Growing together

Customer satisfaction has long been a hallmark of 麻豆原创 Business One, with an enviably high customer retention rate in the upper 90% range. Customers typically only leave the solution when a business ceases to exist, for example through bankruptcy or acquisition. “Looking back at the last 30 years, we鈥檝e had almost no customer churn,” said Darius Heydarian, global head of Partner Enablement for 麻豆原创 Business One. “Some customers started with a handful of employees and stayed through two or three decades of change, growing with the solution.”

Heydarian named two main reasons for the high customer retention: ongoing innovations in the solution itself and the 麻豆原创 Business One partner ecosystem. 麻豆原创 Business One operates on a fully indirect model: the solution is sold and implemented solely by partners, who also support customers during their day-to-day operations. Those partners are autonomous, locally present, and in many cases have been building solution expertise and knowledge for over 20 years.

Customer voices

Vereens Stores: adapting the software as operations evolve

Vereens Stores, a U.S.-based supplier of fertilizers and turf management solutions, has been with 麻豆原创 Business One for over 20 years. Andee DiCola, chief financial officer and co-owner, points to one thing above all else as the reason for staying: the customization capabilities of 麻豆原创 Business One. 鈥淩ather than having to change the way we operate to fit the software, we have been able to adapt 麻豆原创 Business One to meet our needs,” she said. Looking ahead, she expects AI to help her company work smarter with information already available today. 鈥淚 see opportunities around reporting, data analysis, identifying unusual transactions or exceptions, and helping automate repetitive processes.”

Design Resources, Inc.: using the same core platform across new ventures and markets

Design Resources, Inc. (DRI), which designs and sources branded apparel, runs multiple intradependent businesses on 麻豆原创 Business One. Joe Leimer, chief information officer at DRI, said the solution’s flexibility has followed each new opportunity: “Whenever ownership is exploring a new venture, the flexibility of 麻豆原创 Business One hasn’t been a limit for us. We know our core ERP needs will be handled and backed by 麻豆原创.” For Leimer, choosing an ERP platform 鈥渋s a decision you only want to make once.鈥�

Staying current: three decades of technology shifts

The roots of 麻豆原创 Business One are in a business management solution built for small companies by an Israel-based startup called Top Manage, which was bought by 麻豆原创 in 2002.

Renamed 麻豆原创 Business One after the acquisition, the solution kept pace with鈥攁nd sometimes led鈥攖echnology shifts inside 麻豆原创. In 2011, it was one of the first 麻豆原创 solutions to integrate the new 麻豆原创 HANA in-memory database. That early adoption gave customers speed and analytics capabilities that traditional databases couldn鈥檛 match at the time. In 2020, 麻豆原创 Business One, Web client, was introduced, encapsulating core business logic in a fresh and modernized user experience. Since then, the Web client has continued to evolve, delivering a growing scope of business processes alongside innovative analytics, integration, and customization capabilities.

麻豆原创 Business One version 11, open for initial partner preview in 2027, will introduce a new generation of cloud management tools for 麻豆原创 partners, such as containerized lifecycle management, which allows software updates without system downtime. It also adds native deployment support for hyperscaler platforms including AWS, Microsoft Azure, and Google Cloud, as well as native cloud storage integration via Google Drive and OneDrive.

Adopting AI

In August 2026, 麻豆原创 Business One introduced integrated generative AI capabilities: 鈥淎sk AI鈥� and AI user-defined query. Ask AI allows users to select business data within 麻豆原创 Business One, based on their individual user authorization, and ask contextual questions about it, such as researching shipping costs, delivery times, or alternatives for a product. AI user-defined query allows non-technical users to generate sharable lists using natural language; for example, “Show me the top five customers by outstanding payments.鈥�

The partner AI ecosystem: two examples at work

Beyond native AI features within 麻豆原创 Business One, a growing ecosystem of partner-built AI solutions is already live. MCP (Model Context Protocol) server integration offers partners new ways to build their own agents and AI-powered extensions for 麻豆原创 Business One. 麻豆原创’s lists 54 solutions to date, with many more to come. The following examples shine a light on the quality of solutions offered by the ecosystem.

Third Wave, a U.S.-based 麻豆原创 partner, built Forge AI, an agentic AI that embeds seamlessly into 麻豆原创 Business One, Web client. Users interact in natural language via a copilot window inside the 麻豆原创 Business One user interface. They can search for a specific invoice, create a goods receipt, raise a support call, or cancel an order. The agent reads and writes data within 麻豆原创 Business One, with a human confirmation step for any irreversible action.

麻豆原创 partner Cloudiax took a different approach. Best known as an 麻豆原创 Business One hosting provider, Cloudiax built AXeLANT Chat, an agentic AI running entirely on its own NVIDIA infrastructure in Germany鈥攑urpose-built for companies with strict data privacy and sovereignty requirements. AXeLANT Chat both analyzes business data and, with a human in the loop, carries out tasks such as creating a sales quote in 麻豆原创 Business One.

Built to last

Embedded AI will be the next big chapter of 麻豆原创 Business One. 鈥淭he upcoming version 11 of 麻豆原创 Business One will be aligned with the Autonomous Enterprise strategy, enabling agentic AI capabilities for the Web client and further extensibility possibilities for partner AI innovation,鈥� said Peter Dominik, global head of 麻豆原创 Business One Product Management.

Joost Tigelaar, 麻豆原创 global sales for SME Solutions, summarized the feedback from various conversations he had with partners during the 麻豆原创 Partner Summit for EMEA and MEE in October: 鈥溌槎乖� Business One customers value the flexibility of the solution, the strength of our partner ecosystem, and increasingly expect AI and automation to help them get more value from their business data and make every day work simpler.鈥�

Thirty years after the first customers made the strategic decision to adopt 麻豆原创 Business One, the solution continues to evolve alongside its customers and the 1.2 million users working with it every day.

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Early-career roles have long relied on gradual, hands-on learning: sitting in on meetings, preparing briefing materials, compiling inputs for reports, coordinating across teams, or shadowing customer conversations. These moments helped young professionals build judgment through repetition. Today, that model is evolving. Many of those tasks鈥攔eviewing customer feedback to identify trends, diagnosing why a campaign missed its targets, investigating a product bug or unexpected feature behavior, or interpreting performance metrics to suggest next steps鈥攃an now be completed in minutes with AI. Work that once filled weeks of onboarding is compressed into hours, fundamentally changing how learning happens on the job.

As organizations adapt, the expectations placed on employees are evolving just as quickly. Recent 麻豆原创 research found that nearly 69% of organizations say AI is expanding employees’ day-to-day responsibilities, while more than half say workforce upskilling and reskilling isn’t keeping pace with AI adoption. For early-career professionals, that means entering workplaces where execution is faster, responsibilities are broader, and human judgment is even more important.

Put together, the shift is clear. The new entry-level job brings higher expectations and less time to build confidence. In this environment, succeeding early in your career isn鈥檛 about knowing more鈥攊t鈥檚 about thinking differently. Three skills in particular will set you apart.

AI fluency isn鈥檛 enough鈥攋udgment is what sets you apart

Knowing how to use AI is quickly becoming table stakes. With of students reporting they use AI in their studies, more than half weekly and nearly one in four daily, AI use alone won鈥檛 differentiate candidates.

What will differentiate them is judgment: how someone frames a problem, tests assumptions, and turns a draft into something others can actually use.

AI might give you a solid first draft of a client email, a slide deck, or a performance report. But is the tone right for that customer? Are the numbers interpreted correctly? Does the recommendation actually make sense in context?

Used well, AI doesn鈥檛 replace thinking; it accelerates it. Used poorly, it produces confident-sounding work that鈥檚 misaligned, unsupported, or simply wrong.

Before you hit send or share AI-assisted work, ask yourself: What problem am I solving? What might be missing or incorrect? What feels unclear or risky? And what should happen next?

This isn鈥檛 about overthinking everything. It鈥檚 about making sure your work holds up, not just that it gets done.

Make your work trustworthy

As AI speeds up production, trust becomes a constraint. Fast work only matters if people can trust it. And early in your career, trust is what determines how much responsibility you鈥檙e given.

Your role isn鈥檛 to accept AI output; it鈥檚 to validate it.

If you鈥檙e using AI to summarize a sales call, pull insights from a data set, or draft a response to a customer issue, your job is to check the details: Are the numbers accurate? Are the sources credible? Is anything missing? Does this align with how your company actually operates?

It also means knowing when to pause. If something doesn鈥檛 look right in a report or feels off in a customer message, asking a question is far more valuable than guessing.

Just as important is transparency. Saying, 鈥淚 used AI to draft this, here鈥檚 what I verified, and here鈥檚 what I鈥檓 unsure about,鈥� builds more credibility than presenting something as fully finished when it isn鈥檛.

In practice, this shows up in small, but important ways: double-checking a metric before it goes into a dashboard, confirming details before sending a customer email, or validating assumptions before sharing a recommendation.

You鈥檙e not alone in feeling some skepticism about AI. Gallup鈥檚 Gen Z research shows adoption can be steady as confidence fluctuates, and that skepticism can be an advantage. When you channel it into stronger verification habits and higher standards of proof, you鈥檙e not just moving fast; you鈥檙e producing work others can trust.

Rapid learning is a competitive edge

The biggest advantage you have early in your career is how quickly you learn.

Every task鈥攚riting a follow-up email, building a slide, handling a support ticket, analyzing a report鈥攊s an opportunity to refine how you think. The professionals who progress fastest are the ones who treat each assignment as a learning loop, seeking input early, reflecting on outcomes, and applying lessons immediately.

That doesn鈥檛 require a complex system. It can be as simple as asking: What decision did I make? Why? What worked (or didn鈥檛)? And what will I do differently next time?

This does two things: it converts experience into judgment faster, and it prevents AI from becoming a crutch. AI can generate options, but real growth comes from reviewing outcomes and adjusting how you work.

The entry-level job hasn鈥檛 disappeared, it鈥檚 evolved

Entry-level roles may be fewer and harder to secure, but the roles that exist today are shifting toward earlier responsibility as AI compresses execution time and pulls more meaningful work forward.

The core skills that matter most aren鈥檛 simply 鈥渦sing AI.鈥� They are judgment, the ability to build trust in your work, and the discipline to keep learning.

If you get this right, you won鈥檛 just ramp quickly. You鈥檒l stand out, building the kind of professional judgment, credibility, and confidence that no tool can replace.

Lara Albert is CMO for 麻豆原创 SuccessFactors.

illustrates just how far VMS platforms have come and where they are heading next.

Adoption is already widespread. According to SIA, 88% of surveyed organizations in the Americas and 86% in Europe use a VMS, while another 12% in each region are likely to explore implementation over the next two years. Global VMS spend under management reached US$303 billion in 2025, up 7% year over year.

But what stands out from these figures is how much more organizations now expect from these platforms. SIA describes VMS platforms as moving beyond systems of record to become strategic workforce platforms, taking on a broader role in process automation, compliance and risk management, advanced analytics, and data-driven decision-making.

For workforce leaders, the next era of external workforce management will be defined not just by transactional efficiency, but by how effectively organizations can connect technology, data, and intelligence to make better workforce and spend decisions.

Connected data becomes the foundation

As external workforce programs grow more sophisticated, businesses need a clearer view of the people, suppliers, skills, services, and spend supporting their operations.

SIA emphasizes that accurate and well-governed external workforce data is increasingly important for reporting, predictive analytics, workforce intelligence, and AI. Organizations looking to get more value from AI must first prioritize a strong data foundation. Fragmented or incomplete information can limit the quality of the recommendations and insights AI provides.

The SIA findings are encouraging for in this area. The report gives 麻豆原创 Fieldglass a 4.93 out of 5 score for reporting and analytics, with capabilities across workforce analytics, supplier performance, SOW visibility, and workforce planning.

When data is connected and accessible, VMS platforms can do more than show leaders what has already happened. It can help them identify patterns, anticipate what may happen next, and make more informed decisions about workforce strategy. That is when workforce data becomes a strategic asset rather than simply a system record.

Services procurement moves closer to the center

SIA identifies the growing importance of services procurement, with statement-of-work management as a strategic priority for both VMS buyers and providers. The field guide notes that SOW represents 39% of spend, while only 13% of client programs currently manage SOW within their VMS platform.

That gap points to a significant opportunity. Services represent a substantial share of external spend, yet many organizations still manage that spend outside the VMS.

Companies increasingly rely on external providers for much more than individual workers. They bring in specialized expertise, project teams, and service providers to deliver defined outcomes. Managing those relationships requires more visibility into suppliers, budgets, milestones, deliverables, compliance, and performance.

In the SIA assessment, 麻豆原创 Fieldglass scores 4.96 for SOW and services procurement. But the broader takeaway is that external workforce management can no longer treat workers and services as entirely separate areas. As services become a larger and more strategic part of external spend, organizations need a more connected way to manage both and understand how they contribute to business outcomes.

Global scale is really about managing complexity

Managing an external workforce across multiple countries is about much more than geographic reach. Every market can bring a different mix of labor classifications, tax requirements, supplier structures, regulations, and operating models.

Global scale should therefore be measured by more than the number of countries a platform can support. The real challenge is creating consistency while retaining the flexibility to address local requirements.

麻豆原创 Fieldglass earned top scores in areas including supplier and engagement management, compliance and audit, and mobile enablement. The field guide also identifies 麻豆原创 Fieldglass as the leader in geographic coverage and notes support for more than 190 countries and 21 languages.

For global businesses, capabilities such as worker classification validation, supplier qualification workflows, and configurable onboarding requirements can help create a common framework across markets while accounting for local requirements. The real measure of global scale isn鈥檛 simply where a VMS operates. It鈥檚 how consistently it can help organizations manage workforce complexity across those markets.

AI moves from assistance toward intelligence and action

AI is another area where the market is moving quickly, with an important reality check.

SIA finds that while VMS AI is expanding into areas such as AI agents, predictive analytics, and candidate assessment, adoption remains measured. Buyers are also looking for transparency, configurability, human oversight, and clearer evidence of business value.

That balance matters. The most useful question isn鈥檛 simply how many capabilities a platform offers, but how that AI works in practice. Is it grounded in trusted workforce data? Is it built into the workflows where decisions are already happening? Can it help people act in ways that are useful, explainable, and governed?

麻豆原创 Fieldglass received the second-highest embedded AI assessment in the field guide鈥檚 overall summary, driven by capabilities designed to connect data, intelligence, and automation across workforce and procurement processes.

The meaningful shift isn鈥檛 simply toward more AI. It鈥檚 from AI that assists with individual tasks to intelligence that helps inform decisions and, with appropriate oversight, enables action.

That evolution is likely to accelerate. But more AI does not automatically mean more value. Lasting impact will depend on pairing sophisticated AI with the right data foundation, strong governance, and connectivity across the enterprise.

