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.

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.

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�

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At 麻豆原创 TechEd in 2026, 麻豆原创 announced a series of innovations in Joule Work, Joule Studio, and 麻豆原创 Business AI Platform.

From building and extending AI agents to connecting business data and governing AI experiences, see how 麻豆原创 helps customers move towards the Autonomous Enterprise. Hear 麻豆原创 leaders and industry voices share what鈥檚 new, their perspectives, and more.

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.

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.

The release combines AI agents that can work directly with core business processes and adds new regulatory compliance capabilities and industry-specific innovations across asset-intensive industries, retail, and energy sectors. Together, these enhancements help strengthen the foundation for organizations that need to scale AI responsibly.

Building the foundation for enterprise AI

As organizations explore how AI can drive business outcomes, one challenge keeps surfacing: AI alone is not enough. To operate at enterprise scale, AI needs access to trusted data, process context, business rules, and governance.

This is why the conversation about AI increasingly becomes a conversation about ERP. AI agents require more than information. They need to understand what actions are permitted, which controls must be respected, and when decisions require human review. 麻豆原创 Cloud ERP Private can provide this foundation through integrated business processes, harmonized data, and enterprise-grade governance. FPS02 helps extend these capabilities, bringing intelligence directly into the processes where business happens.

AI embedded within business processes

FPS02 introduces AI agents and AI-assisted capabilities across key business domains, helping organizations automate routine work while maintaining oversight and accountability.

In finance, agents and AI-assisted capabilities help support processes spanning planning and forecasting, period-close activities, working capital management, tax processes, and compliance operations. Rather than requiring employees to manually identify issues and coordinate follow-up activities, 麻豆原创 Cloud ERP Private and analytical business insights can surface risks, recommend actions, and help manage exceptions.

In sales order-to-cash processes, agents enable sales teams to quickly identify fulfillment issues, receive resolutions, and create or update sales orders directly in their workflow, which helps minimize delays and improve customer satisfaction.

Joule and 麻豆原创 Document AI help streamline quality management by uploading, extracting, and matching certificate data, flagging exceptions, and creating pre-filled defects from natural-language inputs and source documents to help accelerate review, accuracy, and inspection release.

By receiving key observations, recommendations, and a clear action plan听directly within business processes, employees can focus on judgment and expertise rather than information gathering and exception management.

Compliance built into the core

FPS02 also delivers important regulatory capabilities directly within 麻豆原创 Cloud ERP Private.

The release includes support for IFRS 18 indirect-method cash flow statement reporting, helping organizations prepare for evolving financial reporting requirements. Additional updates include support for Germany’s AUSWIB1M foreign-trade reporting requirements and Canada First Nations Tax determination with GST499-1 return generation. By incorporating these capabilities into standard business processes, organizations can address compliance obligations while maintaining consistent governance and operational efficiency.

Industry innovation across critical operations

FPS02 deepens industry-specific capabilities across asset-intensive operations, upstream oil and gas, defense and security, and retail.

For asset-intensive industries, enhancements further integrate permit-to-work processes into maintenance operations, helping ensure operational controls are embedded directly within workflows. Upstream oil and gas organizations can gain new capabilities that extend Joule into production and revenue accounting, while 麻豆原创 Document AI helps interpret gas plant statements and propose accounting entries for review. Defense and security organizations can benefit from innovations supporting equipment allocation and ERP operations in limited-connectivity environments.

Retail organizations gain four new data products that help expose retail master data through 麻豆原创 Business Data Cloud, working to create a stronger foundation for future AI-driven processes. FPS02 also introduces operational enhancements including support for partial order fulfillment scenarios, GS1 2D barcode enablement, and expanded product traceability through serial-number integration between point-of-sale and inventory processes.

Advancing the Autonomous Enterprise

The Autonomous Enterprise is not built through a single AI breakthrough or software release. It emerges through the combination of trusted business data, integrated processes, governance, and intelligent capabilities working together across the enterprise.

麻豆原创 Cloud ERP Private 2025 FPS02 helps advance that foundation in concrete terms: by embedding intelligence within business processes, strengthening compliance capabilities, and expanding industry-specific innovation.

To explore the full FPS02 release and its implications for your business, visit .

Maura Hameroff is chief marketing officer for 麻豆原创 Cloud ERP Private and RISE with 麻豆原创.

With the 2H 2026 release and throughout the remainder of the year, we’re taking the next step toward Autonomous HCM, bringing together agentic AI, trusted workforce data, and HR applications to help deliver the outcomes that matter most to organizations. At its core are three pillars: automate the HR function to release capacity from administrative work, redesign how tasks, workflows, roles, and teams are structured across people and AI, and reinvent how organizations recruit, develop, move, and reward their people.

