBAM across multiple systems, spreadsheets, and manual processes. That approach becomes difficult to sustain under audit and as volumes increase.

麻豆原创鈥檚 approach brings together the key elements of CBAM management into a connected process: data collection and mapping, emissions and cost calculation, analytics, carbon and financial accounting of liabilities, certificate asset management, and declaration generation.

麻豆原创 will offer an integrated, end-to-end CBAM solution for declarants, designed on and . This builds on 麻豆原创鈥檚 strengths in carbon and financial management.

This new approach allows companies to move away from fragmented workflows toward an integrated solution tied to core business processes.

Coming soon: End-to-end CBAM data capture and calculation with 麻豆原创 Sustainability Footprint Management

麻豆原创 Sustainability Footprint Management will provide the operational backbone for CBAM by helping to establish a reliable data foundation. At its core will be customs data, such as import records and CN code classification, sourced from 麻豆原创 Global Trade Services and other trade systems. Without that layer, companies struggle to identify where CBAM applies.

Building on this foundation, 麻豆原创 Sustainability Footprint Management will help centralize CBAM-relevant ERP, trade, and supplier data in one place; ingest supplier emissions data via Excel uploads with automated interpretation; and calculate embedded emissions, certificate requirements, and estimated CBAM costs. A dedicated CBAM dashboard will allow organizations to analyze emissions and cost exposure, while built-in reporting capabilities support the generation of CBAM reports ready for submission to EU authorities.

Available today: Financial control of CBAM exposure with 麻豆原创 Green Ledger

A critical differentiator in CBAM readiness is the ability to connect carbon data with financial processes. Today, with 麻豆原创 Green Ledger, companies can track the number of certificates required, valuate them as financial liabilities, continually revaluate based on price changes, and post financial impacts to the appropriate accounts. This is the level of traceability finance teams and auditors expect, in line with the International Accounting Standards.

This integration enables organizations to:

• Recognize, valuate, and periodically revaluate CBAM liabilities.
• Track certificate holdings and their valuation (coming soon).
• Support audit-ready reporting and verification.
• Forecast and plan for cash impacts (coming soon).
• Break down and manage costs on a product level (coming soon).

Most organizations still treat operational data and financial impact as separate. These capabilities can bring them together.

Beyond compliance: enabling better decisions

While CBAM is often framed as a compliance burden, it also provides an opportunity to improve decision-making. When carbon costs are visible and integrated into business processes, companies can see exactly where exposure sits and where action will make the biggest impact.

With better data, organizations can:

• Identify high-cost emission hotspots in their supply chains.
• Evaluate supplier choices based on emissions and cost trade-offs.
• Improve forecasting and budgeting of carbon-related costs.
• Reduce reliance on conservative and costly default values by using actual data.

This does not remove the cost, but it can make it easier to manage and, in some cases, reduce.

What to focus on now

As CBAM has entered its definitive phase, the immediate priority is building a reliable data foundation. That includes accurate customs data and clear mapping of where CBAM applies. Supplier engagement is another practical constraint. Collecting verified emissions data often requires structured outreach and follow-up. Tools such as can support with supplier communication and data collection, but the effort remains organizational as much as technical.

From there, organizations can assess their CBAM exposure, ensure the necessary data flows are in place, and plan their solution architecture to optimize total cost of ownership, automation, and integration into business functions. It continues with ensuring that emissions calculations are traceable and auditable. That influences behavior; carbon now drives planning, which no team can ignore.

At the same time, companies need to define how CBAM data flows into finance. Linking emissions, liabilities and certificates to financial systems early helps avoid rework and reduces audit risk.

What鈥檚 next

CBAM is a broader shift in how environmental impact is reflected in business performance. Carbon is tracked, priced, and managed alongside other cost drivers.

Organizations that treat CBAM as a periodic reporting task will likely remain reactive. Those that integrate it into financial and operational decision-making will be better position to manage its impact.

For more information, visit .

Gunther Rothermel is chief product officer for 麻豆原创 Sustainability.

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

Business travel still delivers value, but expectations are evolving

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

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

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

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

Overtourism: the hidden cost of peak season travel

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

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

On the road: employees expect more support

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

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

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

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

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

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

Rising budgets, missing tools, and backup

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

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

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

The trust equation

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

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

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

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

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

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

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

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

Unprecedented flexibility for suppliers

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

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

Strategic benefits for Applied Materials

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

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

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

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

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

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

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

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

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

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

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

Cedric Bru is CEO of 麻豆原创 Taulia.

Demand shifts quickly, supply networks are more exposed to disruption, cost and margin pressure remain constant, and the decisions that determine whether a company can respond with confidence rarely sit inside one function.

The enterprise is left with a critical question: How do you move fast enough to capture opportunity without putting fulfillment, margin, or customer trust at risk?

Many of the world鈥檚 largest organizations navigate this challenge on a regular basis. It is exactly the kind of moment that exposes the limits of how enterprises currently operate. Connecting the dots across functions, systems, and decisions still takes too much time, too much manual effort, and too much stitching across fragmented landscapes. By the time teams have gathered the data, aligned the functions, modeled the trade-offs, and agreed on a response, the environment has already shifted.

This is why we introduced the Autonomous Enterprise at 麻豆原创 Sapphire. The goal is to sense change earlier, understand its impact across the enterprise, coordinate the right response, and keep people in control of important decisions. This is a fundamental shift in how businesses can operate: intelligence that is continuous, decisions grounded in real-time context, and an enterprise that moves as a connected system rather than a collection of disconnected parts.

Autonomy at scale

An Autonomous Enterprise is an organization that can continuously sense what is happening across its operations, reason over those signals using business context and established rules, and act across end-to-end processes without depending on manual coordination at every step. AI assistants and agents advance work across the enterprise in alignment with the goals, policies, and constraints defined by humans.

Every AI-driven action is auditable and traceable. Human judgment is deliberately embedded in decisions that require accountability and exceptions that fall outside defined parameters.

Three principles underscore the Autonomous Enterprise:

1. Process knowledge: Deep, industry-specific understanding of how a business truly runs
2. Business data: Enriched, connected, contextual data that gives AI something real to work with
3. Governance: The backbone that keeps everything upright, traceable, and within policy

Beneath it all is the 麻豆原创 platform, ensuring every layer works in concert, every agent operates within guardrails, and every outcome can be traced back to a decision made by a human.

Intelligence that works across the business

The average business landscape probably doesn鈥檛 look like one system, one vendor, or one clean stack. Your processes still have to run end to end across all of it: record to report, plan to make, source to pay, hire to retire, order to cash. If AI is going to work in the enterprise, it has to work across this landscape, not inside one application or vendor boundary.

IDC shows that more than 50% of business decisions still take between one and seven days. That is the gap we are closing鈥攆rom days to moments.*

At the core of the Autonomous Enterprise is the 麻豆原创 Autonomous Suite. Joule becomes the way you interact, as a single entry point into your business. In the middle, the 麻豆原创 Autonomous Suite connects your core domains: finance, supply chain, spend, HCM, and customer experience. And underneath, everything is grounded in your business context, your data, your processes, your rules, your governance.

With 麻豆原创鈥檚 unified foundation of applications, data, and business context, AI is embedded directly into how work gets done, enabling autonomous, end-to-end execution rather than isolated use cases.

The operating model behind this is built on a clear division of responsibility: people set priorities, policies, and guardrails. Assistants understand role and process context and coordinate activity across domains. Agents carry out the defined work, detecting signals, triggering actions, and resolving routine tasks continuously in the background.

And while automation is a part of this, the bigger shift is intelligence and optimization. The system is no longer following predefined workflows. It is using business context to understand what is happening, and what should happen next. This is the shift from systems of record to systems that help run the business.

Autonomous Finance shows what changes

Finance offers a clear example of how this model changes the work itself. Many finance organizations still contend with manual steps, fragmented data, and slow cycles. In a volatile environment, that lag translates directly into slower responses to risk, missed opportunities, and diminished confidence in the decisions that shape performance.

With Autonomous Finance, more of that work can be handled by the system, allowing finance teams to spend less time chasing numbers and more time shaping decisions. The function begins to move from reconciling the past to shaping the future.

Autonomous Finance is not one capability, one agent, or one use case. It is built across the entire finance process, from planning to revenue management, treasury, closing, compliance, and tax. Within each area, assistants are supported by specialized agents working continuously in the background. Some focus on forecasting, some on billing, some on cash, and some on closing. The important point is that these capabilities are connected, so decisions in one area can flow into the others. Connected assistants, specialized agents, continuous optimization. That is the model.

The impact across these areas compounds. Finance teams reclaim meaningful capacity as manual reporting, reconciliation, and transaction processing give way to continuous intelligence. Cash cycles compress. Close timelines shorten. Forecasting becomes more accurate and more responsive to changing conditions.