The VMS of the future is a decision platform

麻豆原创 Fieldglass received the second-highest overall capability score in the SIA landscape, with particularly strong performance across supplier management, services procurement, analytics, compliance, and mobile enablement. But the bigger story goes beyond any score.

External workforce management is becoming more connected. Leaders need to understand people alongside services, suppliers, skills, spend, and risk. They need workforce data that can move across business processes rather than sit in isolated systems and intelligence that helps turn that information into better, faster decisions.

The organizations best prepared for what comes next will be those that can bring those pieces together to build a more agile, informed, and resilient external workforce strategy.

That is where the next generation of VMS technology is heading.

Ansley Condon is global GTM lead for 麻豆原创 Fieldglass.

In this environment, incentive compensation has evolved well beyond commission calculations. It has become a critical tool for driving engagement, reinforcing desired behaviors, and building trust between sales teams, field teams, and leadership. And as business models evolve, so do the roles it supports. In addition to revenue generators, incentives are now used to manage KPI-based performance across a wide range of functions, including customer service, delivery, and warehousing.

For growing organizations, however, implementing and managing incentive compensation can be a challenge. Teams need reliable calculations, streamlined administration, and clear visibility into performance and earnings, along with a solution that is easy to deploy, adopt, and manage without added complexity.

麻豆原创 is introducing 麻豆原创 Incentive Management, starter edition, a new offering designed听for revenue leaders, total reward leaders, and compensation leaders with core compensation management capabilities for growth-focused organizations.

Building a foundation for a connected revenue operation

As organizations scale, incentive compensation becomes increasingly connected to the broader revenue ecosystem.听麻豆原创 Incentive Management, starter edition, helps establish that foundation by supporting the integration of听data across the business and drawing from ERP, HCM, and SFA systems. By connecting information from operational systems, organizations can improve calculation accuracy, provide greater performance visibility, and support more informed decision-making.

The starter edition is equipped with core capabilities out-of-the-box听that can provide a streamlined foundation for organizations seeking to modernize incentive compensation management while maintaining simplicity and ease of administration.

• Dedicated mobile app access for sellers and revenue generators with easy access to compensation information on the go
• Payee portal to help give sellers greater visibility into incentive compensation, earnings, and performance
• Plan administration to help simplify the management and maintenance of compensation
• XDL integration capabilities to support data exchange and connectivity
• Embedded analytics to help provide insights into compensation activities and performance outcomes

Delivering value across the revenue organization

The impact of incentive compensation extends beyond compensation teams. When incentives are managed effectively, organizations can improve alignment, transparency, and operational efficiency across the revenue organization.

• Revenue operations and compensation teams can streamline plan administration, automate calculations, reduce manual effort, and gain insights through embedded analytics.
• Sellers and revenue-generating teams can gain greater visibility into earnings, performance metrics, and incentive opportunities through self-service access and mobile capabilities.
• Sales managers and business leaders can leverage compensation and performance insights to support coaching, forecasting, and strategic decision-making.
• Business and IT stakeholders can benefit from secure integration capabilities that connect compensation processes with existing business systems, helping create a scalable foundation for growth.

By providing a shared source of visibility across stakeholders, organizations can improve confidence in compensation processes while reducing administrative complexity.

Learn more about how 麻豆原创 is helping organizations turn compensation into a strategic lever for high-impact growth with .

Barbara Linning is global vice president for Product, Engineering, and Cloud Operations at 麻豆原创.

That is why and the have signed a strategic agreement to develop and manage a sovereign cloud environment in the Netherlands. This marks a significant milestone for 麻豆原创 Sovereign Cloud in the European Defense Sector. It enables the Dutch armed forces to modernize critical digital systems while strengthening security, improving resilience, and creating the foundation for future cloud and AI innovation.

Reliable and innovative digital systems are increasingly essential to defense readiness, with recent conflicts demonstrating the importance of cloud resilience. The cloud environment in this case will be designed around the Ministry of Defense鈥檚 specific security and sovereignty requirements, with 麻豆原创 responsible for maintenance, updates, and key aspects of operations. It is scheduled to become operational on January 1, 2028.

As Thomas Saueressig, Chief Customer Officer and member of the Executive Board of 麻豆原创 SE, emphasizes,听the key is to bring innovation and sovereignty together: 鈥淔or the Dutch Ministry of Defense, it is essential to benefit from modern technology while retaining control over the security and sovereignty of its critical digital systems. This partnership demonstrates how these priorities can go hand in hand. As a European technology company, we are supporting the Ministry of Defense in its ongoing digital transformation with a cloud environment that meets its specific security and sovereignty requirements.鈥�

Greater resilience through a multicloud strategy

麻豆原创 Sovereign Cloud will form part of the Ministry of Defense’s broader multicloud strategy, which combines different cloud environments based on the security and operational requirements of individual applications. This includes sovereign cloud, private cloud, and environments for highly classified information.

By diversifying its cloud infrastructure, Dutch Defense aims to reduce dependencies on individual technologies and providers while strengthening its ability to maintain and restore critical services during disruptions, including cyberattacks.

Bart Van der Biest, managing director 麻豆原创 Benelux, highlights why that ability to retain control matters over the longer term: 鈥淲ith a sovereign cloud, it is not just about where data is stored, but also about who has access to systems and who runs cloud operations. Those decisions must remain in the hands of the Ministry of Defense, regardless of how technology or geopolitics change. Ultimately, sovereignty is not only about the control you have today, but also about the freedom you retain tomorrow.鈥�

From national milestone to European role model

This agreement provides a foundation for expanding sovereign cloud capabilities as Dutch Defense鈥檚 technological and operational needs evolve. It could serve as a basis for other eligible Dutch government entities seeking to modernize critical services under comparable security and sovereignty requirements.

Further, with this agreement, the Dutch Ministry of Defense is demonstrating how digital sovereignty can move from strategic ambition into practical implementation. Across Europe, armed forces and security-sensitive organizations face the same challenge: modernizing critical systems, embracing innovation, and building long-term resilience. Sovereign cloud provides a foundation to help achieve these goals.

The Netherlands is setting an important example for Europe: digital sovereignty becomes meaningful when it translates into real capabilities, greater resilience, and the freedom to share the future with confidence.

Martin Merz is president of 麻豆原创 Sovereign Cloud.

AI agents are now creating a new chapter. The next frontier is no longer analyzing faster, but letting agents act: working together across planning, procurement, manufacturing, logistics, and operations.

For more than 50 years, 麻豆原创 has helped organizations run their supply chains by connecting business context, intelligence, and execution across end-to-end processes.听

We are proud to share that 麻豆原创 has been positioned in the Market Shapers quadrant of the inaugural Gartner庐 Emerging Market Quadrant for Integrated Supply Chain AI Agents 鈥� Established Vendors. This is Gartner’s first formal assessment of this emerging category. In our view, it reflects the progress we have made in bringing AI agents into the heart of supply chain operations, and the direction we continue to take.听

Gartner describes the Market Shapers quadrant this way:听

From assistants to orchestrated agents听

Supply chain decisions never happen in isolation. A supplier delay can ripple through production schedules, logistics, customer commitments, and financial results. Yet much of the AI in supply chains today still works within a single function or a single task. It can point out a problem, but people still must weigh the trade-offs and act across several systems.听

With Autonomous Supply Chain Management, people define goals and priorities, AI assistants orchestrate activity across domains, and agents execute the work within governed, end-to-end processes. Through , role-specific assistants coordinate specialized AI agents across design, planning, sourcing, manufacturing, delivery, and operations. Together, they sense disruptions, analyze the impact, simulate alternatives, and execute coordinated responses, with people making the decisions that matter most.听听

Joule is where it all comes together. People express what they want to achieve, and Joule Assistants take it from there.听 Each assistant is tailored to a specific role, while agents handle specialized tasks within business-defined guardrails.听

For example, when a planner describes a goal, the Supply Chain Planning Assistant coordinates demand, inventory, and capacity agents to build and simulate scenarios, so the planner can focus on evaluating outcomes and making the decision. People direct. Assistants coordinate. Agents execute.听

And because 麻豆原创 supply chain agents work within the broader 麻豆原创 Autonomous Suite, a supply chain decision can trigger the right financial, workforce, or customer action as part of the same coordinated response.

Agents that act where the work happens听

We believe AI agents create real value only when they move from recommendation to execution safely. Greater autonomy must be accompanied by greater accountability. As agents take on more responsibility, organizations need visibility into what those agents are doing and why. That requires three things working together.听

First, agents need to work inside the system of record. 麻豆原创 agents operate directly in 麻豆原创 Cloud ERP and the 麻豆原创 supply chain management suite executing production orders, convert requisitions, or direct warehouse tasks, with no middleware between decision and action. They can also extend coordination to trading partners through 麻豆原创 Business Network.听

Second, agents need business context. 麻豆原创 Business Data Cloud, 麻豆原创 Knowledge Graph, and our supply chain semantic model give agents a shared understanding of business entities, relationships, and rules. That helps them understand what a signal means across domains and how a disruption will affect the business.听

Third, agents need governance. Through 麻豆原创 Business AI Platform, Joule Studio lets customers and partners build agents, and 麻豆原创 AI Agent Hub governs both 麻豆原创 and non-麻豆原创 agents with identity enforcement, audit trails, and explainability. Joule Work gives people one place to monitor and act on agent recommendations.

Delivering real results for customers听

Customers will not value us by how many agents we build, but by the outcomes those agents deliver. Recognition is only meaningful when it reflects real impact for our customers, that are already putting supply chain agents to work with capabilities available today.听

Takeda Pharmaceuticals will use Industry AI for regulated manufacturing, where agents monitor production KPIs, flag deviations, and initiate corrective workflows in GxP environments. The expected results include up to 10% productivity gain and 25% less stock-out revenue loss.听

Lemvigh-Mueller deployed three coordinated agents to handle supplier order confirmations, reaching more than 90% touchless processing of 100,000 confirmations a year.听

RWE is applying Autonomous Asset Management to its offshore wind fleet, where AI agents analyze past incidents, identify root causes, and generate pre-filled work orders for technicians.听

Looking ahead听

Supply chains don’t become autonomous overnight. The journey happens step by step: starting with agents that recommend, then agents that act within clear guardrails, and expanding autonomy as data, processes, and governance mature.听听

As we work with our customers and partners, our agent coverage will continue to expand every quarter across supply chain domains.听听

We are grateful to our customers, partners, analysts, and 麻豆原创 teams whose collaboration shapes our products and strategy. We see this recognition as an important milestone, and we remain committed to helping organizations turn their supply chains into a source of resilience and lasting business value.听

Devesh Mishra, General Manager and Chief Product Officer for 麻豆原创 Supply Chain Management

*Gartner, Emerging Market Quadrant for Integrated Supply Chain AI Agents 鈥� Established Vendors, Tess Frenzel, Carlton Sapp, October 28, 2026.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.听
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 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.听

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

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

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

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

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

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

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

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

Learn more about and .

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

Depending on the underlying architecture of your AI solution, that data can cross multiple applications, vendors, and regions before a response ever reaches you.

That data journey matters, especially for organizations in regulated industries who are entrusted with sensitive or regulated data. It is why sovereign capabilities have become critical to keeping data within approved geographic boundaries, limiting who can access it, and supporting compliance with data residency and regulatory requirements.

Today, to support customers with specific sovereignty and data residency needs, 麻豆原创 made Cohere鈥檚 agentic AI platform, North, generally available on 麻豆原创 Business AI Platform. North enables enterprises to securely deploy AI agents and workflow automations at scale within their own infrastructure, without compromising on control or compliance.

North: Cohere鈥檚 enterprise-grade AI agent platform

North sets the standard for business performance by helping teams automate work and accelerate decisions that drive results, all in one scalable, secure workspace. It enables organizations to create AI agents and workflow automations to streamline operations across finance, sales, HR, IT, and customer support. Use cases include forecasting, CRM management, meeting preparation, IT service automation, and enhanced customer service.

Deep customization across models, agents, and workflows ensures North adapts to specific organizational needs while maintaining robust control over deployments.

The 麻豆原创 and Cohere partnership is especially valuable in sovereignty-conscious markets globally where AI adoption needs to align with national expectations of security, privacy, transparency, and accessibility.

麻豆原创 and Cohere’s North platform: Compliant AI made even easier

The combination of 麻豆原创 Business AI Platform and North can deliver exponential value. 麻豆原创 can help reduce overhead by hosting, operating, and supporting the solution end to end. Our APIs, solutions, and knowledge sources make it easier for North users to build AI agents grounded in their unique business context, policies, and enterprise data.

麻豆原创鈥檚 managed delivery model connects North’s AI capabilities to 麻豆原创 environments, reducing the need for organizations to manage separate infrastructure, operations, support, and commercial relationships. In other words, 麻豆原创 Business AI Platform transforms North into a managed, enterprise-ready, and sovereign AI service.

This partnership expands 麻豆原创鈥檚 sovereign solutions that can be tailored to customer needs. For customers with the most advanced requirements, offers data, operational, technical, and legal sovereignty. Our sovereign AI offerings include and Industrial AI Cloud.

For regulated industries and the public sector, this is the foundation for adopting AI while keeping sovereignty, compliance, and business value aligned from day one.

Try it today: .

Brenda Bown is chief marketing officer for 麻豆原创 Business AI Platform and Joule at 麻豆原创.

See Legal Notice on  for use terms, disclaimers, disclosures, or restrictions related to this material.

This is the context in which 麻豆原创 redesigned the 麻豆原创 Services and Support portfolio eight months ago. It鈥檚 built around a simple premise: transformation isn’t a milestone but a continuous journey, and every organization deserves a partner to guide it at every stage.

The response from customers was clear. Success plans from 麻豆原创 became the default way organizations engage with 麻豆原创’s cloud portfolio, from early cloud deployments to complex, multi-solution transformations powered by 麻豆原创 Business AI.

As of today, November 2, 2026, Max Success Plan鈥斅槎乖粹�檚 most strategic level of engagement鈥攊s available to every customer, whether they choose to work with 麻豆原创 directly or through a trusted partner. For those working through a partner, it is now accessible via Cloud Choice (CCFlex) and Resell (VAR), with managed service providers coming soon.

With this, success plans are open to every 麻豆原创 customer, regardless of how they engage with 麻豆原创. has been included in every 麻豆原创 cloud subscription from day one. has long been available across 麻豆原创鈥檚 full customer base, both directly and through partners. now helps close the loop鈥攖he same complete, tiered path to value that 麻豆原创 has offered directly since February 2026听is now available to every customer, regardless of how they choose to engage with 麻豆原创. 听

The evidence: why guidance determines who wins with AI

The data on this is stark. RAND Corporation found that 80% of enterprise AI initiatives fail to deliver their intended business value鈥攖wice the failure rate of standard IT projects. S&P Global Market Intelligence found that 42% of companies abandoned most of their AI initiatives in 2025, up from 17% the year before. And MIT鈥檚 widely cited GenAI Divide report found that 95% of organizations investing in generative AI see zero measurable return on their P&L, while that same research found that pairing AI adoption with an experienced external partner roughly doubles deployment success, 66% versus 33% for internal-only efforts.

The differentiator isn鈥檛 the technology stack. It鈥檚 guided execution.

This is exactly the gap success plans were built to close. They are 麻豆原创鈥檚 cloud engagement model: the structured, continuous guidance customers have been asking for, helping organizations adopt the latest innovations and stay ready for whatever comes next. Foundational Success Plan, Advanced Success Plan, and Max Success Plan can scale that guidance to match a customer鈥檚 complexity and AI ambition. And Max Success Plan, as 麻豆原创鈥檚 most strategic level of engagement, can be that experienced external partner the research points to, built directly into the 麻豆原创 relationship, not bolted on beside it.

The proof: what success plans actually deliver

IDC’s research found that organizations using achieved $4.9 million in average annual benefits, 28% less time spent on incident management, and a 39% reduction in impactful security breaches*. customers saw a 381% three-year ROI, $3.4 million in annual average benefits, and 19% quicker time to market for new features and functions**. And customers achieved a 521% three-year ROI, $4.4 million in end-user productivity gains per organization per year, and a 24% improvement in business processes***.

Guidance that pays for itself鈥攁t every level. Full findings for each success plan are available on .

One clear goal: consistent guidance, regardless of how you reach 麻豆原创

When 麻豆原创 introduced the success plans, the goal was clear: give every 麻豆原创 cloud customer a clear, predictable path to value, one plan included with every subscription and two additional that scale with ambition. What became apparent almost immediately was how many of those customers already reach 麻豆原创 through a partner鈥攁nd how natural the next question was: why should the path to value look any different because of that?

Extending Max Success Plan through partner channels closes a gap that had nothing to do with the value of the offering and everything to do with how customers happened to reach it. That relationship should no longer determine the level of guidance available.

鈥淎s AI raises the stakes for successful transformation, customers need consistent, strategic guidance regardless of how they engage with 麻豆原创. By making Max Success Plan available through our partner channels, we are expanding access to the expertise and support customers need to turn innovation into lasting business value.鈥�

Thomas Saueressig, Chief Customer Officer and Member of the Executive Board of 麻豆原创 SE

Max Success Plan: strategic guidance, built into every engagement

Max Success Plan is 麻豆原创鈥檚 most comprehensive success plan, designed for organizations navigating complex transformations, scaling 麻豆原创 Business AI, or operating in fast-moving business environments. It combines strategic expertise, proactive guidance, and hands-on support to help customers continuously evolve and maximize the value of their 麻豆原创 investments.

Key capabilities include:

• A designated success plan manager who helps orchestrate the end-to-end customer experience with deep knowledge of the customer鈥檚 business and 麻豆原创 landscape
• Cross-solution process improvement and transformation advisory that can connect strategy to execution across the full 麻豆原创 landscape
• Tailored AI use case adoption, including prototype development to validate ideas and de-risk investment before scaling
• Everything in the Advanced and Foundational Success Plans, like guided transformation, proactive risk detection, and mission-critical support, so nothing is lost, only added to

Max Success Plan can be directly integrated into how customers run and evolve their 麻豆原创 solutions. It can be continuous, proactive, and tightly connected to the product road map customers are already investing in. That holds true whether a customer reaches 麻豆原创 through a partner or engages directly.

鈥淔or many of our customers, the partner relationship is the primary one. Extending Max Success Plan into Cloud Choice and Resell means our partners can now sell that same strategic service offering themselves.鈥�

Karl Fahrbach, Chief Partner Officer at 麻豆原创

Available now: growing to meet you where you are

Max Success Plan is now available to organizations working with Cloud Choice (CCFlex) or Resell (VAR) partners. Managed service provider (MSP) is currently piloting with a few customers, allowing MSP partners to align their delivery models and prepare for broader availability.

This phased approach reflects a key insight since February: the organizations that get the most from success plans are those that adopt at a pace matching their own readiness.

As 麻豆原创 builds toward the , where AI increasingly supports execution while people set the direction and hold accountability, the guidance layer around that technology matters as much as the technology itself.  Expanding Max Success Plan across partner models gives more customers access to the strategic guidance they need to adopt innovation, navigate continuous transformation, and realize greater value from their 麻豆原创 investments.

The guidance is in place. The next step is yours.

The difference between an AI initiative that transforms a business and one that quietly stalls is rarely the technology. It is the guidance around it鈥攖he expertise that connects a product roadmap to a business outcome and keeps that connection strong as both evolve.

That guidance is now available to every 麻豆原创 customer, however they choose to reach us. Success plans remain the starting point for any 麻豆原创 cloud engagement, for customers mapping out their next transformation and for the partners they rely on to get there.

Ready to take the next step? to find the level of engagement that best fits your organization. Partners can explore the success plans within the partner portal.

Ville Leivo is global head of Cloud Success and Premium Portfolio at 麻豆原创.

Ryseff, J. and Narayanan, A. Why AI Projects Fail and How They Can Succeed. RAND Corporation, 2025.
S&P Global Market Intelligence. Generative AI Shows Rapid Growth but Yields Mixed Results. Voice of the Enterprise: AI & Machine Learning, October 2025.
Challapally, A., Pease, C., Raskar, R., and Chari, P. The GenAI Divide: State of AI in Business 2025. MIT NANDA (Project NANDA, MIT Media Lab), July 2025.

*IDC White Paper, sponsored by 麻豆原创, The Business Value of Using 麻豆原创鈥檚 Foundational Success Plan, doc ##US54140525-BVWP, April 2026
**IDC White Paper, sponsored by 麻豆原创, The Business Value of Using 麻豆原创鈥檚 Advanced Success Plan, doc #US54296426-BVWP, April 2026
***IDC White Paper, sponsored by 麻豆原创, The Business Value of Using 麻豆原创鈥檚 Max Success Plan, doc #US54437226-BVWP, May 2026

Those days have long since been lost to history.  

From a single building in Bengaluru, 麻豆原创鈥檚 presence in India has expanded across the country, transforming a small operation into one of 麻豆原创鈥檚 most important global innovation hubs. Today, more than 18,000 employees are on the payroll, making it 麻豆原创鈥檚 second largest location. 

麻豆原创鈥檚 presence in the country has two closely connected dimensions: 麻豆原创 India, led by Manish Prasad, and 麻豆原创 Labs India, led by Sindhu Gangadharan.  

麻豆原创 India is a regional business organization serving India, Sri Lanka, and Bangladesh. With locations in Bengaluru, Delhi, Hyderabad, Gurugram, Mumbai, and Pune, 麻豆原创 Labs India drives product development and innovation for 麻豆原创 solutions used worldwide. 

麻豆原创鈥檚 commitment and trust in 麻豆原创 Labs India to deliver innovation is evident with the opening of 麻豆原创 Labs India Innovation Park, located in Bengaluru, . Inaugurated in August 2025, this 鈧�194 million investment is 麻豆原创鈥檚 largest research and development hub outside of Germany and can accommodate an additional 15,000 employees.听

From local solutions to global impact 

Solving local challenges and then scaling the solutions globally has become a hallmark of 麻豆原创 Labs India. Teams working to help customers navigate India鈥檚 complex tax reporting requirements laid the foundation of 麻豆原创 Document and Reporting Compliance. Today, this solution serves customers in more than 50 countries. 

That pattern of turning local expertise into global product innovation is now visible in 麻豆原创鈥檚 AI strategy. 

With Sudhakar Singh, who leads the AI Security office, and the global head of engineering for Joule and head of 麻豆原创 Business AI, Rahul Lodhe, both based at 麻豆原创 Labs India, the location plays a visible role in shaping the agenda for the Autonomous Enterprise.听

Teams in India engineered , an AI solution grounded in 麻豆原创鈥檚 business data and context and integrated across 麻豆原创 and non-麻豆原创 systems. Accessed via a conversational interface, Joule has evolved from a copilot and听now orchestrates background AI capabilities and 听

One example is the Data Product Creation Agent, designed to help鈥� users overcome the lack of quality data products鈥攐ne of the hidden bottlenecks in AI.听听

In a matter of months, with help from domain experts beyond 麻豆原创 Labs India, the proof-of-concept (PoC) became the Data Product Creation Agent.听听

The speed of development was astonishing, says Leena Kharti, development manager for the team. Kharti, who has worked in development for 16 years, attributes this to the mindset shift in her team. 鈥淲e got into the habit of thinking differently: today there鈥檚 a blocker, so let’s work on something else that moves us forward. In AI, speed is a feature, and that mindset made all the difference,鈥� she says.听听

The same spirit of innovation extends beyond 麻豆原创 Labs India’s own teams to the wider network of founders, engineers, and emerging technology firms around them鈥攁nd the ideas it generates.听

India’s startup ecosystem鈥攖he third largest in the world鈥攈as become a powerful source of innovation, complementing the country’s deep engineering talent and entrepreneurial culture. This offers 麻豆原创 Labs India a valuable pipeline of new ideas, technologies, and partnerships that can help shape the next generation of enterprise software.听

To tap into that energy, 麻豆原创 Labs India launched the 麻豆原创 Startup Studio program in Bengaluru in June 2026. The six-month accelerator program offers selected startups the opportunity to co-create enterprise-ready solutions with 麻豆原创 teams and scale through 麻豆原创’s global ecosystem.听

This year’s cohort included ANSCER Robotics, Pulse Energy, and QNu Labs, whose work spans enterprise AI, robotics, industrial intelligence, and quantum-safe technologies.听听

While the focus areas of this year鈥檚 cohort differ, the program鈥檚 goal is the same: to turn emerging technologies into practical business solutions. This provides 麻豆原创 an early window into innovations that could help define the future of the Autonomous Enterprise.听

鈥�India has all the ingredients to win in AI鈥� 

India has an outstanding talent pipeline with more than 2.5 million STEM graduates in 2026 and one of the world鈥檚 most dynamic startup ecosystems. Combined with 麻豆原创 Labs India鈥檚 growing role in innovations such as Joule and agentic AI, these strengths are shaping the solutions 麻豆原创 delivers to customers worldwide. 

As 麻豆原创 advances its vision of the Autonomous Enterprise, the importance of 麻豆原创 Labs India lies not simply in its size but in its influence on what 麻豆原创 ships to its global customer base.听听

鈥淚ndia has all the ingredients required to win in AI,鈥� says CEO of 麻豆原创 SE Christian Klein. 鈥淔irst, the quality of technical talent and universities is extremely strong. Second, India is investing heavily in building the infrastructure that AI ecosystems need. And third, it鈥檚 a country that is pro-business with a strong focus on becoming an even better place to invest.鈥澨�

• Build on proven business controls: You already have governance standards and practices in place. AI governance should connect to and extend these controls.
• Adopt innovation with confidence: The approach to governance we advocate is the same publicly available and documented approach we use to develop, deploy, and govern the solutions and services we deliver.
• Stay in control as your business grows: The controls and standards you have in place should not only promote AI adoption, but also enable you to assess costs, risks, and value as adoption increases.

Since the advent of this new world of autonomous technology, people have endeavored to identify the most impactful application of it. As the path to value realization has become clearer, a new challenge has emerged: ensuring both that AI usage does not lead to unintended and potentially harmful consequences and that this usage remains compliant with emerging regulatory regimes.

By adhering to the principles outlined above, 麻豆原创 makes it possible accomplish both ends. More importantly, as we showed at 麻豆原创 TechEd, 麻豆原创 also provides the solutions necessary to govern, control, and innovate now and into the future. 

AI governance announcements from 麻豆原创 TechEd

Here鈥檚 what we talked about at 麻豆原创 TechEd on the AI governance front.

True AI governance isn鈥檛 about figuring out if policies and rules have been followed after the fact; it should be about making sure governance is applied when it matters. 麻豆原创 Enterprise AI Gateway ensures this kind of proactive governance by consolidating authentication, policy enforcement, and routing at runtime, while also allowing for continuous monitoring, usage tracking, and auditing. The gateway thus controls traffic across MCPs, APIs, agent-to-agent connections and LLMs. Building on 麻豆原创 Integration Suite, the gateway consolidates and streamlines real-time oversight and control over AI in action.

As a compliment to the gateway, we also announced customer access to curated 麻豆原创 Sovereign Cloud LLMs directly through  . This allows customers to use these LLMs in critical processes while adhering to established governance standards. What鈥檚 more, by allowing direct access on , customers can reduce both cost and complexity in AI operations.

Being able to provide agents with the architecture context they need to operate effectively is also essential to the control of agent behavior. At 麻豆原创 TechEd, we described new features in the 麻豆原创 LeanIX portfolio enabling enterprise architects to control the architecture data Joule Agents use to make and execute decisions.

This architecture context feature relies on a knowledge intake agent to extract relevant information from security and architecture decision documents, as well as propose connections to the 麻豆原创 LeanIX knowledge graph, and submit all data for human approval before making it available to agents.

Finally, to support EU AI Act assessments directly in , we also introduced our new AI Governance Assistant. The assistant audits and flags incomplete documentation in the agent inventory, pre-fills fields from completed assessments, identifies systems that appear high-risk but lack classification, and triggers reassessment when a fact sheet changes materially.

And just as the architecture context feature depends on human approval, all outputs from the AI Governance Assistant appear as drafts for legal and compliance review.

麻豆原创 and AI governance in depth

The solutions and innovations around AI governance we discussed at 麻豆原创 TechEd are part of a much broader story. That story is, to a degree, historical. 麻豆原创 has long played a key role in the governance of core operational and financial functions. Our approach to AI governance is built on鈥攁nd a logical extension of鈥攖his legacy.

Of course, 麻豆原创 is hardly new to this, having long ago established clear internal standards for AI governance while continuously introducing features and solutions devoted to AI governance. Among these solutions is 麻豆原创 AI Agent Hub, itself an outgrowth of AI governance capabilities in 麻豆原创 LeanIX. By allowing companies to quickly create and maintain a reliable inventory of agents, regardless of platform, MCP servers, and LLMs, the hub鈥檚 powerful discovery features are essential to comprehensive AI governance.

The other part of the story is value. While governance of AI is crucial for myriad reasons, companies invest in AI to achieve measurable business goals. As it turns out,t he observability necessary for effective governance can be leveraged and augmented to serve the purpose of performance management and evaluation.

With 麻豆原创 Signavio agent mining capabilities, for example, you can monitor agent behavior within the context of your business processes. In this way, you can ensure agent compliance while capturing the business impact agents deliver.

Governance should not be an afterthought

The evolution of AI is not going to slow down, and neither will AI adoption. Unfortunately, while the focus here rightly falls on continuously discovering new ways to leverage AI for operational excellence and competitive advantage, this focus should not mean that governance takes a back seat.

Governance, on the contrary, is the key to successful AI adoption.