Because these capabilities are connected, improvements in one area reinforce the others: faster billing flows into better cash visibility, which flows into stronger planning confidence, which flows into more decisive action at the executive level. Compliance strengthens as well, not through added controls, but through better intelligence embedded in the process itself, supporting requirements across ISO, SOC, and SOX with greater accuracy and less manual effort.

The result is not incremental improvement in isolated tasks. It is a fundamentally different operating posture for the finance function, one where the system handles orchestration and people direct outcomes.

Industry AI adds depth

Autonomous domains give breadth across business functions, while Industry AI provides the depth of knowledge. The same supply chain problem looks very different in life sciences, in industrial manufacturing, in agribusiness, in retail, or in energy. The rules, regulations, data models, and value chains are different.

麻豆原创 is not starting from generic AI and trying to teach it how an enterprise works. We start with decades of industry and process knowledge, already embedded in the systems that run the world鈥檚 most complex businesses. Our AI is grounded in sector-specific processes, end-to-end value chains, operational realities, and compliance requirements. And our ecosystem extends this with specialized expertise, so organizations can adapt the intelligence to their markets and their industries.

This is not AI for the sake of AI. This is AI applied to the real operating model of each industry.

The path forward

That is the real shift: not AI operating in isolated tasks, but AI helping the enterprise continuously sense, reason, act, and learn. People remain in control throughout, while the system handles the orchestration required to bring together the right data, context, and decision at the right moment.

The Autonomous Enterprise marks a shift from managing processes to directing outcomes. It moves organizations from reacting to events to anticipating them, and from stitching together decisions after the fact toward helping the business move as one connected system.

This does not require waiting for a perfect, fully transformed landscape. Organizations can begin by applying AI on top of existing landscapes and evolving their business as they go. That work is already underway with many of our customers. What they have in common is that they are starting now, moving faster, making better decisions, and building the foundation for a more autonomous enterprise, step by step.

This is a journey. And it begins with the recognition that the enterprise of the future will not be defined by how efficiently it executes predefined processes, but by how intelligently it can sense change, weigh trade-offs, and move with confidence when it matters most.

For more on 麻豆原创鈥檚 broader Autonomous Enterprise announcement, read The Future of the Enterprise Is Autonomous. For more details on 2026 麻豆原创 Sapphire announcements, see the .

Manoj Swaminathan is general manager and chief product officer of 麻豆原创 Autonomous Suite, Finance & Spend, and member of the Extended Board of 麻豆原创 SE.
Eric van Rossum is chief marketing officer of 麻豆原创 Global Product Marketing and chief product officer of 麻豆原创 Industries and Globalization.

*IDC Resource Map for 麻豆原创, 麻豆原创 Custom Survey 2026: Enterprise Process Automation Survey鈥� April 2026, sponsored by 麻豆原创, doc #US54531626 _RMD , May 2026

Market volatility, liquidity pressures, and currency fluctuations are exposing the limits of traditional planning models built around fixed timelines and after-the-fact analysis. To keep pace, finance teams need the ability to continuously sense change, understand its impact, and steer performance with confidence.

The challenge is that many organizations are still planning with processes designed for a different era. Siloed data, manual workflows, and episodic planning cycles make real-time decision-making difficult, limiting visibility across the entire business. reinforces the urgency: 72% of organizations still find financial planning, budgeting, and forecasting too time-consuming.* In a volatile environment, that lag translates directly into slower responses to risk, missed opportunities, and diminished confidence in the decisions that shape performance.

This is why finance needs a new operating model, one that moves beyond periodic exercises and toward continuous steering. At 麻豆原创 Sapphire, we are introducing 麻豆原创 Enterprise Planning, a new flagship offering designed to close the gap between insight and action, enabling planning to continuously drive business performance.

The shift from periodic planning to continuous steering

Traditional financial planning has always provided structure, but too often that structure comes at the expense of agility. Planning occurs in fixed windows. Teams work from historical snapshots, static assumptions, and fragmented inputs. By the time a variance is understood or a scenario is modeled, the business may already be operating in a fundamentally different environment.

麻豆原创 Enterprise Planning is designed to move organizations beyond these constraints through a continuous approach to planning and execution built on speed, confidence, and control. Finance teams gain the ability to detect signals as they emerge, evaluate constraints in real time, and connect plans directly to execution.

This Sense-Reason-Act model represents a fundamental shift in how planning operates. Rather than waiting for a planning cycle to surface issues, agents continuously monitor for material changes and respond through guided, explainable decisions embedded in everyday processes. At the same time, 麻豆原创 Analytics Cloud continues to support the iterative Plan-Do-Check-Act cycles that finance teams rely on for strategic and tactical planning across mid- to long-term horizons, including model creation, forecasting, variance analysis, and scenario simulation. Together, these two approaches create a planning ecosystem that is both responsive in the moment and disciplined over time.

The solution embeds Joule Agents directly into the planning process, helping connect strategy to operations in real time. Agents can interpret internal and external data signals, model their impact on KPIs, simulate scenarios, recommend actions, and orchestrate planning workflows with built-in governance and explainability. Planning shifts from a single point in time to continuous workflows. When decisions are made, Joule Agents can update plans to support downstream execution. General availability is planned for Q3 2026.

Built on 麻豆原创 Analytics Cloud and 麻豆原创 Business Data Cloud, these capabilities form a more connected, intelligent planning ecosystem that enables organizations to act decisively and with full transparency.

Why governed data and connected planning matter

Continuous planning is only as reliable as the data it is built on. Without a unified data foundation, even the most advanced analytics cannot produce trustworthy outcomes. As automation increases, this challenge becomes more acute: decisions execute faster, but errors can scale just as quickly.

That is why our approach is not AI in isolation. 麻豆原创 Enterprise Planning is built using 麻豆原创 Business Data Cloud data products and the 麻豆原创 Analytics Cloud solution. 麻豆原创 Analytics Cloud remains the foundation for strategic and tactical planning cycles, while 麻豆原创 Business Data Cloud provides the governed data foundation underpinning the entire ecosystem. This helps ensure compliance, auditability, and enterprise-wide trust, which becomes even more critical as AI-driven automation expands.

Continuous planning in practice

What makes this vision tangible is how it shows up in real financial workflows. By continuously monitoring market signals and financial positions, these solutions help organizations reduce the lag between insight and action, improving both speed and decision quality. This is the Sense-Reason-Act model at work: sensing shifts in currency markets, reasoning through the impact on cash positions, and acting through guided decisions that keep the business aligned with its financial objectives.

More broadly, the Autonomous Finance domain brings together Joule Assistants and Joule Agents to provide CFOs and finance organizations with more insight, control, and support across their operations. Beyond planning, specialized Joule Assistants coordinate multiple agents to support key finance processes including financial closing, billing, governance, and tax and compliance. The result is a finance function where intelligence is embedded across the full operational scope, not confined to a single workflow.

Because these agents are delivered within 麻豆原创鈥檚 planning and finance solutions, they carry a native understanding of enterprise data, planning semantics, and mission-critical business processes. The goal is not to replace finance expertise, but to augment it. This gives teams the foresight needed to navigate complexity with greater confidence.

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

The future of finance is continuous

The future of finance will be defined by the ability to connect data, processes, and decisions across the enterprise in a continuous loop. Organizations that can sense change as it happens, reason through its impact using trusted and governed data, and act by connecting plans back to execution will be best positioned to navigate volatility with the agility and discipline that modern finance demands.

With 麻豆原创 Enterprise Planning, organizations can move beyond static planning cycles and toward a more intelligent, continuous approach to steering performance.

For more details, refer to the and the .

Lawrence Martin is chief product officer and head of Public Cloud Engineering at 麻豆原创.
David Imbert is head of Finance Product Marketing at 麻豆原创.

*IDC Spotlight, sponsored by 麻豆原创, The Rise of Dynamic Planning in the Agentic AI Era, #US54493826, April 2026

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

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

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

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The business AI imperative

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

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

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

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

ERP as the foundation for business AI

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

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

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

麻豆原创 Business AI Platform

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

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

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

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

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

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

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

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

Industry AI: H&M and Sector-Specific Transformation

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

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

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

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

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

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

Closing: The Autonomous Enterprise

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

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

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

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

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

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

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

Introducing 麻豆原创 Business AI Platform

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

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

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

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

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

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

Designing the Autonomous User Experience

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

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

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

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

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

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

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

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

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About 麻豆原创

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

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, our new user experience, is gaining momentum and driving significant impact for our customers. Customers are already efficiency, enhancing processes, improving , and .

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

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

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

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

Some highlights from Q1 2026:

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

Joule

Joule, enhancements

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

To set up, see: , , and .

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

For more information, see .

麻豆原创 Joule for Consultants, enhancements

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

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

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

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

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

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

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

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

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SECTION

麻豆原创 Business AI for supply chain

Project Setup Agent
Beta release

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

Get started .

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

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

Get started .

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

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

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

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

Get started .

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

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

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

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

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

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

and get started .

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

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

Get started .

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

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

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

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

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麻豆原创 Business AI for finance

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

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

Get started .

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

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

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

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

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

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

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

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

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

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

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

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

Get started .

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

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

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

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

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麻豆原创 Business AI for spend management

Expense Report Validation Agent
General availability

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

Get started .

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

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

Get started .

Expense Automation Agent
麻豆原创 Early Adopter Care

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

See the demo .

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

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

Get started .

Policy Navigator
麻豆原创 Early Adopter Care

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

Get started .

麻豆原创 Business AI for procurement

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

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

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麻豆原创 Business AI for customer experience

Catalog Optimization Agent
General availability

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

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

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

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麻豆原创 Business AI for IT and developers

Joule Studio code editor and Joule Studio CLI

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

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

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Joule with 麻豆原创 Datasphere
General availability

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

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麻豆原创 Document AI, enhancements

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

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

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

Generative AI Hub in AI Foundation, enhancements

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

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

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

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

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

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

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

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

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

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

See .

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

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

Get started .

麻豆原创 Business AI for industries

Tender Analysis Agent
General availability

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

Get started .

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

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

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

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

Get started .

Joule with 麻豆原创 Intelligent Clinical Supply Management
General availability

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

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

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

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麻豆原创 Business AI for business transformation management

Joule with 麻豆原创 Signavio solutions
General availability

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

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

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

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

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

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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.

A great way to break through that roadblock is to listen to leaders who jumped in early to use AI to transform outcomes. , a managing director of Finance in the Global IT division at Accenture, is one of those people.

The professional solutions and services company employs nearly 780,000 employees across 52 countries, who work with 350 partners to serve over 9,000 clients. The idea of transformation at Accenture鈥檚 scale might be intimidating to some, but not Lambert. He鈥檚 leading an聽ongoing transformation聽of Accenture鈥檚 finance function, which he calls 鈥渢he heartbeat鈥� of the company.

The results he鈥檚 achieved鈥� including saving the finance team a combined 57,000 hours annually by having AI generate narrative summaries for reporting鈥攕hine a spotlight on what鈥檚 possible. And he鈥檚 just getting started.

I had a chance to speak with him about how he became a leader in AI-driven transformation, and what others can learn from his achievements. This is a lightly edited version of our conversation

Q: As you know, innovating with AI is about reshaping how a business delivers value. But not every business leader is leading the charge. Some are watching and waiting. Why did you roll up your sleeves and decide to be on the forefront?

A: Taking a leadership position on AI is important to keep moving forward and shaping new services and capabilities. For example, across a company our size, even though we鈥檙e hyper focused on emerging technologies, we can find small problems across our technology landscape. There are processes and data living in different places and silos develop over time. Most large companies have this challenge. But those are valuable processes, and the business data we have is especially valuable. AI opens up new opportunities to bridge those gaps and deliver more end-to-end outcomes, so that our finance function can meet the growing business expectations of our stakeholders.

For many companies, the key to getting impactful results from business AI is to start with one function that鈥檚 central to business performance. Why was finance the right place for you to begin, and what did you want to achieve?

I always say finance is the heartbeat of our organization. I heard one of our global IT leaders use that phrase, and while it was inspirational, it also made me think, 鈥淟et鈥檚 not accidentally cause a heart attack for the organization.鈥�

Jokes aside; he was right. Your transactional and operational data flows through finance, and management decisions sit on top of it. Starting there gave us the ability to make end-to-end impact across processes that touch procurement, liquidity, forecasting, receivables, and more. And 麻豆原创 gives us a digital core where all that transactional data is harmonized.

The bottom line is that finance is the natural starting point if you want to move from reactive reporting toward more proactive, AI-driven insights that you can use to help move the business forward. So, we set out to unify data and transform finance processes in a way that scales across the whole value chain.

Cash and liquidity are so important in the finance function, and to an entire company. But managing it requires bringing together data, forecasting, and decision-making across many teams. How did AI help?

If finance is the heartbeat of a company, cash and liquidity are the lifeblood of your systems. Here鈥檚 a great example: Accenture engages in a lot of acquisitions, and we run operational cash in 50-plus countries, so it鈥檚 easy for decisions to default to historical, manual reviews. That鈥檚 what was happening at Accenture before a forward-thinking leader stopped by and asked if we could apply machine learning to the problem. Great leaders often ask great questions, and that one really got us thinking.

[AI] freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth.

Eli Lambert

We took inspiration from retail: how stores treat inventory based on discounts and sales. If you treat cash like stock, you can apply those same learning models to figure out how much you really need to hold onto at any point in time. That鈥檚 how we built what we call 鈥淚ntelligent Cash.鈥� It brings all the business data together into a single data mart, a repository for structured data for a specific department or line of business, and uses machine learning to generate recommendations that our teams can act on.

AI is so good at this, and here鈥檚 what鈥檚 incredible: It freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth. Now what used to take months, or even more than a year to build, we can now do it in days or weeks because 麻豆原创鈥檚 data cloud brings [麻豆原创] Datasphere, Databricks, and our machine-learning workloads into one place. The result is faster decision-making, better visibility, and much more accurate forecasting.

I love hearing about how you were able to use gains, delivered through strategic AI innovation, and then channel those gains into a high-value activity for the organization.聽 I know you also worked on receivables, something that impacts cash flow and customer relationships. What pain points did you face, and how did automation and machine learning transform the process?

Receivables were highly manual compared to payables. Clearing was inconsistent, and reconciliation took a lot of time because payments often come incomplete or with partial data. Anyone who works in or near finance knows exactly what I鈥檓 talking about. So, we co-developed on the 麻豆原创 platform a machine-learning-based receivables solution. It more than doubled the automation rate for receivables processing and tripled automatic reconciliation, about a 300% improvement.

As part of that, we introduced high-confidence, one-click matching recommendations that reduce errors and cut down the manual work. We saw a seven percent uplift in auto-clearing with a cash application scheduler built on the 麻豆原创 platform that delivers matches about 77% faster. All of that adds up to a more efficient receivables process, improved cash-flow visibility, and better productivity for the team.

In a global organization like Accenture, reconciling financial data and surfacing meaningful insights can be a huge amount of work. You turned to generative AI to help, which is really smart. What led you to that approach, and how is it changing your team鈥檚 day-to-day experiences?

We were dealing with balance sheet reconciliations across 50-plus countries, and the process was decentralized. I know a lot of companies face this problem. So, first, we moved everything online. Then we brought in machine learning and generative AI to analyze cost categories, summarize data, and surface important shifts.

[Our] Intelligent Financial Advisor, built on the 麻豆原创 platform, can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That鈥檚 saved about 57,000 hours globally. Our teams can focus on higher-value analysis instead of manual reconciliation.

Eli Lambert

We then deployed an Intelligent Financial Advisor built on the 麻豆原创 platform that can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That鈥檚 saved about 57,000 hours globally, just in controllership work, and helped us move to a three-day global close instead of five. The insights come faster and clearer, and the teams can focus on higher-value analysis instead of manual reconciliation. It鈥檚 also helping create more consistent roll-ups across regions and letting us use our talent more strategically.

I鈥檓 hearing this theme of not only measurable business gains from outputs, but the ability to better allocate time from manual, rote tasks to ones that deliver far more value for the business. That also applies to planning and forecasting. How did you bring AI into that part of the finance function?

Our planning work had grown too complex. Remember, we鈥檙e a large-scale, multifaceted global business. So, we replaced old models with 麻豆原创 Analytics Cloud, which gives us multi-year planning models enhanced by AI.

We applied it first to merger and acquisition modeling, where accuracy really matters. It lets us model very complex data sets and helps our finance team collaborate more easily across the business. The results have been more accurate forecasts, reduced risk of errors, and much better collaboration between executives and practitioners. Early results were strong, and that encouraged us to expand AI use in planning more broadly.

What advice do you have for leaders who are not as far along in using AI to supercharge business results?

First, start with a high-impact function tied to real outcomes. Then focus early on data quality and harmonization; it鈥檚 the foundation for everything that comes after. Then get your cadence right and your team working together. Hone in on the use cases that really matter to you鈥攖he best vendors can help you identify those鈥攁nd make sure to get the help you need from those vendors and their partners.

Use AI to spur growth. At Accenture, we鈥檝e been able to use AI to save significant cash in one area, which we then invest in another, high-growth process鈥攁cquisitions in our case. That鈥檚 how you use AI to really rethink your business and move it to the next level.

Eli Lambert, on advice to other enterprises

As you go, take a crawl-walk-run approach: start slow then increase the pace of scale and adoption over time. Be sure to invest in change management and upskilling as you go to spur learning and adoption. And partner closely with technology providers and system integrators who鈥檝e been there before. That accelerates everything.

The final suggestion I have is to use AI to spur growth. At Accenture, we鈥檝e been able to use AI to save significant cash in one area, which we then invest in another, high-growth process鈥攁cquisitions in our case. That鈥檚 how you use AI to really rethink your business and move it to the next level. And that鈥檚 possible today in ways that were not, even five years ago. Seize that opportunity.