It is important to remember that governance in the era of AI is not simply a question of regulatory or corporate compliance. Because this technology involves varying degrees of autonomy, governance must be applied to the behavior of the technology itself, not just to the behavior of the humans managing it. This is one reason that we emphasize runtime enforcement of governance policies.

The challenge we see today, however, is that no one has yet established clear industry benchmarks and standard practices for AI governance. There also seems to be a general lack of established platforms focused on governance.

We say a 鈥済eneral lack鈥� because, in point of fact, 麻豆原创 offers precisely such a platform, one built on decades of governance expertise and experience.

Governance has never been an afterthought for us. And when it comes to AI governance today, as the many innovations outline here show, it is absolutely top of mind.

Dominik Rose is chief product officer for 麻豆原创 LeanIX at 麻豆原创.

As AI is deployed across business processes, effective decision-making now hinges on unifying fragmented data and business context. AI agents can generate results quickly, but good judgement depends on deep understanding of how an organization runs. They need access to data across the enterprise, trusted views of business entities, and an understanding of the relationships, processes, and rules that define how the business operates.

麻豆原创 Business Data Cloud (麻豆原创 BDC) is the trusted data foundation for 麻豆原创 Business AI Platform, providing a business data fabric that connects data, semantics, processes, and policies, while preserving business context that gives data meaning. The innovations announced this week extend these capabilities making enterprise data more accessible and providing agents with the trusted business context for successful AI.

Extend the business data fabric with an open lakehouse foundation

Enterprise data is distributed across sources, including databases, warehouses, and data lakes. Making that data accessible across environments often requires duplicating copies, increasing costs and making it harder to maintain consistent business context.

With the completed acquisition of Dremio, 麻豆原创 is enhancing the open lakehouse foundation for business users and AI agents to access, share, and work with distributed data across the landscape.

• Discover, govern, and share data across platforms: 麻豆原创 BDC now supports native Apache Iceberg and a universal catalog powered by Apache Polaris, providing an open and governed data foundation for users and Joule to consume data. Together with 麻豆原创 BDC Connect, this builds on 麻豆原创鈥檚 commitment to platform interoperability with zero-copy data sharing across partners including Databricks, Snowflake, Google, AWS, and Microsoft.
• Query distributed data with zero copies: Supported by Dremio鈥檚 high-performance SQL query engine, users can federate access across lakehouses and relational databases including SQL Server, MongoDB, Oracle, and Postgres without moving data. Through integration with Joule Work, business users can ask complex questions in natural language and get answers grounded in trusted 麻豆原创 and custom data products and the business context behind them, without needing to know where the data resides or how to query it.
• Accelerate analytics and AI autonomously: For developers, maintaining query performance requires building summary tables, updating materialized views, or creating separate BI extracts to keep analytical queries fast enough for analytics and AI. This engineering work is reduced with Dremio鈥檚 Reflection technology, which learns from how your data is being used and automatically precomputes and optimizes the results your workloads need. As workloads change, Reflections adapt with them, removing the need for manual performance tuning while delivering sub-second performance at scale for analyst queries and high-concurrency agentic workloads.

鈥淲ith 麻豆原创 Business Data Cloud, we are building an enterprise foundation that unifies, governs, and is ready for innovation, so analytics and AI can scale without compromising the quality, compliance, and trust.”

Ganesh Sivakumar, Global Chief Data Analytics & AI Officer, Mead Johnson Nutrition

Accelerate your journey to the cloud

We also announced an expansion of the partnership with Databricks, bringing the latest set of industry-leading data platform capabilities including Lakeflow, Agent Bricks, Unity Gateway, and Lakebase to 麻豆原创 Databricks in 麻豆原创 Business Data Cloud.

麻豆原创 Application Data Foundation makes data from 麻豆原创 applications available for consumption in your 麻豆原创 BDC engine of choice, including 麻豆原创 Databricks. This provides a cloud-native way to access data in 麻豆原创 Business AI Platform, helping to lower the total cost of ownership and reduce time to value. 麻豆原创 data products can subsequently be shared across the business data fabric using 麻豆原创 BDC Connect for zero-copy access, avoiding the creation of additional pipelines, data copies, and silos.

For example, a manufacturer could use historical sales, inventory, and supply chain data to develop AI models for demand forecasting. The data is accessible from 麻豆原创 BDC, where data professionals can enrich it with third-party data and create custom data products in 麻豆原创 Databricks. Data engineers can build models on a semantically rich foundation, helping produce forecasts grounded in logic that is already embedded in 麻豆原创 systems while accelerating model development.

Build a trusted system of context across domains

Unified master data is only as valuable as the governance applied to it. Across the enterprise, the same customer, supplier, or product can appear differently across systems with different identifiers, attributes, and relationships. For an AI agent, understanding that these records represent the same business entity is essential for making trusted decisions.

Earlier this year, we announced the acquisition of Reltio, adding capabilities to help customers cleanse, harmonize, and unify data across systems. With the acquisition now complete, is now generally available in 麻豆原创 BDC, bringing multi-domain master data management natively into the business data fabric.

麻豆原创 Reltio鈥檚 AI-based entity resolution matches and reconciles records across 麻豆原创 and third-party systems to create unified, 360-degree views of business entities, including customers, suppliers, products, and other critical entities. By bringing together trusted attributed, relationships and business interactions 麻豆原创 Reltio gives business users and agents a more complete understanding of these entities across sources. These trusted profiles can be made available as governed data products in 麻豆原创 BDC, giving Joule and Joule agents consistent, connected business context to support decisions and actions.

Turn enterprise knowledge into reusable business context for AI

Every organization operates according to a defined set of business rules, relationships, processes, and policies that determine how decisions are made. 麻豆原创 Knowledge Graph provides rich business context across your data, connecting thousands of entities, relationships, and rules, creating a comprehensive view of the enterprise. Agents working on specific tasks need access to the context most relevant to the business problem they are solving, rather than the full enterprise model.

At 麻豆原创 TechEd Berlin, we introduced 麻豆原创 Business Data Cloud Ontology Manager, a new AI-assisted experience that enables teams to create domain-specific views of 麻豆原创 Knowledge Graph for applications, agents, or defined business scenarios. These curated views bring together the entities and relationships most relevant to each use case, grounding agents in the right business context they need to solve a particular business problem.

For example, a collections agent needs to understand customers, invoices, payment terms, and credit risk. 麻豆原创 Business Data Cloud Ontology Manager lets teams create and publish a domain view for collections. This context helps Joule Agents connect information across these business entities, supporting answers and recommendations that reflect the relationships and rules relevant to the scenario. Because these views inherit definitions and governance from 麻豆原创 Knowledge Graph, teams can reuse the same business semantics in views for related use cases, such as cash flow or order management, without having to redefine those concepts.

Get started

Irfan Khan is president and chief product officer of 麻豆原创 Data & Analytics.

In 1996 麻豆原创 held its first event for developers. Thirty years later in Berlin, 麻豆原创 CTO Philipp Herzig told the 麻豆原创 TechEd keynote audience, 鈥淲e are experiencing the biggest and fastest technological shift in our lifetime.鈥�

鈥淔or decades developers have been building systems in a structured, deterministic way. But the world for builders has changed tremendously with AI,鈥� he said. 鈥淚t changes what’s possible. This is what 麻豆原创 TechEd is all about: combining both classical and new technologies that unlock the magic for business and provide endless opportunities to innovate.鈥�

From vision to execution

Underscoring his point, Herzig noted that during the 麻豆原创 Sapphire conference earlier this year, the company 鈥減ut out a bold vision to enable the Autonomous Enterprise, re-imagining every business process end-to-end with AI. And just like 30 years ago, the whole tech stack is shifting.鈥�

For 麻豆原创, this is reflected in Joule Work and the new 麻豆原创 Business AI Platform. Herzig described Joule Work as a 鈥済roundbreaking new way to experience software鈥� that taps into 麻豆原创鈥檚 five autonomous domains and assistants that reason and act across 麻豆原创 and non-麻豆原创 data, and 麻豆原创 Business AI Platform as 鈥渢he foundation where you build and extend agents, manage all your data, and govern AI experiences.鈥�

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Designed for better business outcomes

Elaborating, he emphasized that 麻豆原创 Business AI Platform 鈥渋sn鈥檛 just another tech platform, it鈥檚 specifically designed to build better business outcomes, deliver faster time-to-value and better price performance.鈥� In addition, it is enterprise-readiness from day one: secure, governed, and compliant, 鈥渁ll made possible through our unique context layer built on 50 years of domain and industry expertise.鈥�

Together, Joule Work and 麻豆原创 Business AI Platform underpin 麻豆原创鈥檚 commitment to build the Autonomous Enterprise. 鈥淎nd it鈥檚 here today,鈥� Herzig told the audience. 鈥淲e are excited to show you our latest updates at 麻豆原创 TechEd.鈥�

For the rest of the keynote, Herzig and his 麻豆原创 colleagues鈥攊ncluding 麻豆原创 Chief AI Officer Jonathan von Rueden; Manoj Swaminathan, president and chief product officer for 麻豆原创 Autonomous Suite; Tina Berlin, head of AI Foundation EU at 麻豆原创; Michelle Niedernhuber, lead product manager for 麻豆原创 AI Agent Hub; and Soeren Ruder, chief product officer and global head of Customer Experience & Solutions鈥攄etailed these updates and demonstrated them on stage.

Joule Work

Beginning with Joule Work, Herzig said 麻豆原创 had re-designed the user experience from the ground up: 鈥淎ll to make it more delightful and intuitive to use than ever. One starting point. One experience.鈥�

Joule Work now connects all 麻豆原创 applications as well as Office365, Claude Cowork, Glean, and ServiceNow out of the box. It remembers preferences and context from past interactions and continuously adapts. In addition to the conversational interface, shareable spaces that dynamically assemble data into interactive applications are also available.

The keynote speakers described Joule Work as a redesigned, cross-application AI experience available everywhere, including web, desktop, mobile, voice, and even Apple CarPlay. And they noted that Joule Work has been tested for months by more than 110,000 麻豆原创 employees, along with more than 150 customers, who joined the 麻豆原创 Early Adopter Care program, 鈥渁ll so you get the best AI experience.鈥�

One early adopter, Robert Mueller from Bosch Digital, joined Herzig on stage and said his teams had been testing Joule Work, Joule Studio, and the first assistants and agents and that the first user feedback had been positive. 鈥淥ur next step is to bring Joule into daily use,鈥� he said.

麻豆原创 Autonomous Suite

Manoj Swaminathan then took the stage to demonstrate the 麻豆原创 Autonomous Suite. 鈥淔or over five decades, 麻豆原创 has built the systems that help you run your business in the best way possible. Continuing that focus, we now deliver out-of-the-box a suite of 麻豆原创-managed assistants and agents across five horizontal domains, with industry specialization.鈥�

鈥淚鈥檓 excited to announce today 40 assistants and 300 agents between 麻豆原创 and our partners for you to explore,鈥� he added. 鈥淭hese give you a baseline for systems of intelligence and systems of action, built around three priorities, delivering: automation, intelligence and insights, and optimizations, with value that can be realized and measured, across reduction in cost, increased productivity, increased speed, improved quality, and more.鈥�

The new assistants include a Financial Consolidation Assistant that can reduce the time to close the books by up to 68%, a Sourcing Assistant that can reduce total cost of ownership by 15%, and a Supplier Management Assistant that detects and responds to supplier risk 60% faster.

鈥淔or these tasks, an answer that is 鈥榩robable鈥� is just not good enough,鈥� Swaminathan said. 鈥淵ou need the right answer, every single time, and they need to be compliant and traceable. That is what these assistants and agents uniquely enable, because every single one of these agents are ISO and SOC compliant.鈥�

麻豆原创 Business AI Platform

He noted that every agent is natively an Agent to Agent (A2A) endpoint, connected through 麻豆原创鈥檚 A2A gateway, and the platform supports extensions by default. 鈥淔or you as a builder, we鈥檝e made available the 麻豆原创 Business AI Platform, the foundation the 麻豆原创 Autonomous Suite is built on, so you can build to whatever your requirements are in Joule Studio.鈥�

鈥淵ou might ask, 鈥業’m already doing this with support from public models, why build on top of 麻豆原创鈥檚 Business AI Platform?鈥� Because a public model does not know your processes, your master data or your company. 麻豆原创 Business AI Platform does,鈥� Swaminathan shared. 鈥淥ur AI agents are embedded in the deterministic workflows of our applications. They deliver the one true answer as they run on the same data, following the same rules, and leave the same audit trail as our applications and you decide the level of autonomy.鈥�

But he pointed out that the 麻豆原创 Autonomous Suite does not restrict itself to only back-office operations. 鈥淥ur biggest customers are losing 11% of revenue to unplanned downtime experiences with on-site operations. Customers understand the problem but there hasn’t been a solution that responds to these challenges until now.鈥�

Physical AI by 麻豆原创

To address this issue, Swaminathan announced physical AI by 麻豆原创, which extends the Autonomous Enterprise into the physical world, turning business decisions into physical action, and bringing verified outcomes back into 麻豆原创 in a secure and governed way.

During an on-stage appearance, Ricky Watts, who runs the Industrial and Robotics Division of Intel, said the company is already running physical AI at its operational site in Chengdu, where 麻豆原创 warehouse management is driving autonomous robots closing the loop, writing outcomes back into 麻豆原创 as live transactions.

Jonathan von Rueden then took over the keynote stage to talk about the more than 300 standard Joule agents across the suite and how Joule Work and skills can adapt and customize standard agents to get the most from AI. If you want to go further still, he said, 鈥淛oule Studio is just one click away.鈥�

Joule Studio: Extend, build, and integrate your agents

Joule Studio, 麻豆原创’s agent-building platform, lets anyone customize AI with a skill: a natural language description and a few lines of code defining what an agent should do, opening agent-building beyond developers.

If you want to go deeper, and need to know where to begin, the Process Consulting Agent identifies where and what to build before development begins. “Just say what you want to get it done,” said von Rueden. 麻豆原创 Knowledge Graph finds the relevant APIs, MCP servers, and data products, and Joule Studio generates the specifications, structure, and integration logic.

The result: Moving from intent to a working agent takes roughly 2.5 hours instead of 13. Because agents don’t need to guess at requests, they use fewer tokens. “In our testing, that adds up to 50% lower cost per task,” von Rueden told the audience.

“With Joule Studio on 麻豆原创 Business AI platform, we focus on four things: business outcomes, time to value, performance for price, and enterprise readiness with deep integration.”

That last point matters in complex landscapes. With dozens of platforms, data sources, and tools running alongside 麻豆原创 systems, agents need to work across those boundaries safely and with governance, without a custom integration for every new platform.