I couldn鈥檛 agree more with Lambert. AI really does provide an opportunity to re-imagine entire business processes for greater impact.

To keep exploring what鈥檚 possible, at Accenture. Then see more AI use cases in and across all your , including procurement, supply chain, manufacturing, and more.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI.

These critical pre-purchase moments generate interest and open pathways toward deeper customer relationships. Organizations that convert interest into measurable outcomes with clarity, accuracy, and speed strengthen the overall customer experience, thereby boosting loyalty and bottom lines.

CX becomes even more meaningful as opportunities progress into clear agreements supported by accurate configuration, pricing, and quoting. This transition from opportunity to agreement represents one of the most consequential stages in the customer journey.

Lead-to-cash represents a coordinated motion across sales engagement, pricing precision, service alignment, and performance visibility. When these capabilities operate together, organizations deliver consistent customer experiences while maintaining operational clarity.

Eight years running: a leadership signal at the heart of lead-to-cash

Within the lead-to-cash journey, quoting connects sales engagement, performance management, service continuity, and ERP alignment. It represents a critical moment where customer intent is translated into accurate pricing, configuration, and agreement terms.

When 麻豆原创 CPQ operates within 麻豆原创 Customer Experience, it becomes part of a connected lead-to-cash motion that spans 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, sales performance management solutions, and 麻豆原创 ERP. Sales teams engage with structured opportunity data and guided pricing logic. Service teams inherit full visibility into agreed terms. Performance leaders access insights grounded in accurate pipeline and quoting data.

When it comes to this level of intelligent, real-time, connected processes, very few companies can compete. 麻豆原创 was again recognized as a Leader in the 2025 Gartner庐 Magic Quadrant™ for Configure, Price, and Quote Application Suites. This marks the eighth consecutive year 麻豆原创 has been positioned in the Leaders quadrant based on Ability to Execute and Completeness of Vision.

麻豆原创 CPQ supports organizations in producing accurate quotes — even in environments with advanced configuration and pricing requirements — helping accelerate sales cycles and improve sales execution across complex selling environments.

Extending CPQ leadership across 麻豆原创 Customer Experience

In modern enterprises, quoting connects directly to demand generation, pipeline management, contract processes, fulfilment, and service delivery. brings together commerce, customer data, marketing, sales, service, and sales performance management into an integrated portfolio designed to support truly connected customer journeys.

Within this portfolio, 麻豆原创 CPQ plays a pivotal role in the lead-to-cash journey. When integrated with 麻豆原创 CX solutions, it helps align pricing strategy, product configuration, customer agreements, and sales performance insights across the revenue lifecycle. The result is a more reliable transition from opportunity to revenue realization.

Connected lead-to-cash experience

For CX leaders, lead-to-cash is a core driver of experience differentiation and revenue execution. A connected lead-to-cash strategy ensures that:

• Customer intent is translated into accurate configuration and pricing.
• Sales engagements reflect approved pricing and product standards.
• Customer agreements are consistently captured and supported across systems.
• Sales performance and revenue outcomes remain visible and aligned across teams.

Business impact of connected lead-to-cash

Lead-to-cash determines how consistently organizations translate customer engagement into measurable outcomes.

By combining 麻豆原创 Customer Experience capabilities with a CPQ solution recognized for its ability to execute and completeness of vision, organizations strengthen alignment across sales, pricing, service, performance management, and ERP systems, transforming engagement into measurable outcomes with confidence and precision.

In today鈥檚 environment, customer experience and operational precision are closely connected. Strength in one reinforces performance across the other.

You can learn more about how 麻豆原创 CX connects 麻豆原创 Sales Cloud, 麻豆原创 CPQ, 麻豆原创 Service Cloud, sales performance management solutions, and 麻豆原创 ERP across the lead-to-cash journey .

Sindy Conway is senior Product Marketing consultant for 麻豆原创 Customer Experience.

鈥痚nables organizations鈥攈owever complex, across however many channels, and regardless of which CRM they run鈥攖o produce quick and accurate quotes, accommodating the most advanced configuration and pricing requirements, resulting in a better sales experience and faster sales cycles.

Gartner evaluated 16 vendors and named 麻豆原创 a Leader based on our Ability to Execute and Completeness of Vision. We believe this recognition serves as an acknowledgment of 麻豆原创鈥檚 ongoing commitment to providing customers with a CPQ solution that can meet and exceed their needs. 

麻豆原创 CPQ is an essential component of the 麻豆原创 product portfolio that help automate the quote-to-cash process, which enables organizations to convert sales opportunities into profitable repeat customers. 麻豆原创 customers can transform to 鈥渆verything-as-a-service鈥� with innovative revenue models, quickly adapt to market changes, support multiple sales channels, and support regulatory compliance with end-to-end automation.  

Our customers are the reason we do this, and they participated in the Gartner Peer Insights process by providing reviews that included: 

• 鈥淚t just makes the whole sales cycle move faster.鈥�
• 鈥淚t was relatively simple to onboard when I was a new user鈥� platforms and creating value for customer.鈥�
• 鈥淲e had a very positive experience with 麻豆原创, the platform is scalable, stable.鈥�
• 鈥淎n intuitive user interface that simplifies configuration, robust integration capabilities. Powerful customization options.鈥�
• 鈥溌槎乖� CPQ is amazingly stable and consistent product with the ability to connect with different platforms and creating value for customer.鈥�

Customer case studies provide descriptions of specific value. For example, EXFO Inc, has increased the number of quotes created per month by 70 percent. 

鈥淵ou聽basically give聽the salesperson one to two days of their week back by using 麻豆原创 CPQ,” noted Dominic Kasten, director of Sales Technologies for聽Cleaver-Brooks.聽“When you give time back to salespeople, you are encouraging them to sell solutions to customers instead of just reacting to specifications.”

Hear from other customers and learn more about how 麻豆原创 helps to automate quote-to-cash with鈥槎乖� CPQ,鈥�,鈥�, and鈥� at sap.com.

David Imbert is head of Product Marketing for Finance at 麻豆原创. 
Lawrence Martin is chief product officer for Finance at 麻豆原创. 

Gartner, Magic Quadrant for Configure, Price, and Quote Application Suites, Luke Tipping, Mark Lewis, January 22, 2026 
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner鈥檚 business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are a trademark of Gartner, Inc., and/or its affiliates. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from 麻豆原创. 

In an interview, BSH Head of Governance, Methods, and Systems Heiko Schletz explained how the company is reshaping finance and why its move to the cloud is a critical ingredient to a successful AI-enhanced future.

Technology follows vision

Represented in more than 50 countries, BSH manufactures home appliances in 39 factories worldwide. Schletz鈥檚 team oversees group controlling and is responsible for ensuring that financial data flows smoothly from its core systems, such as ERP, all the way up to the consolidated group level. His team manages how financial data is structured and integrated across all global entities, ensuring it can be used effectively for company-wide reporting and decision-making.

BSH is working to bring accounting and controlling together into one integrated process, supported by real-time data and analytics. One principle is guiding this transformation: technology follows vision, not the other way round. As part of its transformation journey, BSH is embracing change by testing new technologies to support its vision.

To simplify reporting efforts, for example, , 麻豆原创鈥檚 next-generation data management platform that can unify and govern all 麻豆原创 data and seamlessly connect with third-party data.

A recent use case automatically connected accounting balances, controlling P&L data and market metrics in 麻豆原创 Datasphere and delivering consolidated reports without spreadsheets and manual effort. 鈥淭his shows where the journey is going鈥攋oining sources, bringing them together,鈥� Schletz says.

Breaking down silos to empower AI

BSH鈥檚 long-term goal in the financial area is to get rid of silos between accounting, controlling, and treasury. Schletz envisions a parallel ledger architecture that supports both鈥攍egal entity and consolidated group views鈥攅nabling advanced analytics such as value-driver trees. By moving to 麻豆原创 S/4HANA Cloud Private Edition, integrated with 麻豆原创 Datasphere and 麻豆原创 Analytics Cloud, BSH aims to create a single source of truth for finance spanning from subsidiary ledgers to group-level consolidation.

Schletz is convinced that with a cloud-based, synchronized toolset, his finance team can deliver the latest figures for decision-making faster and with less manual consolidation. 鈥溌槎乖粹�檚 AI evolution is running in the direction we also want to go,鈥� Schletz explains. 鈥淭he technology meets our vision and that鈥檚 why it鈥檚 a perfect fit.鈥�

The company has relied on 麻豆原创 solutions for decades, starting with 麻豆原创 R/3 and now running 麻豆原创 S/4HANA, 麻豆原创 Business Warehouse, and 麻豆原创 Analytics Cloud. The next milestone is cloud migration: RISE with 麻豆原创. 鈥淚n the next two years, we go into the cloud,鈥� Schletz says. 鈥淲e want a synchronized toolset that gives us a holistic view.鈥�

To get the most out of analytics and AI functionalities, BSH is currently consolidating and simplifying its comprehensive business application landscape. The company鈥檚 target is to move from six separate ERP solutions to one global 麻豆原创 S/4HANA environment that covers all subsidiaries and geographies.

How to prepare an 麻豆原创 S/4HANA transformation

Schletz鈥檚 advice for other organizations exploring the RISE with 麻豆原创 journey is to start with a clear vision. 鈥淚f you don鈥檛 begin with a concept that combines accounting and controlling, don鈥檛 start with 麻豆原创 S/4HANA,鈥� he says, noting that a finance transformation is not an isolated IT project鈥攊t requires alignment across logistics, sales, and customer service. 鈥淭he 麻豆原创 S/4HANA conversion is a cross-functional adventure,鈥� he adds.

BSH鈥檚 journey is ongoing, but the direction is set. Cloud migration via RISE with 麻豆原创, integrated data, and a finance function designed for insight rather than manual effort. 鈥淲e want the machine to do what it does best, so people can focus on creating value,鈥� Schletz concludes.

Economist Impact鈥檚 new report, “,” sponsored by 麻豆原创, reveals how CFOs are shifting from stewards of financial accuracy to architects of business resilience, digital innovation, and long-term value. Based on input of 480 CFOs globally, the report highlights widening responsibilities, rising risk pressures, and an urgent need to adopt AI with both speed and discipline.

To lead effectively through volatility, today鈥檚 CFO agenda demands operational agility, intelligent automation, and a reimagined approach to workforce development. Here鈥檚 how today鈥檚 finance leaders are adapting.

The expanding CFO mandate

Gone are the days when CFOs focused solely on financial planning and reporting. Today, their influence extends far beyond traditional finance boundaries. Nearly 90% of CFOs report they are more involved in digital transformation and risk management than three years ago. Two-thirds are actively shaping sustainability and ESG strategies.

This evolution reflects a broader truth. CFOs are now central to decisions that impact customers, products, and talent. They are expected to anticipate disruption, mitigate risk, and enable agility鈥攁ll while safeguarding profitability.

Macroeconomic, geopolitical, and technological shifts are pushing CFOs deeper into operational decision-making. As one CFO quoted in the report explained, finance leaders today must 鈥渨ear multiple hats鈥� and develop a deep understanding of business fundamentals, processes, and controls to guide transformation effectively.

With expectations rising and responsibilities converging, the next challenge is clear: aligning these expanded priorities with the capabilities required to execute them.

A sharper risk radar in an uncertain world

CFOs are on the front lines of uncertainty with increasing pressure to keep risks and costs from ballooning. In fact, more than 80% of CFOs reported that they are now more involved in risk management and compliance, with 34% significantly so.

Yet, it is not higher costs that worry CFOs most, it is unpredictability. Inflation, shifting trade rules, and increased interest rates make capital allocation more challenging, with only 37% feeling confident about meeting liquidity targets, compared with nearly 90% for revenue goals.

In response, CFOs are doubling down on what they can control. AI-enabled scenario planning is enabling faster, more sophisticated modeling, while real-time operational signals are being translated into forward-looking risk indicators. Flexibility has also become essential, from upgrading systems for adaptable production to renegotiating vendor contracts with shorter, more variable terms.

Ultimately, the mandate is clear: build organizations that can absorb shocks, respond in real time, and maintain strategic momentum despite uncertainty.

CFOs at the center of AI adoption

Digital transformation has become a core responsibility of the CFO鈥檚 role, with nearly nine in ten reporting increased involvement鈥攎uch of it centered on AI. Finance leaders cite especially strong potential in compliance, where generative AI can parse complex regulations, track rule changes, and automate updates to internal systems.

But several challenges stand in the way of scaling AI鈥檚 impact:

Talent: the biggest barrier to AI acceleration

More than 60% of CFOs cited upskilling and hiring digitally fluent talent as top challenges, with fragmented systems and limited real-time data access adding further friction. As a result, CFOs are strengthening both team skills and data quality, recognizing that AI can only scale when people know how to use it and the data behind it is trusted.

The ROI paradox

CFOs must deliver quick wins from AI even though its most meaningful gains in forecasting, innovation, and growth take longer to materialize. To resolve this tension, leading CFOs are setting clear performance benchmarks, directing AI toward revenue-driving use cases, and coordinating across the business to scale capabilities that unlock sustained value.

Designing the workforce for an AI future

While AI is reshaping work, rising concerns about workforce displacement remain a real challenge for finance teams. However, nearly seven in ten CFOs see AI as a tool to augment human capability, prompting a rethink of roles, skills, and hiring decisions. Leading CFOs are redesigning early-career roles, investing in digital and analytical skills, and building blended teams that pair human judgment with AI-driven insight to strengthen the leadership pipeline.

Taken together, these shifts signal a broader evolution: finance is moving from a function rooted in historical reporting to one defined by predictive insight, real-time decision support, and enterprise-wide capability building.

CFOs who balance rapid efficiency with long-term investment in data, skills, and new ways of working will turn AI into a sustainable competitive advantage rather than a short-lived productivity boost.

Looking ahead: the new CFO playbook

Economist Impact鈥檚 research shows that the modern CFO shapes how organizations navigate risk, adopt AI, and build the workforce capabilities required for continuous transformation.

This shift demands a new playbook that unlocks capacity through automation, strengthens cross-functional alignment, builds flexibility into systems and supply chains, and reimagines finance career paths for a digital-first future. As one interviewee noted, 鈥淭he modern CFO is not just the guardian of value but the architect of future value.鈥� That future will belong to leaders who pair disciplined cost and risk management with bold investment in data, skills, and AI-driven insight.

With 麻豆原创鈥檚 financial management solutions, finance leaders can unify data, processes, and intelligence to meet the expanding demands of the role. As the expectations placed on finance continue to grow, 麻豆原创 remains committed to empowering CFOs with the clarity and confidence needed to lead through uncertainty and shape a more resilient future.

.

David Imbert is head of Product Marketing for Finance at 麻豆原创.

We believe this recognition reflects 麻豆原创鈥檚 continued commitment to innovation, customer success, and delivering extended planning and analysis solutions that empower organizations to thrive in today鈥檚 uncertain business environment.

The Gartner Magic Quadrant can help FP&A professionals assess which vendors are best suited for their purposes. The analyst firm defines financial planning software as solutions that enable FP&A transformation and cover planning, budgeting, forecasting, modeling, performance reporting, and agile insights.

麻豆原创 was recognized as a Leader by Gartner for its ability to execute and completeness of vision.

Unify business planning with 麻豆原创 Business Data Cloud

麻豆原创 continues to build on its market leadership by accelerating its vision for unified business planning. With the introduction of 麻豆原创 Business Data Cloud earlier in 2025, the company has brought together operational, financial, and strategic planning on a single, trusted data foundation. This benefits financial professionals and other strategic planners by eliminating silos and enabling real-time, AI-driven insights for intelligent planning across the enterprise.

麻豆原创 Analytics Cloud in 麻豆原创 Business Data Cloud provides a powerful tool to customers that drive enterprise planning. Customers benefit from the following capabilities:

• Intelligent planning: Machine learning and generative AI automate forecasting and predictive planning by simulating best, worst, and realistic 鈥渨hat-if鈥� scenarios — and providing smart recommendations. This aligns with 麻豆原创’s agentic AI vision, in which agents continuously monitor internal and external data, proactively identifying risks and opportunities for planners.
• Seamless planning: Deep integration with 麻豆原创 Datasphere enables live access to governed, semantically-rich data without replication. This supports live reporting, collaborative planning and analysis, and data-driven decision-making with one tool for data preparation, modeling, planning, and analytics.
• Extended planning and analysis: Customers can plan across all lines of business by combining transactions, analytics, and planning with 麻豆原创 S/4HANA, 麻豆原创 SuccessFactors software, 麻豆原创 Integrated Business Planning for Supply Chain, and third-party data.
• 麻豆原创 Business Planning and Consolidation (麻豆原创 BPC) modernization: 麻豆原创 BPC customers can modernize planning to a scalable, cloud-native environment with enhanced collaboration and AI capabilities.

麻豆原创鈥檚 unified approach is already delivering measurable value for customers worldwide. By leveraging 麻豆原创 Business Data Cloud and 麻豆原创 Analytics Cloud for planning, organizations are achieving faster, more accurate forecasts, streamlined planning, and greater alignment between finance and operations.

Customers successful with 麻豆原创 Analytics Cloud for planning include Blue Diamond Growers, BMW, Calleway Golf, Decathlon, Juniper Networks, Microsoft, Mondelez, Stihl, and many others across all industries.

Kemira Oyj, a global leader in sustainable chemical solutions, accelerated its digital transformation by deploying 麻豆原创 Analytics Cloud for planning. Kemira simplified over 50 percent of legacy master data and harmonized data structures to the cloud across 400 plants in 37 countries within 15 months. This transformation enables real-time financial planning for more than 1,000 users, and enhances forecast accuracy, supporting agile decision-making, sustainability goals, and future-ready business models.