“You’d expect a certain level of out-of-the-box capabilities and we’ve delivered,” Tina Berlin said. Agent gateway is generally available today, connecting agents and endpoints across any landscape boundary.鈥�

For more complex scenarios requiring rate limiting, routing, protocol translation, and advanced access control, 麻豆原创 is announcing 麻豆原创 Enterprise AI Gateway, built on 麻豆原创 Integration Suite as a natural overlay to agent gateway.

Herzig then returned to the stage to underscore the importance of reliable data for business AI. “Any AI system is only as good as the data it reasons over,” he said, “data that lives in different physical systems, be it 麻豆原创 or non-麻豆原创, on clouds, on-premise, and the edge.”

麻豆原创 Business Data Cloud

鈥淪o how do you bring it together in a consistent, AI-ready way?鈥� The answer, Herzig said, is 麻豆原创 Business Data Cloud, which provides customers with universal business context across their entire multi-cloud landscape. 鈥淎nd thousands of customers are benefitting from the outcomes, time to value, and lower cost already today.鈥�

In addition, he announced that the full scope of the entire Databricks platform will now be available natively in 麻豆原创 Business Data Cloud. This means that data from different systems can now be combined into a 鈥済olden鈥� master record, providing customers with a unified, AI-ready data foundation.

麻豆原创 executives also introduced a new 麻豆原创 Business Data Cloud Ontology Manager, available as beta. With it, customers can enrich 麻豆原创 Knowledge Graph with their industry-specific concepts or build entire ontologies.

A new Governance Assistant ensures compliance with EU regulation, while agent observability, and 麻豆原创 Signavio agent mining close the loop between agent design and production behavior to deliver governance. Runtime verification enforcement ensures that agents not verified in 麻豆原创 AI Agent Hub cannot be invoked.

Governance

All of it is 麻豆原创-managed with governance built in: ISO and SOC compliant, runtime enforced, with every agent testable and traceable via 麻豆原创 AI Agent Hub. And the new 麻豆原创 Enterprise AI Gateway delivers policy enforcement, enhanced observability, and runtime controls all in one place.

鈥淔or AI governance we need to connect the design time layer where we define, assess and verify an agent with the runtime layer that enforces, monitors and optimizes agent behavior,鈥� Michelle Niedernhuber said.

With the EU AI Act now in force, 麻豆原创 is releasing the AI Governance Assistant for the 麻豆原创 Early Adopter Care program customers in November, she shared. The assistant uses existing information, guides the agent owner through the remaining questions, and produces an audit-ready AI risk classification.

Together, 麻豆原创 AI Agent Hub and 麻豆原创 Enterprise AI Gateway serve as the design and runtime integration layer that connects oversight with enforcement. By connecting oversight, runtime enforcement, and continuous improvement, 麻豆原创 gives enterprises confidence that their agents operate as intended.

鈥淕overnance is not what slows AI adoption,鈥� Niedernhuber noted. 鈥淚t is what makes AI adoption at scale possible.鈥�

AI proven under pressure: 麻豆原创 brings AI to space

Rounding out the keynote, Herzig announced that together with partner Hewlett Packard Enterprise (HPE), 鈥溌槎乖� is taking our tabular foundation models, 麻豆原创-RPT and TabPFN, for real-time predictions in space to put AI to work under extreme conditions.鈥�

In space, a delayed decision can mean the difference between system safety and total loss. 麻豆原创’s AI enables faster decision-making and earlier failure prediction, when the stakes are highest.

Herzig explained how the foundation models analyze live hardware telemetry, identify sensor degradation, and reconstruct missing readings in real time. The outcome is a structured report recommending actions to the spaceship crew or ground staff on what to do to prevent a problem from becoming a crisis.

鈥淣ow, if we can run predictions like this in one of the most unforgiving environments, think about what this means for your operations here on Earth. That鈥檚 AI proven under pressure,鈥� Herzig said, bringing the keynote to a close.

Supporting key workstreams, including data management, test management, custom code modernization, and business configuration, these assistants help organizations simplify transformation activities, accelerate project execution, improve decision-making, and increase readiness for cloud ERP adoption.

ERP transformation has become a business imperative for many organizations. Cloud modernization connects organizations to AI, automation, and analytics capabilities that legacy on-premises infrastructure could not support at scale. However, transformation programs can be complex, involving numerous stakeholders and requiring thousands of business and technical decisions.

This year at 麻豆原创 Sapphire, 麻豆原创 introduced new agent-led transformation tooling and outlined how migration and modernization assistants can reduce ERP migration efforts.

Today, four of these assistants are available to early adopter customers and are already supporting live cloud ERP transformation projects.

Migration and modernization assistants go live in customer transformation projects

ERP transformation projects require organizations to assess large volumes of business data, understand quality issues, evaluate migration implications, and prioritize remediation efforts across complex landscapes. The Data Management Assistant analyzes data quality metrics, identifies and prioritizes issues based on business impact, recommends remediation actions, and helps execute corrections through both automated and guided remediation approaches.

AkzoNobel, a global leader in paints and coatings, is currently on a RISE with 麻豆原创 journey, consolidating its ERP landscape as part of a large-scale transformation. Participating in the 麻豆原创 Early Adopter Care program, the company uses the Data Management Assistant to define data quality scope, evaluate issues across the landscape, and prioritize remediation efforts based on business impact. The assistant gives a structured, end-to-end approach to orchestrating workstreams and managing data quality throughout the company’s ongoing transformation.

“The Data Management Assistant gave us clear visibility into our most business-critical data quality issues and helped us prioritize remediation where it matters most,” said Thilak Selvam, Master Data Program Lead at AkzoNobel. “It reduced analysis effort, accelerated our planning, and provided a structured approach to maintaining data hygiene and quality controls as we prepare for future transformation initiatives.”

Defining test scope, preparing test cases, validating business processes, and ensuring representative data is available often requires substantial effort from both business and IT teams. The Test Management Assistant accelerates test preparation and improves testing effectiveness. By analyzing business processes, requirements, and user stories, it helps define test scope, identify critical business scenarios, generate test cases, and determine the test data needed to execute them.

Test cases are handed off to 麻豆原创 Test Automation by Tricentis for automated execution, enabling continuous validation at scale. The result is less manual effort, improved coverage of business-critical processes, and greater confidence in deployment readiness.

The Custom Code Assistant helps organizations modernize their custom developments as part of the transition to 麻豆原创 Cloud ERP Private and continues to support clean core compliance and cloud-ready transformation beyond initial migration.

It supports teams in assessing custom code, identifying migration and remediation opportunities, aligning extensions with clean core principles and 麻豆原创-recommended APIs, and adapting legacy applications for cloud-ready architectures. Acting autonomously, it classifies findings by complexity, applies available automated fixes, and generates AI-driven recommendations and code corrections for the remaining findings. The assistant helps customers determine where to adopt 麻豆原创 standard processes and where custom extensions provide genuine business differentiation. This reduces technical debt, accelerates cloud adoption, and enables organizations to preserve the capabilities that set their business apart while taking full advantage of 麻豆原创 innovation.

For customers adopting 麻豆原创 Cloud ERP, the Business Configuration Assistant supports organizations in accelerating business design and implementation planning. It recommends and autonomously applies configurations tailored to the customer’s industry, segment, and business goals, supporting teams in defining scope, setting up organizational structures, and translating requirements into implementation-ready configurations. It also extracts source configurations, flags deviations from 麻豆原创 target requirements, and maintains a full audit trail鈥攔educing manual effort, improving consistency, and accelerating the path to a production-ready 麻豆原创 Cloud ERP system.

Expanding across the full transformation life cycle

麻豆原创 plans to further expand the migration and modernization assistant portfolio across the transformation life cycle. Planned innovations include the System Analysis Assistant, which creates a customer-specific understanding of existing systems, configurations, and custom developments, providing the foundation for AI-supported guidance throughout the transformation journey and the basis for all other assistants, grounded in customer-specific system data.

In addition, the Project Management Assistant is planned to provide real-time project insights, proactively identify risks, and help orchestrate transformation activities across teams and stakeholders.

The portfolio will also be extended with the Rollout Assistant, which helps organizations align global templates with regional requirements, accelerating local rollouts while reducing effort for business and IT teams through AI-driven guidance.

Together with the Data Management, Test Management, Custom Code, and Business Configuration Assistants available today, these capabilities will offer customers a fully integrated, agent-led transformation experience that helps organizations simplify complexity, accelerate execution, and realize value from 麻豆原创 Cloud ERP faster.

As 麻豆原创 continues to expand its migration and modernization assistant portfolio, organizations will increasingly benefit from AI support across every phase of ERP transformation, from initial system assessment and project planning through implementation, go-live, and beyond.

Learn more at .

Maura Hameroff is chief marketing officer for 麻豆原创 Cloud ERP Private and RISE with 麻豆原创.
Sonja Li茅nard is head of ABAP Platform at 麻豆原创 SE.
Soeren Ruder is chief product officer and global head of Customer Experience & Solutions for 麻豆原创 SE.

The planned collaboration aims to bring together 麻豆原创鈥檚 enterprise application portfolio, 麻豆原创 Sovereign Cloud and 麻豆原创 Business AI capabilities with NTT鈥檚 global infrastructure, full-stack integrated operations, deep industry AI expertise and global customer reach to help customers benefit from cloud and AI innovation while retaining control over sensitive data, operations, technology and security. The planned collaboration reflects the shared ambition of 麻豆原创 and NTT to advance secure, resilient and sovereign digital transformation for regulated industries.

Supporting Sovereign Cloud Requirements 

As an initial area of cooperation, 麻豆原创 and NTT plan to work together to provide a broad set ofsovereign capabilities that combine the 麻豆原创 Sovereign Cloud On-Site offering with NTT-managed infrastructure and operations to customers in key markets. 

麻豆原创 Sovereign Cloud On-Site is 麻豆原创鈥檚 deployment offering for customers with stringent sovereignty, regulatory and compliance requirements. It enables 麻豆原创-managed cloud solutions to be operated in a customer-owned or customer-selected data center. This provides organizations with a high degree of sovereignty across local physical control, data residency and operational assurance, while helping customers address applicable local legal and regulatory requirements.

By combining the capabilities of 麻豆原创 Sovereign Cloud with NTT鈥檚 global reach and expertise in advising, designing, delivering and operating secure infrastructure, the companies intend to support customers in geographies where strong local control is required. These capabilities are intended to help governments, defense organizations, defense industry companies, critical infrastructure operators and other regulated enterprises modernize mission-critical processes while maintaining control over sensitive data and operations and addressing compliance requirements. 

Exploring the Value of AI  

Building on this foundation, 麻豆原创 and NTT intend to explore broader opportunities to help governments, critical infrastructure operators and highly regulated industries modernize mission-critical processes and adopt AI in a secure, responsible and sovereign manner.

鈥淲ith this cooperation, 麻豆原创 and NTT aim to give customers a practical path to cloud and AI innovation while maintaining the control, governance and trust they require,鈥� said Dominik Asam, member of the Executive Board and Chief Financial Officer, 麻豆原创 SE. 鈥淔or public sector organizations and regulated industries, sovereignty is not just a compliance requirement, it is the foundation for resilience, competitiveness and the responsible adoption of AI. Together, we want to help customers innovate while meeting their sovereignty requirements.鈥� 

鈥淥ur customers are looking for practical ways to use AI to solve real business challenges and create new value,鈥� said Yutaka Sasaki, Representative Member of the Board, Senior Executive Vice President and Chief Financial Officer, NTT. 鈥淏y combining 麻豆原创鈥檚 business applications and AI capabilities with NTT鈥檚 deep understanding of industry needs and mission-critical operations, we aim to help organizations transform how they work, make better decisions and accelerate business outcomes through AI.鈥�

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Media Contact:
Clara Villanueva, +1 (610) 235-7247 听clara.villanueva@sap.com, ET
麻豆原创 麻豆原创 Room; press@sap.com

This press release was created with the assistance of artificial intelligence and has been reviewed by the editorial team.
This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ.听 Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
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International Space Station鈥檚 use of tabular AI and expanded partnerships demonstrate the art of the possible with 麻豆原创 AI

BERLIN 鈥� (NYSE: 麻豆原创) today announced a series of AI innovations at its annual 麻豆原创 TechEd conference that expands its portfolio for developers.

The enhancements to the Joule Studio and Joule Work solutions and 麻豆原创 Business AI Platform, including the latest tabular foundation model from 麻豆原创, offer new tools to build and extend AI agents, a richer data and business context for those agents to reason with, and strengthened governance controls to run them responsibly across the enterprise.

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 use no-code and pro-code tools to build and extend agents, choosing from more than 70 AI models. Customers in the Joule Studio early adopter program also can design and run n8n workflows in Joule Studio, with broader availability to follow. The 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph solutions 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. Importantly, 麻豆原创 customers鈥� data remains their data. 麻豆原创 does not share customer data with anyone to train or improve large language models.

鈥溌槎乖� TechEd has always been where builders shape what comes next,鈥� said Philipp Herzig, CTO of 麻豆原创 SE. 鈥淭his year, we are showing how we are moving from AI learning your business to running it. Joule Work is where you put your intent and get work done, in one experience across the entire 麻豆原创 landscape and beyond. That is the shift we are delivering at scale. Everything underneath runs on 麻豆原创 Business AI Platform, delivering faster time to value through out-of-the-box business understanding, higher accuracy, enterprise-readiness that is secure, governed and compliant from the first deployment, and a unique context layer built on over 50 years of process and industry expertise, encoded into every agent action. This is AI built where real work happens.鈥�

Joule Work and Joule Studio Make the Autonomous Enterprise Real

New capabilities for Joule Work will help make AI agents more effective at handling complex business processes via open protocols including Agent2Agent (A2A) and Model Context Protocol (MCP). These innovations advance Joule Work to better understand context, make decisions and execute multistep business workflows across enterprise systems and business domains:

• New agentic thinking capabilities that plan and orchestrate the steps assistants and agents need to reliably complete complex, multistep business workflows across 麻豆原创 landscapes.
• New capabilities in 麻豆原创 Knowledge Graph will incorporate data from additional 麻豆原创 and non-麻豆原创 sources that are unique to each customer. Users can query and explore their customized data in natural language and get accurate answers grounded in their own information.
• Advancements in interoperability allow third-party agents to securely invoke Joule Agents through a governed agent gateway. This enforced access control enables agents from different providers to collaborate across business workflows without sacrificing governance or control.
• New 麻豆原创 Enterprise AI Gateway solution will extend governance to AI interactions, offering a centralized way to manage AI traffic across the enterprise that will consolidate authentication, policy enforcement, routing, monitoring, usage tracking and auditing.
• New Joule Assistant and 麻豆原创 AI Agent Hub expand governance capabilities, and a new AI Governance Assistant helps customers meet compliance requirements for the EU AI Act. These are designed to address critical barriers to scaling enterprise AI: keeping AI systems classified, compliant and operating within defined organizational guardrails and context.