Taras Podbereznyj, CIO of Kemira Oyj, said, “Data is at the heart of our transformation program. It will continue to steer the company as the foundation for innovation and insights, supporting new digital business models and levels of agility and helping us become more competitive in the market.鈥�

Looking ahead

As organizations navigate increasing uncertainty and volatility, 麻豆原创 remains dedicated to helping customers turn data into action. Moreover, the company vision is clear: to provide the most comprehensive, intelligent, and trusted planning platform, helping every business make confident plans across the enterprise and perform at their best in the face of uncertainty.

Dan Yu is chief marketing officer of 麻豆原创 Business Data Cloud.

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 is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

We believe this recognition reflects 麻豆原创鈥檚 completeness of vision and ability to execute in the Cloud ERP Finance market.

Why we believe 麻豆原创 Cloud ERP is recognized as a Leader

At the core of this recognition is , built on a unified, dimension-driven architecture that provides real-time planning and decision-making across global entities. With localization for 60 countries, embedded analytics to enhance compliance and controls, and advanced capabilities including treasury, subscription billing, and joint venture accounting, 麻豆原创 Cloud ERP equips organizations to operate with confidence and scale with agility.
 
This recognition comes to life in the stories of our customers.

Customer spotlight: Bain & Company

One example is Bain & Company, a global consultancy that implemented 麻豆原创 Cloud ERP to standardize and future-proof its finance foundation across 40 countries. By unifying core financial processes and adopting a 鈥渃lean core鈥� approach with minimal customizations, Bain accelerated its global close, strengthened governance with transparent KPIs, and simplified the integration of acquisitions.

As Stephen Mackey, EVP of Global Finance at Bain, explained that 麻豆原创 Cloud ERP 鈥渨as the best fit for Bain. It future-proofed our technology stack, enabled us to get the data we needed to make business decisions, and positioned us to grow.鈥�

And from a CIO perspective, Ramesh Razdan added: 鈥淲e wanted a platform that solves the problems of today and sets us up for the future. With 麻豆原创, we鈥檝e built a strong foundation, and this is just the beginning.鈥�

A key differentiator is the integration with , which enables customers to automate critical finance processes such as validating account balances, processing invoices, and reconciling payments. With prebuilt bots and no-code tools, CFOs can accelerate the financial close and improve efficiency, while laying the foundation for 麻豆原创鈥檚 next wave of AI-driven financial analysis and automation planned for 2026.

When we speak to customers about 麻豆原创 Cloud ERP, they appreciate the seamless integration with (麻豆原创 BTP), which allows them to extend, customize, and connect finance processes without disrupting their core. They highlight the leading capabilities delivered with , enabling finance teams to gain natural language interaction, contextual financial insights, and task automation that reduce manual work and support faster, smarter decision-making.

Looking ahead

We believe being named a Leader in the Gartner Magic Quadrant for Cloud ERP Finance highlights 麻豆原创鈥檚 commitment to continuous innovation. From enhanced localization and compliance support to intuitive and embedded AI, 麻豆原创 Cloud ERP is designed to help finance leaders stay ahead of regulatory, technological, and business change.

With 麻豆原创, CFOs can streamline operations, strengthen controls, and drive resilience — leading finance into the future with confidence.

.

David Imbert is head of Product Marketing for Finance at 麻豆原创.
Lawrence Martin is chief product officer for Finance at 麻豆原创.

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 is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from 麻豆原创.

This achievement underscores 麻豆原创鈥檚 dedication to empowering federal agencies with secure cloud-ready solutions that align with the government鈥檚 modernization and transparency goals.

麻豆原创 S/4HANA Cloud, the company鈥檚 next-generation 麻豆原创 Business Suite foundation, delivers comprehensive financial management capabilities specifically designed for the public sector.

With advanced automation, real-time analytics, and a secure cloud architecture, agencies can optimize financial operations, strengthen oversight, and accelerate mission outcomes. From appropriations management to budget forecasting and performance tracking, 麻豆原创 S/4HANA Cloud enables organizations to work faster, smarter, and with greater accountability鈥攁ll while maintaining full compliance with federal policies and cybersecurity standards.

Advancing the government鈥檚 modernization agenda

The FM QSMO Marketplace, operated by the U.S. Department of the Treasury, is a key pillar in the government鈥檚 strategy to modernize financial management. By curating a pre-vetted catalog of approved commercial and federal solutions, the marketplace simplifies acquisition processes and enables agencies to adopt high-quality, secure, and standards-based systems with confidence.

New marketplace listings from 麻豆原创 include 麻豆原创 S/4HANA Cloud Private Edition, 麻豆原创 Analytics Cloud, integration services for 麻豆原创 Business Technology Platform (麻豆原创 BTP), and a full suite of risk mitigation services covering activation, transition, and operational support. Together, these offerings give agencies the flexibility and agility to modernize their core systems while achieving improved performance, transparency, and accountability.

Accelerating modernization: a win for government and industry

The FM QSMO Marketplace reduces barriers to modernization by centralizing evaluation, contracting, and compliance, streamlining the path for agencies to adopt proven solutions. For government leaders, it offers access to technologies that promote consistency, interoperability, and data-driven collaboration across departments. The result is enhanced efficiency, transparency, and value for the American public.

For 麻豆原创, this selection is both a recognition of decades-long partnership and a reaffirmation of its mission to help government operate at the聽speed of mission. By making secure cloud technology and deep public sector expertise from 麻豆原创 easily accessible, agencies can modernize confidently with solutions that deliver measurable and lasting impact.

Partnering for the future of government

麻豆原创鈥檚 inclusion in the FM QSMO Marketplace reflects a shared vision with the U.S. Treasury: to advance financial excellence, accountability, and trust in government.

Together, 麻豆原创 and its federal partners are shaping the next era of an聽AI-enabled, data-driven, and citizen-focused聽public service, one where modern technology forms the backbone of smarter decisions, stronger performance, and greater outcomes for every mission.

David Robinson is president of Cloud ERP and acting managing director of U.S. Public Services at 麻豆原创.

Before diving into the heart of the keynote, Steinhaeuser invited 麻豆原创 solution area CMOs to deliver lightning-fast recaps of news from the various 麻豆原创 Connect event tracks. In just under two minutes each, they covered finance, procurement, supply chain, HR, and customer experience. From autonomous accounting and next-gen procurement to AI-driven talent acquisition and smarter customer loyalty, the message was clear: across every business function.

The real challenge: connecting priorities

As Steinhaeuser pointed out, 鈥淭he reality is each business area has its own unique priorities and, of course, all-important urgent matters.鈥� The real challenge is not just launching new features; it鈥檚 aligning processes, data, and teams to conquer uncertainty and achieve true customer focus. 鈥淪imply putting an [AI] agent on top of a broken process will solve nothing,鈥� he said.

The flywheel: AI, data, and apps in motion at 麻豆原创

So, how does it all work together? Enter the 鈥渇lywheel鈥� model: the dynamic cycle of AI, data, and applications that drives synergy across the enterprise. This is not just a theoretical approach. Steinhaeuser showed how 鈥溌槎乖� runs 麻豆原创鈥� using the flywheel model.

First, he said, 麻豆原创 uses 鈥渞ole-based AI assistants, powered by specialized agents, [to] support team members across all areas of 麻豆原创.鈥�

Next comes data. Earlier this year, 麻豆原创 became 鈥渃ustomer zero鈥� for (麻豆原创 BDC), connecting all enterprise data in a single layer to generate faster and better insights across financial, workforce, and sales planning. 鈥淲e’re excited to go live with the first set of intelligent apps, starting with People Intelligence,鈥� he added.

Finally, the app layer: 麻豆原创 has moved from 麻豆原创 ERP Central Component (麻豆原创 ECC) to and , through RISE with 麻豆原创. 鈥淭he 麻豆原创 Business Suite is where data is created and where AI delivers impact,鈥� Steinhaeuser said.

But processes continuously evolve with AI, he said, noting that business transformation management, powered by solutions like and , helps 麻豆原创 and its customers continually improve processes and architecture, with AI embedded everywhere. The company also uses to ensure employees stay informed and engaged at every step.

鈥淒riving 麻豆原创’s own transformation, I understand the challenges you face,鈥� Steinhaeuser said. 鈥淚 am convinced leveraging the flywheel of AI, data, and apps across the entire 麻豆原创 Business Suite is how we win together.鈥�

Embedded AI: tangible value, secure, and seamless

Brenda Bown, chief marketing officer of Business AI at 麻豆原创, took the stage to highlight how business AI is showing up in day-to-day work. 鈥淚’ve heard three consistent themes in conversations [with customers]: first, you want AI that can provide tangible value; second, that is secure and properly governed; and third, that works seamlessly across your teams and business. We’re here to deliver just that,鈥� she said.