麻豆原创 also announced that Joule Studio has new observability and administrative controls to give developers and administrators visibility into agent behavior, model usage and spend. Joule Studio is 麻豆原创鈥檚 AI-first development environment that is accessible through Joule Work for building and extending enterprise agents. Joule Studio will run a time-bound promotion for select customers, where deploying and running custom agents on the 麻豆原创-managed Joule Studio runtime will remain free through December 2026. The design-time tooling for building and testing agents and n8n workflows with AI-assisted development will remain free under fair-use limits.

麻豆原创 Business Data Cloud as the Grounding Engine of the Autonomous Enterprise

麻豆原创 Business Data Cloud Ontology Manager in 麻豆原创 Business Data Cloud (麻豆原创 BDC) will provide an AI-native authoring experience for building ontologies across 麻豆原创 and third-party data. By helping AI understand how data from different teams, systems and applications relate to one another, AI agents can more accurately reason and make decisions across the enterprise. The solutions complement 麻豆原创 Knowledge Graph, which provides rich business context for 麻豆原创 data.

Additionally, 麻豆原创 is expanding its lakehouse capabilities with Dremio, adding native support for Apache Iceberg and Apache Polaris plus a high-performance, federated query engine that will enable unified data access without moving or copying the data. 麻豆原创 expands its partnership with Databricks, bringing the full scope and latest innovations of data engineering and enterprise AI capabilities into 麻豆原创 Business Data Cloud as 麻豆原创 Databricks. Core innovations such as Lakeflow, Agent Bricks, Unity Gateway, and Lakebase will help customers to simplify data management and provide a strong data foundation for their AI journey. With a deeper integration to 麻豆原创 Business Data Cloud, a new 麻豆原创 Application Data Foundation, as well as an integration between Joule and Genie, 麻豆原创 and Databricks are helping customers establish an enterprise-wide lakehouse as the foundation for the Autonomous Enterprise and Joule Work.

The 麻豆原创 Reltio solution, recently announced at 麻豆原创 Connect, will bring multidomain master data management to 麻豆原创 BDC. It unifies and enriches customer, supplier and product records across 麻豆原创 and non-麻豆原创 systems into trusted, reusable data products that can power applications and AI agents.

Tabular Foundation Model Trusted for Business, Validated in Space

麻豆原创 announced its latest tabular AI model, 麻豆原创-RPT-2.0, which will expand beyond classification and regression to support anomaly and outlier detection, and richer AI-powered analysis through embeddings. These native, multi-table capabilities will further reduce manual engineering while delivering faster, deeper insights from structured data. In addition, a new 麻豆原创-RPT-TS model will help customers predict values across time-indexed business metrics for key forecasting use cases using the 麻豆原创 Integrated Business Planning solution without requiring custom machine learning.

In partnership with HPE, 麻豆原创 also announced the deployment of 麻豆原创-RPT on the HPE Spaceborne Computer-2 aboard the International Space Station (ISS), one of the most significant edge-compute validations of any enterprise AI model to date. 麻豆原创-RPT is planned to run directly onboard the ISS to support anomaly triage, predict issues before failures escalate, reconstruct missing sensor values from historical data, and generate explainable mission-health reports for astronauts and ground teams 鈥� all without requiring data to be sent back to Earth for analysis.

鈥淭he same trusted 麻豆原创-RPT model that powers global supply chains also can work in space,鈥� said Herzig. 鈥溌槎乖粹�檚 deep-rooted, proven expertise has led to the International Space Station鈥檚 use of our tabular foundation model to predict potential mission-critical failures in orbit. With 麻豆原创-RPT and TabPFN from Prior Labs, we bring the unique capabilities of tabular AI into the enterprise and across every frontier.鈥�

Out-of-the-Box Assistants and Agents Define Our Advantage

For five decades, 麻豆原创 has built the systems of record that run your business. We will deliver, out of the box, a suite of assistants and agents across five horizontal domains all running on 麻豆原创 Business AI Platform 鈥� with industry specialization 鈥� that provides a baseline for systems of intelligence and systems of action.

Ecosystem Partners Help Expand Platform Capabilities

The global 麻豆原创 partner ecosystem brings new capabilities to the ongoing operation and extension of solutions on 麻豆原创 Business AI Platform, helping customers advance toward the Autonomous Enterprise. At 麻豆原创 TechEd, the following partner announcements were highlighted:    

A collaboration with Intel closes the loop between enterprise workflows and autonomous robots, bridging informational technology and operational technology through physical AI.

In addition, new 麻豆原创 solution extensions with , and extend autonomous capabilities for customers, helping them run their core processes end to end, without handoffs between systems.

Beyond technology, 麻豆原创 is empowering its strong developer community with new enablement and engagement with tens of thousands joining for leading up to 麻豆原创 TechEd, new reference architectures, tutorials and hands-on opportunities attendees can try onsite at the event and at home.

To learn more about today鈥檚 announcements visit our .

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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 .

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Space is unforgiving. Vacuum, radiation, orbital debris, and solar storms can produce mission-critical crises at any moment. Adding to the complexity, onboard sensors and mission system telemetry produce too much time-sensitive data to be routed back to Earth for analysis. Latency has to be reduced to ensure the safety of the spacecraft. 

Enter 麻豆原创’s tabular AI foundation model, , which makes predictions directly from structured data鈥攅ven with some data missing鈥攁nd without requiring the usual model training or fine-tuning. Running directly onboard the ISS on HPE Spaceborne Computer-2, it supports anomaly triage in real-time telemetry data, predicts issues before failures escalate, reconstructs missing sensor values based on historical data, and generates explainable mission-health reports for astronauts and ground teams.

“This is proof that tabular AI on 麻豆原创 works at the edge of possibility,” said Mayank Shrivastava, vice president of Generative AI and Foundation Models at 麻豆原创 SE. “Space is the ultimate stress test for AI. If a foundation model can predict mission-critical failures in orbit, under radiation, energy, and connectivity constraints, imagine what it can do for a global enterprise. This is the same model that will predict supplier risk, cash flow anomalies, and equipment failures across your enterprise. What works in orbit works in your supply chain.” 

Imagine this: in orbit, data arrives in fragments, connectivity comes and goes, and you cannot send a technician when something starts to fail. You have to anticipate problems with a variety of signals. Enterprises face the same challenge on the ground: a supplier whose deliveries are quietly slipping, a payment pattern that鈥檚 drifting, a compressor that鈥檚 running hot. Predictive models that are able to spot early warning signs in a satellite are also built to find those same signals in your business data, so you can act before small problems become critical ones.

How 麻豆原创 strengthens business functions with models for structured data 

麻豆原创-RPT is part of 麻豆原创’s portfolio of tabular foundation models, or TFMs, that includes the model by Prior Labs, and , which orchestrates, manages, and governs TFMs, predictions, and workflows. Large language models (LLMs) excel at language and reasoning, but TFMs are built for the structured tabular data that underpin real-world operations, such as the sensor-driven data used in this project.  

麻豆原创-RPT is about 50,000 times more energy-efficient than state-of-the-art LLMs. This is especially important in space, where every watt is constrained. 麻豆原创-RPT is also highly adaptable. It requires no pre-training as conditions evolve. 

Outcomes that will be delivered onboard include real-time predictions for computer and mission health status, an overall mission risk score, an assessment of whether manual intervention is needed, human-readable explanations of AI-driven findings, and ad hoc reports for crew and ground teams. 

鈥淗PE Spaceborne Computer-2 continually demonstrates the advantages of having a high-performance compute solution aboard the ISS, supporting advanced technologies and on-going research in space鈥� said Scott Wiest, Vice President and Global Sales Chief Technologist, HPE Fellow. 鈥淒eploying 麻豆原创鈥檚 tabular AI on HPE Spaceborne Computer-2 shows what鈥檚 possible with real-time detection and prediction using AI in the ultimate edge environment.鈥� 

From orbit to enterprise 

The deployment of 麻豆原创-RPT on HPE鈥檚 Spaceborne Computer-2 on the ISS, by early 2027, will show how it strengthens business functions on earth. The constraints of space, including extreme energy limits, intermittent connectivity, radiation interference, and zero tolerance for failure are relevant to challenging use cases on earth. These include disaster relief operations, agriculture in remote areas, distributed manufacturing, and spread-out supply chains.  

Every insight aboard the ISS accelerates 麻豆原创’s road map for enterprise edge AI and key use cases that leverage structured data for the most important analytical and operational processes, even in the most challenging settings.  

Looking ahead, the mission lays the groundwork to support space-based data centers, deep space exploration, and other collaborations on space missions. Our long-term vision for space-based compute infrastructure is anchored in proven, efficient, and accurate AI. 

Explore how to deliver tabular AI on 麻豆原创, learn more about the specialized capabilities behind it, and try the 麻豆原创-RPT AI model with your own data on the . 

Brenda Bown is chief marketing officer for 麻豆原创 Business AI Platform and Joule at 麻豆原创.

AI has gone from insight to action and now, with physical AI, it comes into the physical world.

Enterprises have made substantial investments in both AI and physical AI. AI helps business systems deliver remarkable insights and recommendations on financials, inventory, and supply chains. Physical AI creates value every day with physical systems鈥攕uch as industrial robots, humanoids, sensors, or robot dogs鈥攄eployed across production lines, warehouses, field service teams, and logistics networks.

As automation, robotics, and sensing technologies have matured across industries, the volume and richness of the physical signals coming from physical execution has grown substantially; physical operations today generate continuous intelligence about factors such as asset health, task completion, quality, throughput, and exceptions.

Today, however, there is a gap between what the business knows and the intelligence generated by physical operations on the ground. This gap is one of the most common sources of operational friction across industries. It contributes to delayed responses, ill-informed decisions, and physical operations that never enter the business record. As a result, revenue and margins decrease while risk exposure increases.

From enterprise intelligence to physical action

This year at 麻豆原创 TechEd, 麻豆原创 revealed how our physical AI removes this operational friction: the system that runs your business is the same system that controls your physical operations.

The enterprise knowledge in your business system triggers the physical actions; physical signals arrive with the business context already attached;听physical operations in the field flow back into 麻豆原创 as structured data intelligence. Every physical action leaves a traceable, auditable record tied to the relevant business process.

The full context of your business is behind every physical action; verified outcomes are brought back into 麻豆原创 in a secure and governed way for follow-up actions.

When the system that runs your business is also the system that governs how, when, and why physical tasks are executed, the result is a fundamentally different kind of operational capability. For enterprises already running 麻豆原创, this means physical execution inherits the intelligence, context, and governance already in place.

This is how physical AI by 麻豆原创 transcends automation and extends the Autonomous Enterprise into the real world.

“Foxconn’s vision for intelligent manufacturing requires physical systems that understand business context, not just motion commands,” said Dr. Wang, head of Physical AI at Foxconn. “With physical AI by 麻豆原创, warehouse tasks from 麻豆原创 Extended Warehouse Management directly orchestrate our humanoid robots and autonomous transport systems. Asset inspections triggered by maintenance plans flow to our robotic platforms and return verified outcomes back to 麻豆原创. This closed loop between enterprise systems and physical execution is what sets this collaboration apart: the robot knows why it acts, and the business system knows what happened.”

How physical AI by 麻豆原创 capabilities are designed to transcend automation

1. Business-driven physical action

Physical AI is built to ground every physical execution in the business context: order priorities, safety rules, inventory status, and compliance constraints. Every action has a clear reason tied to business outcomes, and the business system remains in control of what gets executed, when, and why. The result is physical execution that is intelligent from the start, not just automated.

2. Physical evidence into digital workflows

Every physical task creates a complete audit trail and physical observations flow back into 麻豆原创 automatically, transforming field outcomes into business insights rather than isolated records in a separate system. Physical world events become inputs to the decisions and processes that follow.

3. Enterprise-governed execution

Built on the 麻豆原创 Business AI Platform, physical AI capabilities are designed to provide traceability, auditability, and human-in-the-loop control as first principles. Physical world data flows into 麻豆原创 Business Data Cloud as new structured data products, enriching planning, predictions, and operational strategy over time.

4. Vendor and hardware agnostic

Working across device platforms, physical systems, and AI models of the customer’s choosing, customers retain freedom over their physical layer. 麻豆原创 provides the business context, orchestration, and governance. Partners provide the physical execution.

Physical AI at work with customers and partners

础谤莽别濒颈办-尝骋: physical AI executes and records inbound storage tasks

础谤莽别濒颈办-尝骋 is a joint venture between Turkish durable consumer, Ar莽elik and South Korea鈥檚 LG Electronics, manufacturing air conditioners. Physical AI by 麻豆原创 enables a humanoid robot to execute and record inbound storage tasks. This has addressed the costly and persistent issue of incorrect storage placements in inbound logistics with potential savings of millions of euros.

• Associated 麻豆原创 solutions: 麻豆原创 Extended Warehouse Management
• Physical AI ecosystem partners:
• Video:

Intel: physical AI in autonomous warehouse operations

Intel is one of the world’s largest semiconductor manufacturers, operating a vast global warehousing and logistics network and serves as both a听 proof-of-concept environment and a technology collaborator. By integrating 麻豆原创鈥檚 enterprise workflows and warehouse processes with Intel-powered edge AI and robotics technologies, warehouse transport tasks generated in 麻豆原创 are automatically translated into robot missions and executed autonomously in the warehouse.

麻豆原创 provides the business context, workflow governance, and process orchestration, ensuring that every physical action aligns enterprise priorities and operational goals. Intel provides the physical AI hardware and software foundation at the edge, delivering the compute AI and real-time robotics capabilities that enable autonomous systems to perceive, decide, and act in dynamic environments. The combined capabilities of 麻豆原创 and Intel create a closed-loop system; business decisions become physical actions and operational outcomes are continuously fed back into business systems.

In a proof of concept demonstrated at Intel鈥檚 Chengdu site in China, material movement is automated end-to-end, reducing both transport distances and repetitive manual handling. Physical AI capabilities from 麻豆原创 and Intel demonstrate how enterprise decisions can directly drive physical execution in logistics workflows.

• Associated 麻豆原创 systems: 麻豆原创 Extended Warehouse Management
• Physical AI ecosystem partners:

Holcim: industrial asset inspections

Holcim is a Swiss multinational building materials and sustainable construction company, operating in heavy industry. The company has significant infrastructure assets with inspection, maintenance, and safety use cases. Since attending the Embodied AI Jam held in August 2026, Holcim has implemented physical AI capabilities for asset inspections with industrial drones and quadruped dogs.

On-site quadruped dogs inspect conveyor belts in dark, dusty areas and the status of kilns in extreme temperatures. Drones inspect assets in heights difficult for humans to reach. And end-to-end automation starts and finishes in a 麻豆原创 system. In 麻豆原创鈥檚 asset management solution, the maintenance plan triggers an action for the industrial drone/quadruped dog; once the action is executed, a maintenance notification is created back into 麻豆原创.

• Associated 麻豆原创 systems: 麻豆原创 Plant Maintenance
• Physical AI ecosystem partners: , ,
• Video:

Start your physical AI journey today

From warehouse operations to asset maintenance, from safety inspections to keeping your operations compliant, physical AI capabilities from 麻豆原创 have the potential to transform your business. It connects the intelligence of your enterprise with real-world execution; the 麻豆原创 system that you trust to run your business is the same system that informs the actions of your physical systems, be it a robot, an industrial drone, or a robodog.

As a part of Industry AI, you can begin your physical AI journey today with warehouse operations and asset inspection use cases or a physical AI use case, tailor-made to your requirements. Contact 麻豆原创 via email for more information.

Lukasz Ostrowski is global head of Physical AI and Robotics at 麻豆原创.

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

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

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

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

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

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

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

Now, let鈥檚 dive into recent innovation.

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

Joule

Joule Work

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

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

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

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

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

Joule Studio

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

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

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

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

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

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

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

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

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

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This capability is generally available.

Autonomous SCM

Product Design Assistant

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

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

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

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

Logistics Assistant

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

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

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

Manufacturing Assistant

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

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

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

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

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

Supply Chain Planning Assistant

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

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

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

Asset Operations Assistant

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

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

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

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

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

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

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This capability is generally available.

Autonomous HCM

HR Service Assistant

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

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

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

HR Operations Assistant

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

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

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

Performance and Goals Assistant

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

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

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

HR System Assistant

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

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

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

Autonomous Finance

Billing Assistant

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

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

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

Financial Closing Assistant

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

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

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

Cash and Treasury Assistant

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

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

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

Financial Consolidation Assistant

• Financial Consolidation Data Submission Agent
• Financial Consolidation Orchestration Agent

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

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

Sales Operations Assistant

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

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

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

Recurring Receivables Assistant

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

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

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

Accounts Receivable Assistant

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

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

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

Tax Compliance Assistant

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

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

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

International Trade Assistant

• International Trade Product Profile Agent
• International Trade Classification Agent

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

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

Accounts Payable Assistant

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

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

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

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

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

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This capability is generally available.

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

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

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This capability is generally available.

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

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

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This capability is generally available.

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

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

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This capability is generally available.

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

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

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This capability is generally available.

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

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

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This capability is generally available.

Autonomous Spend Management

Purchasing Assistant

• Purchase Order Lifecycle Management Agent

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

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

Supplier Management Assistant

• Supplier Management Identification and Optimization Agent

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

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

Procurement Contract Assistant

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

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

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

Category Management Assistant

• Category Strategy Development Agent
• Market Insights Agent

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

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

Invoicing Assistant

• Invoicing Orchestration Agent

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

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

Receiving Assistant

• Return Goods to Supplier Agent

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

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

Requisition Assistant

• Purchase Requisition Creation Agent
• Purchase Requisition Optimization Agent

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

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

Operational Procurement Assistant

• Invoice Reminder Agent
• Material Spend Advisory Agent

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

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

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

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

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This capability is generally available.

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

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

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This capability is generally available.

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

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

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This capability is generally available.

Concur Travel, AI-assisted trip changes

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

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This capability is generally available.

Concur Expense, AI-assisted approval manager

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

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

Concur Expense, AI-assisted corporate card management

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

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

Concur Travel, AI-assisted travel filters

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

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This capability is generally available.

Concur Travel, AI-assisted travel search

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

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This capability is generally available.

Autonomous CX

Case Management Assistant

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

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

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

Deal Qualification Assistant

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

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

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

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

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

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This capability is generally available.

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

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

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This capability is generally available.

麻豆原创 Engagement Cloud, AI-assisted content composer

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

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This capability is generally available.

麻豆原创 Business AI Platform

Mistral on 麻豆原创 Business AI Platform

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

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This capability is generally available.

麻豆原创 Analytics Cloud, AI-assisted prompt insights

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

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This capability is generally available.

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

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

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This capability is generally available.

麻豆原创 Datasphere, AI-assisted SQL generation

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

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This capability is generally available

Generative AI hub enhancements

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

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

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

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

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

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

Learn more here and . .

Enterprise Architecture Assistant

• Enterprise Architecture Management Agent

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

Get started .

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

Data Management Assistant

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

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

Get started .

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

Process Transformation Assistant

• Process Consulting Agent

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

Get started .

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

ALM Dashboard Generation Agent

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

Get started .

This capability is generally available.

ALM Alert Resolution Agent

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

Get started .

This capability is generally available.

麻豆原创 Regulatory Insights

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

Get started .

This capability is available as beta release.

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

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

Get started .

This capability is generally available.

Industry AI

Spare Parts Request Processing Agent

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

Get started .

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

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

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

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

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

Making commerce more intelligent with AI

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

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

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

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

Supporting complexity at enterprise scale

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

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

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

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

Connecting commerce with your operations

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

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

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

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

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

Looking ahead

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

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

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

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

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

Gartner, Magic Quadrant for Digital Commerce Platforms, Aditya Vasudevan, Penny Gillespie, Sandy Shen, Jason Daigler, Mike Lowndes, 19 October 2026.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner鈥檚 research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.
麻豆原创 was recognized as 麻豆原创 Hybris in 2014, 2016 and 2017.

At a glance

• Current cloud backlog of 鈧�24.1 billion, up 28% and up 26% at constant currencies
• Cloud revenue up 26% and up 25% at constant currencies
• Cloud ERP Suite revenue up 29% and up 28% at constant currencies
• Total revenue up 13% and up 12% at constant currencies
• IFRS operating profit up 18%, non-IFRS operating profit up 18%, both at nominal and constant currencies
• 麻豆原创 updates its 2026 cloud revenue and cloud and software revenue outlook

Christian Klein, CEO:

鈥淨3 was an outstanding quarter, with current cloud backlog up 26% at constant currencies. Our Autonomous Enterprise strategy is not just resonating, it is being adopted. Our customers are investing in the technology behind it 鈥� from the cloud and data foundation to our Business AI. They see that Joule Work makes the business case for their transformation more attractive than ever.鈥�

Dominik Asam, CFO:

鈥淔or the first time, 麻豆原创 crossed 鈧�10 billion in total quarterly revenue and 鈧�3 billion of non-IFRS operating profit, reflecting the strength and scalability of our business model. Our disciplined approach to cost management, paired with continued cloud momentum, demonstrates our ability to grow both the top and bottom line at the same time.鈥�

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 to 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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Aim茅e Leabon, +1 (212) 653-9600, aimee.leabon@sap.com, EDT
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This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
漏 2026 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see for additional trademark information and notices.

This is the sixth time that 麻豆原创 LeanIX has positioned as a Leader in the annual report.听

We believe this reflects our unique strength in helping enterprises breathe life into their enterprise architecture data, discover and govern their AI portfolios, and make rapid transformation a business capability听

The analyst firm positioned 麻豆原创 LeanIX furthest in Completeness of Vision and highest in Ability to Execute, once again. The news is particularly relevant for business leaders as enterprise architecture tools are becoming increasingly important for AI adoption and governance.

Why enterprise architecture matter for AI

Enterprise architecture tools document up-to-date information about organizations鈥� application portfolios and tech stacks. Visibility on this information is essential for leveraging agentic AI and governing it to ensure return on investment.

Enterprise architects are not in the driving seat for AI governance, but with the right tools, they can provide the data chief AI officers and AI governance boards need to make rapid, informed decisions. The 麻豆原创 LeanIX portfolio is able to discover, store, and provide that essential enterprise architecture intelligence.听

鈥淥ur goal is to empower our customers to confirm return on technology investment in the AI era with dynamic, enterprise architecture intelligence that helps drive business transformation,鈥� 麻豆原创 LeanIX Chief Product Officer Dominik Rose said. 鈥淭ransformation never ends, but the AI revolution is the most fundamental step forward that technology has taken this century. Our business transformation solutions empower organizations to take that step in their stride with comprehensive, current EA data.鈥�

What is the Gartner Magic Quadrant for EA Tools?

A Gartner Magic Quadrant is a culmination of research in a specific market, offering a wide-angle view of the relative positions of the market鈥檚 competitors.鈥疊y applying a graphical treatment and a uniform set of evaluation criteria, a Magic Quadrant helps quickly ascertain how well technology providers are executing their stated visions and how well they are performing against Gartner鈥檚 market view.

A Magic Quadrant provides a view of the four types of technology providers in any given field: 

• Challengers execute well today or may dominate a large segment, but do not demonstrate an understanding of market direction.
• Niche players focus successfully on a small segment or are unfocused and do not out-innovate or outperform others.
• Visionaries understand where the market is going or have a vision for changing market rules, but do not yet execute well.
• Leaders execute well against their current vision and are well-positioned for tomorrow.

麻豆原创 LeanIX has been positioned as a Leader in the Gartner Magic Quadrant for Enterprise Architecture Tools for six consecutive years, and this year was positioned furthest in Completeness of Vision and highest in Ability to Execute.

For enterprise architects and senior leaders, Gartner evaluates EA Tools against real-world enterprise needs, including: 

• Capturing, structuring, analyzing, and presenting models
• Supporting change, transformation, and optimization
• Assessing and managing an evolving IT portfolio
• Managing EA knowledge and resources to address stakeholder needs
• Identifying and tracking sustainability initiatives

To see the full analysis and learn why 麻豆原创 was once again listed as a Leader for EA tools, .

Neil Sheppard is a senior content marketing specialist for 麻豆原创 LeanIX.

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 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 research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are trademarks of Gartner, Inc and/or its affiliates.

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

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

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

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

Autonomous sales and revenue: from leads to qualified opportunities

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

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

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

Autonomous service: from case resolution to lasting customer relationships

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

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

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

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

Autonomous marketing and engagement: from campaigns to customer engagement

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

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

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

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

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

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

Unified commerce innovations

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

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

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

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

The next stage of customer experience

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

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

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

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

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

What the cloud introduced, AI has only accelerated: constant software updates, new capabilities, and a faster pace of innovation. For professionals working across the 麻豆原创 ecosystem, keeping pace with that change is becoming an increasingly important part of maintaining their expertise.

For consultants, developers, architects, project managers, and sales and presales professionals, staying current means understanding not only what has changed, but how those changes apply in their work and in the customer situations they encounter.

has evolved alongside this changing technology landscape. As cloud solutions introduced faster release cycles, regular Stay Certified assessments became part of the certification journey, helping professionals keep their knowledge and credentials current.

Building on this evolution, 麻豆原创 is reimagining the Stay Certified experience. Across more than 100 certifications, certified professionals will have greater choice in how they demonstrate that their knowledge remains current. Alongside a knowledge-based assessment option, learners will be able to complete short, practical, AI-powered roleplays designed around real-world customer interactions.

From a stand-alone assessment quiz to scenario-based roleplays

The evolution of Stay Certified reflects a broader shift already taking place across 麻豆原创 Certification: moving beyond assessing what someone knows toward validating how they can apply that knowledge.

AI-powered roleplays bring that principle into the annual certification renewal experience.

Rather than only responding to questions about 麻豆原创 products and capabilities, learners can work through simulated customer interactions built around defined knowledge goals. The scenarios are grounded in content reflecting the latest 麻豆原创 product releases, helping learners demonstrate applied, up-to-date knowledge. After each assessment, learners also receive immediate, personalized feedback highlighting what went well and where they can improve, specific to the scenario rather than a generic score.

Learners who prefer or require a non-AI pathway will continue to have the option of completing a stand-alone assessment quiz.

Keeping pace with 麻豆原创 innovation

As 麻豆原创 products evolve, so does the knowledge required to work with them effectively.

The reimagined Stay Certified approach is designed to keep renewal closely aligned with those developments. Assessments are grounded in content reflecting the latest 麻豆原创 product releases, helping learners focus on the knowledge and capabilities that have changed since getting certified or the latest certification renewal.

For professionals, this creates a clearer connection between maintaining a credential and maintaining relevant expertise. For customers, this means credentials can continue to reflect hands-on, up-to-date expertise aligned with current 麻豆原创 product capabilities.

The approach builds on the broader evolution of 麻豆原创 Certification toward practical, skills-based validation. 麻豆原创 has expanded certification beyond traditional multiple-choice exams to include designed to validate how professionals apply their expertise.

Stay Certified now brings more of that same focus on application into the ongoing certification journey, alongside greater flexibility in managing certification renewal. A new 90-day will allow learners to pass their Stay Certified assessment within the 90-day window, and their certification continues seamlessly based on the original expiry date. Personalized, time-bound reminders will help learners understand when action is required, while improvements within the My Learning section on the 麻豆原创 Learning site will provide clearer visibility into renewal deadlines and certification expiry.

Bringing hands-on validation to 麻豆原创 TechEd

麻豆原创 is also exploring how other practical learning experiences can play a greater role in staying certified.

At , 麻豆原创 will pilot an event-based approach that allows eligible learners to fulfill their Stay Certified requirement through participating in on-site, hands-on sessions.

The approach reflects the same principle behind the new roleplay assessments: practical product knowledge counts. Participation in hands-on sessions can be credited toward certification renewal, providing another way for learners to demonstrate current knowledge.

Staying certified in an era of continuous change

Earning a certification represents an important milestone. In a technology environment that changes continuously, however, expertise cannot remain static.

New releases, capabilities, and AI-powered ways of working mean professionals need to continually revisit what they know, understand what has changed, and apply that knowledge in new contexts.

Reimagined Stay Certified brings that principle more directly into certification renewal.

AI-powered roleplays and the hands-on pilot at 麻豆原创 TechEd expand the ways learners can demonstrate practical, up-to-date knowledge as part of the Stay Certified experience.

As 麻豆原创 technology continues to evolve, staying certified is about more than maintaining a credential. It is about demonstrating that the expertise behind it continues to evolve, too.

Diana Alexandra Roesner is head of Certification Transformation at 麻豆原创.

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

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

The enterprise reality

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

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

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

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

What native integration unlocks

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

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

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

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

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

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

Validation from our customers

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

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

Suite advantage is a strategic choice, not a convenience

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

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

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The story began in 1996, when around 700 developers and technical experts gathered in Orlando, Florida, to explore a new generation of enterprise software.

鈥淚t wasn鈥檛 called ‘TechEd’ back then,鈥� explained Karl Kessler, vice president of Product Management for ABAP AI at 麻豆原创, who attended the Orlando event as a product manager for 麻豆原创 Technology. 鈥淚t was originally called the 麻豆原创 Technical Developers Conference.鈥�

The event came at a time of technological change. 麻豆原创 was opening up new ways to make 麻豆原创 R/3 business processes accessible over the internet. BAPI programming interface provided access to 麻豆原创 business logic through defined interfaces, while ALE made it easier to exchange messages between distributed systems. 麻豆原创 was also working on a 鈥渂usiness framework鈥� designed to integrate distributed business processes.

This raised a question that seems obvious today: how could enterprise software evolve into a platform for an increasingly connected IT landscape?