Joule is now embedded in use cases in trusted 麻豆原创 applications and is making work faster and easier across the enterprise. 鈥淏y the end of this year, we will have more than 400 of these AI use cases,鈥� Bown said. Joule Agents automate tasks across departments, and the new agent builder in (generally available in December) helps customers extend, build, or customize their own agents. 麻豆原创 LeanIX AI Agent Hub and agent mining capabilities in 麻豆原创 Signavio provide governance and transparency for AI agents.

Bown noted that customers like Matur Fompack are using Joule in 麻豆原创 SuccessFactors to hire faster and improve career development. 鈥淭he results are phenomenal: a 48 percent reduction in HR process execution time and 40 percent faster employee development and career planning, and, most importantly, a better employee and candidate experience,鈥� she said.

For processes that require multi-step workflows and nuanced decisions, 麻豆原创 introduced a new generation of role-based AI assistants. 鈥淭hey know your role in the organization, because they are role and context aware,鈥� Bown said. These assistants tap into the right agents for the job, removing any guesswork and helping humans unlock new levels of insight and productivity.

She also showcased how agents collaborate across departments, automate workflows, and even extend 麻豆原创鈥檚 business logic to autonomous devices like robots. Early pilots with partners like NEURA Robotics are already showing Joule Agents planning and executing real work in the real world.

Data and intelligent applications: unified and actionable

Data is only valuable when it is actionable. Irfan Khan, president and chief product officer for 麻豆原创 Data and Analytics, highlighted 麻豆原创 BDC, which unifies enterprise data and powers intelligent applications. 鈥溌槎乖� BDC offers the most powerful foundation for connecting your existing data, building next-generation applications, and the ability to foster and deploy reliable AI,鈥� he said. And the new 麻豆原创 Business Data Cloud Connect solution enables secure, bi-directional data sharing with partners like Databricks and Google Cloud.

Intelligent applications bridge the gap between people and AI. They support smarter decisions and collaboration. 鈥淭hese applications learn from your data and include business simulations to support every business leader with smarter decisions,鈥� Khan explained. 鈥淚f we don’t have a reliable data foundation built around trust, having reliable and resilient data, it becomes very debatable whether or not AI will succeed.鈥�

From insight to action: transformation in practice

How do organizations turn strategy into action? Michael Ameling, president of 麻豆原创 Business Technology Platform (麻豆原创 BTP), demonstrated how 麻豆原创 Business Suite helps drive innovation by uniting core applications, data, and AI, all powered by 麻豆原创 BTP and Business Transformation Management solutions. 鈥淟et’s say you want to understand and improve a business process,鈥� he said. 鈥溌槎乖� Signavio lets you dive deep and understand every detail, and can suggest concrete actions. Then, use those insights to improve the process in 麻豆原创 Build by automating processes and building your own agents.鈥�

He demonstrated how 麻豆原创 BTP and the Business Transformation Management portfolio can help organizations connect systems, gain visibility, and automate processes. Tools including 麻豆原创 Signavio, 麻豆原创 Build, and 麻豆原创 Integration Suite are helping customers like Blue Diamond Growers streamline operations and accelerate transformation.

Services and support: accelerating innovation and realizing value

麻豆原创鈥檚 Anja Schneider, SVP and global head of Premium Engagement and Advisory, wrapped up this segment of the keynote by focusing on how the company鈥檚 services and support teams help customers realize the full value of their 麻豆原创 investments.

鈥淲e鈥檙e with you every step, like a personal trainer,鈥� she said, highlighting how 麻豆原创鈥檚 suite methodology, integrated tools like WalkMe and 麻豆原创 Cloud ALM, and expert guidance help customers realize the full value of their 麻豆原创 investments. She pointed to IBM鈥檚 transformation project as proof: working with 麻豆原创 MaxAttention teams and a clean core approach, upgrades went smoothly with low incidents for more than 150,000 users across 175 geographies.

Customer perspective: Southern California Edison鈥檚 journey

Real-world impact matters. Southern California Edison (SCE) SVP and CIO Todd Inlander shared how the utility company鈥檚 transformation journey with 麻豆原创 is helping modernize its foundational systems and optimize back-office processes. Facing unprecedented demand and environmental challenges, the company is leveraging 麻豆原创 solutions, including 麻豆原创 Business AI capabilities, to transform its operations.

鈥淲e need to adhere to our mission: to deploy safe, reliable, affordable power,鈥� he said. 鈥淲e can’t do that by doing things the way we’ve always done. We have to incorporate 麻豆原创. We’re using it to transform the way we work in our environment. We need to leverage AI because we don’t have enough humans to do all the work. We have to scale.鈥�

As SCE deploys 麻豆原创 Business Suite over the next year, it鈥檚 focusing on keeping a clean core and reducing customizations. 鈥淲hen we implemented ECC 15 years ago, about 66 percent of our enhancements were never used. We’re learning from that experience,鈥� Inlander said. He went on to note that SCE will use the 麻豆原创 deployment time to continue to transform its back-office operations. 鈥淲e’ll be integrating Joule and other AI solutions because doing things the way we’ve always done them and expecting a different outcome is the definition of insanity.鈥�

Steinhaeuser closed the keynote with a look to the future: 鈥淲e鈥檝e made great progress across all lines of business to deliver a unique experience for you鈥攚ith AI becoming your personal assistant, powered by data that defies boundaries and applications that take insight to action. The cross-capabilities you just saw now make the flywheel spin.鈥�

The future is here, and is powered by the synergy of AI, data, and applications. Every business can turn innovation into impact.

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

Unique times

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

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

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The CFO perspective 

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

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

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

New products

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

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

The human-AI partnership opportunity 

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

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

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

Role-based AI assistants

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

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

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

Deep research and new intelligent applications

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

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

麻豆原创 Business Suite delivers unprecedented value

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

However, the pace of change has made traditional approaches, like best-of-breed point solutions, no longer sufficient to help finance teams keep up.

That is why the future of finance technology should act as an orchestra, not disjointed soloists.

Finance teams require a unified experience where applications, data, and AI work seamlessly together. With 麻豆原创 Business Suite, these elements come together as one orchestra, enabling finance to operate in harmony and deliver outcomes that point solutions alone cannot achieve. This means not only faster close and stronger liquidity, but also tighter compliance and credible plans that hold up under change.

To strengthen collaboration across finance and bring innovation to the CFO鈥檚 office, 麻豆原创 unveiled Joule鈥檚 next stage as the AI force at the center of 麻豆原创 Business Suite鈥檚 value creation. A new generation of role-aware assistants in Joule are designed to partner with people in their specific business roles by tapping into the right agents behind the scenes for the job. For finance professionals, 麻豆原创 introduced the next round of automation support using agentic AI.

麻豆原创 is embedding AI-powered reasoning directly into finance processes to automate routine work and elevate the strategic role of finance with the following new agents:

• The Accruals Agent will calculate accruals and deferrals based on system data and present the accountant with a proposal for review, along with a detailed explanation of the calculation logic.
• The Cash Management Agent will automate daily bank statement reconciliation tasks and recommend opportunities for optimization.
• The International Trade Classification Agent will reason over product characteristics and trade regulations and act to classify goods for international shipping, recommending customs tariff numbers and commodity codes

By bringing automation to critical but repetitive finance processes, these agents underscore 麻豆原创鈥檚 commitment to equipping the office of the CFO with AI-driven innovation that strengthens resilience in an increasingly complex business environment.

Finance as the conductor of the enterprise

The role of the CFO has expanded far beyond closing the books or reporting quarterly earnings. Today, finance teams are expected to bring together data, processes, and people from the entire business to enable seamless collaboration.

With 麻豆原创鈥檚 AI-first, suite-first approach, applications and data flow seamlessly, allowing finance leaders to coordinate business operations with confidence. This unified foundation ensures that insights are not siloed, but instead shared across the enterprise, enabling faster and smarter decisions.

Take working capital management, one of the biggest pressure points for finance leaders: A recent of 480 CFOs found confidence levels were strong in almost all areas, including revenue, profit, and customer retention, but only 36 percent felt confident in their ability to achieve working capital targets. 麻豆原创 Business Suite can help organizations meet their targets by automating manual work and enabling actionable decisions — all by leveraging dynamic, autonomous, and action-oriented solutions in 麻豆原创 Business Suite.

When an organization manages incoming receivables, outgoing payables, as well as cash and inventory within 麻豆原创, that connected experience creates data that provides real-time visibility into liquidity through 麻豆原创 Business Data Cloud.聽 That data fuels 麻豆原创 Business AI to analyze situations, recommend actions, and even resolve issues like disputed invoices automatically, which would otherwise slow the cash collection process. And with the integration of Joule and intelligent agents, 麻豆原创 Business AI not only automates, it senses, reasons, orchestrates, and acts 鈥� turning insights into outcomes that free capacity and accelerate the business.

As AI agents take on more of the behind-the-scenes work, finance professionals can step fully into their role as the conductor of the enterprise. They can guide strategy with foresight, aligning every function in harmony and shaping outcomes that extend far beyond revenue or cost goals.