From technical training to hands-on experience

The event gave developers an opportunity to explore these new possibilities. But its format was still a far cry from the multifaceted platform for knowledge sharing, experimentation, and community that 麻豆原创 TechEd would become over the following three decades.

鈥淚n the beginning, it was all very academic,鈥� Kessler recalled. 鈥淭here were no keynotes or product demos on big stages. We went straight into an introduction to the architecture of 麻豆原创 R/3, and the program consisted mainly of lectures: less conference spectacle, more technical classroom.鈥�

The second event, held in Los Angeles later in 1996, offered a glimpse of how quickly the 麻豆原创 ecosystem was evolving. 麻豆原创 and Microsoft showcased new possibilities involving Internet Explorer, SQL Server, and Microsoft Office 97. Hands-on sessions were introduced for the first time, giving participants the opportunity to work with 麻豆原创 software themselves.

鈥淎nd 鈥榟ands-on鈥� really meant getting your hands dirty back then,鈥� Kessler emphasized. 鈥淧articipants had to install the software on their own laptops. Ready-to-use images that you can deploy in just a few clicks today? Not a chance.鈥�

For the first time, participants had access to a software environment where they could experiment for themselves鈥攕omething that has since become a staple of 麻豆原创 TechEd.

Where technology meets community

In the years that followed, this shift continued. With the introduction of 麻豆原创 NetWeaver, technologies like Java, portals, the web, and XML became increasingly prominent alongside the traditional ABAP world. 麻豆原创 TechEd was evolving from an event where technology was explained into a place where developers could try it out and discuss it for themselves.

The community, too, began to play a bigger role. The 麻豆原创 Developer Network created new opportunities for exchange: knowledge was no longer shared only from the stage, but also between sessions, online, and across company boundaries.

Kessler witnessed this evolution firsthand. From 1996 until the onset of the pandemic, he attended every 麻豆原创 TechEd event in the U.S. and Europe. He initially took part as a product manager and speaker, later helping shape the program as a track lead and, for several years, an agenda lead.

Throughout the 2000s, 麻豆原创 TechEd increasingly became a community experience. Kessler has particularly vivid memories of the events in Las Vegas: 鈥淒uring the day, there were lectures and four-hour hands-on sessions. But the day didn鈥檛 end when the official program did. People went out for dinner together, saw shows, or met up at the hotel in the evening. Many of us were there for practically a whole week. And if we had time afterward, we might take a trip to the Grand Canyon or Death Valley.鈥�

By then, 麻豆原创 TechEd had become much more than a conference program. Alongside the technical content, it was the encounters, conversations, and shared experiences that helped shape the community.

New formats emerged from around 2000 onward, further strengthening that community spirit. Demo Jam gave developers a stage to showcase what they had built. The Community Clubhouse created space for conversations beyond traditional presentations. Roadmap sessions offered a look at the 麻豆原创 technologies to come, while hands-on sessions turned attendees from listeners into active participants.

鈥淥ver time, exchanging ideas became at least as important as the technology itself,鈥� Kessler recalled. 鈥淭he more complex the topics became, the more important it was to share experiences, try things out together, and learn from one another.鈥�

Technology was no longer simply presented. It was explored, discussed, and developed further–together.

When the future becomes the present

Throughout the 2000s, the technological foundations of enterprise software continued to evolve. Initially, the focus had been on connecting 麻豆原创 systems with other applications through open interfaces and web technologies. Increasingly, however, a different question came to the fore: what if vast amounts of data could be made available almost in real time, without having to move and process them so extensively first?

Around 2010, 麻豆原创 TechEd brought this idea to life. Vishal Sikka introduced 麻豆原创 HANA and in-memory computing, an approach that processes data directly in main memory. It challenged established assumptions about database performance. Initially focused on analyzing large volumes of data, 麻豆原创 HANA later became a foundation for applications as well.

For 麻豆原创 TechEd, this marked another step forward. The event was no longer just about learning new technologies; it was also about exploring their potential and understanding what they could mean in practice. An idea that had once sounded like science fiction became the starting point for new applications and business processes.

鈥淚t was the transition from 鈥榃hat if?鈥� to 鈥楾his is how we do it,鈥欌�� Kessler said.

Meanwhile, the community continued to evolve. The 麻豆原创 Mentor program gave particularly engaged community members a new role as sounding boards, ambassadors, and representatives of the wider community. Technology and people were evolving together.

鈥淣ot every prediction from back then came true exactly as expected,鈥� Kessler reflected. 鈥淪ome ideas disappeared, while others developed in completely different ways. But time and again, concepts that had sounded futuristic at 麻豆原创 TechEd went on to become part of everyday working life.鈥�

With 麻豆原创 Fiori, 麻豆原创 S/4HANA, and the cloud, new topics arrived at 麻豆原创 TechEd that would go on to shape the 麻豆原创 landscape. Developers could experience and discuss how enterprise software was changing鈥攁nd what those changes meant for their own work.

The focus was no longer just on what a system could do technically. Increasingly, the questions were about how enterprise software should be used: on which devices, through which interfaces, with what data, and with how much flexibility.

The future was becoming more tangible. It was no longer confined to architecture diagrams and technical concepts; it was visible on screens, in mobile applications, and in cloud environments.

At 麻豆原创 TechEd, these changes were not just presented but explored and developed further together. What once opened up new possibilities became an integral part of the 麻豆原创 landscape.

The next future Is already taking shape

Over the years, 麻豆原创 TechEd continually found new ways to bring technological developments to life and bring the 麻豆原创 community together. New formats, greater interaction, and hands-on experiences had all helped shape the event. The community had long since become much more than an audience: it was an essential part of what made 麻豆原创 TechEd what it was.

Then came 2020. 鈥淭he global pandemic suddenly took away so much of what we had come to take for granted,鈥� Kessler said. 鈥溌槎乖� TechEd still took place, but it went digital. There were no exhibition halls where people could meet, no spontaneous reunions or conversations in the corridors, no discussions after a session that unexpectedly sparked a new idea.鈥�

Hands-on experiences and exchanges with others had to take place under entirely different conditions. Something that had grown over many years could not simply be replicated online, one-to-one. 鈥淭hat鈥檚 when we realized just how much 麻豆原创 TechEd depends on personal interaction,鈥� Kessler recalled.

One of Kessler鈥檚 most memorable experiences shows how important that interaction was, even behind the scenes: the 鈥渟peaker ready room.鈥� Every year, around 130 speakers gathered in a large ballroom during 麻豆原创 TechEd week to prepare for their sessions and help one another out. If they ran into problems with connectivity, licenses, updates, or integrating different components, they worked together to find solutions鈥攐ften on the spot and under time pressure.

Over the years, this gave rise to a speaker community made up of familiar faces and newcomers who worked alongside 麻豆原创 IT and the event organizers to make 麻豆原创 TechEd happen. For Kessler, the ready room embodies the event鈥檚 distinctive sense of teamwork. Everyone wanted the sessions to succeed and participants to have the best possible experience. The shared mindset was simple: we鈥檒l make it work鈥攖ogether.

In 2026, 麻豆原创 TechEd finds itself at another technological turning point. Artificial intelligence is changing how software is developed, used and increasingly executed. Developers are working with AI-powered tools. Applications can take on tasks. Agents can execute processes and interact with one another.

鈥淲e have no more idea today of what will be commonplace 10 years from now than developers in 1996 could have known which of their futuristic ideas would eventually become standard practice,鈥� Kessler said. 鈥淏ut that鈥檚 exactly the point. The future looked different at every 麻豆原创 TechEd, yet the questions behind it have remained much the same: how can technology better support people? How can systems work together? How can we make complex processes simpler? What becomes possible when technical boundaries are pushed?鈥�

So, what connects the old hands attending their 30th 麻豆原创 TechEd with the young talents joining for the first time in 2026?

Kessler鈥檚 answer is simple: 鈥淲e all want to know what comes next鈥攁nd discover together what鈥檚 possible.鈥�

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.

• How can this gap be closed? The answer lies in helping companies detect risks, defaults, losses, and liquidity pressures sooner鈥攁nd manage them more effectively.

For Germany and Europe, it is not an abstract challenge, but the key to future prosperity. Our economic strength lies in industry: in mechanical engineering, in the automotive supply chain, in complex manufacturing and logistics processes. According to German Economic Institute estimates, AI can generate around 鈧�440 billion of additional gross value in Germany by 2034.

Good, but not in every case

To get the economy moving again, we need a different kind of AI. Because it is not enough to process and generate language; AI must also be able to deal with data that companies work with the most. And this is where common large language models (LLMs) reach their limits. Structured data鈥攆or example, numbers in tables鈥攈elp companies understand what is happening right now and what may come next.

This requires AI models that understand the relationships among rows, columns, entities, and numbers. This is exactly what current research results show: from the year 2026 prove that even high-performance LLMs perform significantly weaker in table reasoning tasks鈥攆or example, in the ability to draw conclusions from tables and to recognize relationships鈥攖han in language tasks.

When data anticipates what’s next

Tables follow their own logic. Values do not stand next to each other in isolation, but in relationships between columns, rows, timeframes, and business transactions. Accordingly, tabular AI is designed to recognize patterns across columns as well as in the associated tables that make up a company system. The real added value lies in this networked context. Looking at a customer ID in isolation is one thing. Seeing which orders this customer places and predicting warehouse requirements from these data is a completely different thing.

A practical example: a manufacturer wants to identify early which customers are most likely to pay late. For this, a single piece of information is not enough. Among other things, relevant factors include payment history, order history, delivery data, and external industry information. Only the interaction of these data makes reliable predictions possible. That’s precisely what tabular AI models are built for.

What this means for AI investments

Classic machine learning methods are still used in many companies today for predictions based on structured data. They often deliver good results, but typically have to be developed, trained, and operated separately for each individual application: one model for demand forecasting, another for payment default risks or for prioritizing sales contacts, each with data preparation, training, monitoring, and ongoing maintenance.

Tabular foundation models change this equation. They do not have to be redeveloped for every business question but are universally applicable. This creates an immense scaling effect. In the future, instead of hundreds of specialized models, a single one鈥攕uch as 麻豆原创-RPT-1.6, which we have just made available to customers鈥攚ill suffice. With this, companies can build on historic examples and existing patterns from their business data in order to identify new risks and opportunities at an early stage.

For companies, this means a concrete shift: AI predictions will no longer have to be developed as complex individual projects in the future. In other words, the path from a specific question to the universal use of a model for any questions becomes shorter and cheaper.

In addition, since tabular foundation models are not pre-trained on individual use cases, but on large amounts of data, they can also recognize patterns across different use cases that specialized individual models, especially in limited data layers, often overlook. Therefore, in addition to the scaling effect, there is also a gain in knowledge, which previously did not exist in the analytical processing of tabular data.

New category for enterprise AI

At 麻豆原创, we are working on the next level of enterprise AI. With the acquisition of Prior Labs in July, we are further accelerating this path: combining Prior Labs鈥� basic research with the corporate reality of our customers, and the experience of 麻豆原创 in the application of AI in business-critical business processes.

With TabPFN-3, Prior Labs has developed one of the most powerful open-source models for tabular predictions. Together with the tabular foundation models of the 麻豆原创-RPT family and 麻豆原创 Knowledge Graph, this creates an infrastructure that securely generates structured predictions directly from the company-specific data with the individual business context.

In general, now we are seeing a trend toward various types of foundation models. In addition to tabular foundation models, models that have been trained on the basis of time series鈥攆or example, a large number of measurements鈥攁re being created. You can evaluate the measurements, recognize patterns, and thus also predict numerical values over time.

Conclusion

The first wave of generative AI is making employees more productive. The next one can make companies more forward-looking. The decisive factor here is not whether companies use LLMs or tabular foundation models. It is crucial to combine the two where they provide the greatest benefit: LLMs for interaction with language and knowledge, tabular models for forecasts based on structured business data.

Done right, AI becomes more than a productivity tool. It becomes a driver of better business decisions鈥攁nd a foundation for a stronger economy.

To learn more, read (in German).

Its real potential emerges when AI connects with the data and processes behind public services, turning a citizen request into coordinated action across the administration.

For public institutions, there is another essential requirement: innovation must go hand in hand with transparency, accountability, and digital sovereignty. 麻豆原创 combines decades of process expertise with enterprise applications, data, AI, and sovereign cloud technologies.

S.Public, the new 麻豆原创 Experience Center location for the public sector, brings these capabilities to life. It officially opened in Berlin on October 12, 2026.

From a citizen request to coordinated action

Damage in a public space can be reported digitally in a matter of moments. But that is where the actual administrative process begins: determining responsibilities, planning the work, checking resources and budgets, procuring materials if needed, and closing the case. 麻豆原创 solutions can connect citizen engagement, maintenance, procurement, and financial management across this end-to-end process.

At S.Public, a playground scenario makes this tangible. A citizen notices damage, such as a broken swing, and reports it by smartphone. The steps that follow show how a single report becomes coordinated action. The real innovation is not the reporting form itself, but the intelligently connected processes behind it.

When AI understands the administrative context

The same principle applies to public funding. Applications involve documents, eligibility requirements, and financial information. AI can check submitted materials for completeness and consistency, flag missing information early, and provide employees with an explained recommendation for further review. The final decision remains with the responsible public authority.

A funding application for a solar installation illustrates this approach in Berlin. The 麻豆原创 capability behind it is broadly applicable: AI is embedded where employees evaluate information and manage processes. Rather than automating isolated tasks, it supports the broader administrative context.

When digital sovereignty meets AI

How can sensitive data be used with AI while remaining under an organization’s own control? 麻豆原创 Sovereign Cloud addresses four dimensions of digital sovereignty: data, operational, technical, and legal sovereignty.

At S.Public, this concept becomes something visitors can actually walk through. The new Sovereign Cloud Showroom makes 麻豆原创’s security and sovereignty concept physically tangible for the first time. Visitors pass through three security zones: administration, control, and restricted zones. They experience firsthand how the operating environment for 麻豆原创 Sovereign Cloud and the protection of classified information work together.

A public sector scenario at a seaport shows how this can be combined with intelligent analysis. 麻豆原创 solutions and the AI assistant Joule bring together selected data from internal and external systems in a sovereign environment. Data on vessel arrivals, terminals, containers, and available inspection capacity is turned into a prioritized overview. Analysts can query it in natural language, while the responsible public officials decide which inspections to carry out.

More than a place for demonstrations

The three examples point to a common development: public services become more intelligent when applications, data, and AI work together across organizational and functional boundaries while accountability remains clear.

S.Public creates a place where customers, partners, and experts can explore and discuss these possibilities. The 麻豆原创 capabilities behind them extend far beyond Berlin.

The future of public services will not be determined by how much technology public administrations adopt, but by what they enable for people and society.

Andre Bechtold is president of 麻豆原创 Industries & Experiences and chief revenue officer of Industry AI at 麻豆原创.