CFOs and their teams can then move beyond reactive decision-making toward shaping strategy in real time. This will position their office as a driver of growth, resilience, and innovation.

Looking ahead

麻豆原创 is laying the foundation for a new era of autonomous finance, where routine processes are automated, insights are delivered in real time, and finance teams are empowered to focus on strategy and growth.

These innovations mark just the beginning of 麻豆原创鈥檚 commitment to helping businesses navigate an increasingly complex financial landscape with agility, intelligence, and confidence.

Lawrence Martin is chief product officer and head of Public Cloud Engineering at 麻豆原创.
David Imbert is head of Finance Product Marketing at 麻豆原创.

麻豆原创 reimagines enterprise AI with role-based assistants in
Joule that coordinate agents across lines of business

LAS VEGAS 鈥� At its inaugural 麻豆原创 Connect event, (NYSE: 麻豆原创) showcases how the integration of AI, data and applications creates unparalleled business value.

These breakthroughs 鈥� including a new network of role-based assistants in Joule that partner with humans to elevate performance, an expanding data ecosystem that drives deeper insights and supply chain software that anticipates disruptions 鈥� once again revolutionize how business gets done.聽

“To thrive when volatility is the new normal, businesses need more than a patchwork of disparate best-of-breed applications,” said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering. “Our announcements today demonstrate the power of 麻豆原创 Business Suite, where AI, data and applications come together in an experience to propel smarter decisions, faster execution and scalable transformation.”

AI that Partners with People

麻豆原创 unveils Joule鈥檚 next stage as the AI force at the center of 麻豆原创 Business Suite鈥檚 value creation. Drawing on the applications and data from across 麻豆原创 Business Suite, 麻豆原创 is introducing a new generation of role-aware assistants in Joule. Each assistant is designed to partner with a human being in their specific business role. Assistants in Joule tap into the right agents for the job, configuring, orchestrating and managing them so humans can focus on unlocking new levels of insight and productivity.

Supporting the assistants in Joule is a , designed to help execute complex workflows within a specific function. For instance, a People Manager Assistant coordinates a team of specialized agents — including the new People Intelligence Agent, which helps spot and resolve issues like compensation anomalies — to support managers as they drive performance. A new Financial Planning Assistant will be aided by a group of expert agents — including the new Cash Management Agent, which optimizes cash flow and improves interest yields — to help finance professionals drive efficiencies. This new roster of role-aware AI assistants not only partner with people to elevate performance in their lines of business but also work together across business functions to solve complex enterprise-wide problems.

Data that Defies Boundaries

Data fuels AI鈥檚 transformative power but it鈥檚 often siloed in different systems. At 麻豆原创 Connect, we are removing those barriers with 麻豆原创 Business Data Cloud Connect. 麻豆原创 BDC Connect securely links 麻豆原创 BDC with partner platforms to enable a bidirectional flow of business-ready data products across organizational and technological boundaries.

With zero-copy sharing, data stays securely in 麻豆原创 systems yet remains instantly accessible in customers鈥� existing data platforms, preserving business context without costly copies. The result: fewer silos, simpler pipelines, no duplication — just trusted data products where and when they鈥檙e needed.

麻豆原创 also announced that Databricks and Google Cloud are the first partners enabled for 麻豆原创 BDC Connect, with more to follow. As announced in February 2025, 麻豆原创 Databricks remains a data service within 麻豆原创 Business Data Cloud, and 麻豆原创 BDC Connect extends its benefits across an open data ecosystem. These partnerships give customers faster access to data products for analytics and AI, helping teams move from raw data to real-time business outcomes with greater speed and simplicity.

Applications that Turn Data into Action

At the heart of 麻豆原创鈥檚 unique value proposition are enterprise applications where data is created and AI-driven insights are experienced. 麻豆原创 Supply Chain Orchestration is a new AI-native solution that combines the power of Joule with a live knowledge graph to detect real-time risks several suppliers deep and orchestrate a coordinated response, helping customers cut costs and keep supply chains moving. 麻豆原创 Engagement Cloud, a new customer experience solution, uses business-critical context to personalize interactions across customers, suppliers and other stakeholders. And our next-generation 麻豆原创 Ariba procurement suite stands out as an AI-native solution, bringing intelligence to every stage of spend management, from sourcing through supplier engagement.

Altogether, these 麻豆原创 Business Suite innovations mark the beginning of a new era powered by self-reinforcing AI, data and applications that drive intelligence, speed and resilience.

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About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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The solution was clear: implement a cloud-based ERP architecture designed to adapt, scale, and drive a full-scale transformation.

Building strategy on speed and people

E.ON Digital Technology鈥檚 move to the cloud was never about chasing modernity for its own sake, but about seizing control in a fast-moving world. The company understood that transformation is not only about technology; it’s about people and their needs.

Together with employees, E.ON Digital Technology crafted a strategic vision to keep pace with rapid technological change while empowering teams to deliver real solutions. At the core of this transformation was the Digital Lighthouse Project, which became the guiding force through the complexities of digitization.

As Athanasios Tabakis, the lead of the Digital Lighthouse technology at E.ON Digital Technology, explained, 鈥淲e鈥檙e not just implementing isolated solutions; we鈥檙e building a platform that integrates speed, planning, reporting, cost transparency, and AI into one seamless ecosystem.鈥�

This cloud ERP foundation shattered old habits, igniting true transformation by putting agility and speed at the heart of the business.

Today, E.ON Digital Technology boasts an intuitive, adaptive, finance-first design, built around how people actually work. The company鈥檚 guiding principle remains unchanged: transformation doesn鈥檛 start with systems; it starts with trust and clarity.

Max Englisch, manager of Finance Transformation at , emphasized the human and scalable nature of the project: 鈥淭his has never just been about technology. It was about creating a vision that scales, brings people in, and drives real impact — not only for E.ON Digital Technology, but also across the entire company.鈥�

PwC Germany continues to support E.ON Digital Technology in rolling out this vision, ensuring the Digital Lighthouse remains a catalyst for change.

Beyond technology: culture, process, mindset

Standardizing across a complex group like E.ON Digital Technology involves far more than technical upgrades. It requires navigating cultural, operational, and political layers. The S/4 United Program, the backbone of Digital Lighthouse, launched the company鈥檚 journey toward 麻豆原创 S/4HANA Cloud Private Edition as the 鈥渕other ship鈥� guiding the entire enterprise.

Innovation layers were carefully added on top to balance strategic consistency with the flexibility to differentiate. Early work focused on identifying where processes could deliver efficiency and value, laying a solid foundation for future-proof operations. As Tabakis put it, 鈥淭he future of ERP is invisible. It anticipates your needs, speaks your language, and acts when it truly matters. It is not just digital; it is almost human.鈥�

E.ON Digital Technology鈥檚 transformation challenges the entrenched mindset of 鈥渢his is always how we鈥檝e always done it.鈥� In a heavily regulated and complex energy sector, data consistency is critical for both compliance and for confident decision-making. By building a semantic layer aligned with business logic, the company moves from fragmented inputs to coherent, faster decisions with increased accountability.

鈥淪uccess comes from those who relentlessly pursue their vision until it becomes a reality,” Englisch added. “That鈥檚 exactly what E.ON Digital Technology has achieved.鈥�

E.ON Digital Technology embraces AI and flexibility for the future

E.ON Digital Technology鈥檚 innovation journey did not stop at cloud ERP. Early adoption of transformed data into conversational insights. Now, the company is advancing toward agentic AI, where AI proactively supports decision-making by alerting teams to deviations and nudging interventions before the issues arise. In energy finance, where timing is everything, this responsiveness is a game changer.

Trust in AI remains paramount. Tabakis noted that 鈥渢rue confidence in AI comes when it aligns with how people think and work. Our mission is to build that trust step-by-step, helping people see AI not as a threat, but as a powerful ally to accelerate their work.鈥� This human-centered approach is essential in a world where half embrace AI鈥檚 promise and half fear its impact.

The energy transition itself demands agility. Renewables introduce volatility, and new consumption patterns like heat pumps and electric mobility add complexity. The grid must balance supply and demand in real time, pushing E.ON Digital Technology鈥檚 business units — and adding their IT teams — to stay flexible.

To manage this, E.ON Digital Technology relies on , a real-time financial management engine that mirrors actual usage, supports dynamic modeling, and scales with demand within the ERP. This technology allows the company to trace billions in value flows accurately and auditable, ensuring flexibility and compliance go hand in hand.

Englisch summarized the future vision: 鈥淭he future of ERP lies in creating an open, connected data foundation that integrates 麻豆原创 and non-麻豆原创 sources, enabling scalable data products and AI-ready architectures. It鈥檚 about moving beyond process efficiency to real-time insights that empower business users to unlock true value.鈥�

E.ON Digital Technology鈥檚 transformation journey shows how cloud ERP, combined with human centered approach and cutting-edge AI, can drive speed, trust, and agility in the complex, rapidly evolving energy landscape. It is a blueprint for how companies can not only adapt but also lead in the digital age.