This vision comes to life through working together across 麻豆原创 applications to help carry out end-to-end HR processes and help people make better decisions, faster, all within a single workspace: . This is what it looks like when AI works across an organization鈥檚 data, applications, and processes: not more AI for the sake of AI, but AI that delivers impact you can measure.

Building on the 麻豆原创 investments organizations have already made, the innovations we鈥檙e delivering throughout the second half of 2026 help automate, redesign, and reinvent work to realize the full promise of Autonomous HCM.

These innovations span the full employee lifecycle:

Plan & hire

Finding, hiring, and onboarding the right talent gets easier and faster, with every step aligned to business needs.

• As AI drives up application volumes, recruiters risk spending more time clearing a queue than assessing if a hire works for the business. can address this with repeatable, configurable screening that helps narrow large applicant pools to a focused shortlist. builds on that, with results now fully reportable in Report Builder, giving recruiters visibility into the reasoning behind AI-assisted decisions. Together, they help free up the time and attention to do what the business actually needs鈥攁pply their judgement to identify and engage the candidates most likely to succeed.

• helps put agentic capability directly inside the recruiter workflow so hiring never loses momentum, with monitoring pipelines and booking interviews through agentic scheduling, Winston Interview delivering a conversational AI-led screening experience, and the Interview Summary Agent automatically capturing and posting a structured summary to the candidate profile.

• Helping new hires get up to speed and start contributing faster, the equips HR teams to deliver personalized, timely onboarding at scale, with intelligent prioritization, simplified onboarding task creation, and automated custom form conversion.

Manage & pay

These innovations help organizations work more efficiently and stay compliant while giving employees fast, seamless support to reduce the routine inquiry burden on HR teams.

• The helps build trust in payroll so employees can have confidence in every paycheck. Administrators can quickly identify anomalies, resolve issues, and stay ahead of compliance, while employees can get direct access to clear, confident answers about their pay without raising a support ticket.

• The can reduce manual effort, prevent bottlenecks, and keep HR processes moving, proactively resolving workforce and payroll data issues, automating employee and job changes, and streamlining position management. It can also detect approval bottlenecks, nudge approvers, and recommend next steps.

• The can give HR administrators a smarter way to manage day-to-day system operations鈥攆rom audit readiness and compliance tracking to time and benefits configurations鈥攔educing routine maintenance and helping HR teams drive continuous improvement and innovation.

Develop & grow

These innovations connect skill-building, learning, and career development into a more intentional experience designed for continuous growth.

• The helps move organizations to proactive workforce readiness, connecting compliance monitoring, upskilling, and development in one experience. It can guide the right learning actions at the right time for employees, managers, and admins, from flagging compliance deadlines to identifying skill gaps and recommending targeted learning.

• People perform at their best when they know their employer is invested in their development. The helps employees close skill gaps, build personalized development plans, and set meaningful goals. For leaders, conversational talent discovery through Joule helps surface internal candidates, successors, and experts with a clear explanation of what shaped each result, helping organizations match the right talent to opportunities and grow people from within.

The next step toward Autonomous HCM

These are not standalone capabilities. Together, they are Autonomous HCM in action: AI working with trusted data and HR processes to remove friction, surface insight, and give people the space to focus on outcomes that matter. The organizations building that way today are the ones that will lead tomorrow.

Discover what鈥檚 new in the 麻豆原创 SuccessFactors 2H 2026 release with the or .

Rebecca Carr is president of HCM Products for 麻豆原创 SuccessFactors and CEO for SmartRecruiters.

With more than 1,500 events across the city running alongside the UN General Assembly, the event was hailed as the .

Delegates arrived feeling three pressures that had been building all year: an energy price shock, a summer of climate disasters, and a fast-growing argument about the resources artificial intelligence consumes. What came out of the week was a clearer picture of where sustainability now sits in business: inside the numbers that decide whether companies grow, stall, or get stopped.

Sustainability is back, on different terms

For a few years, sustainability seemed to be slipping down the corporate agenda. Last week suggested , but not in the form it left.

Energy is the clearest example. UN Climate Chief Simon Stiell put the cost of energy shortages linked to the Iran war at more than for American consumers alone. The UK’s Ed Miliband urged governments to treat climate and nature loss . Clean power is increasingly an argument for independence and competitiveness.

Physical risk is the second. Iceland’s Prime Minister Kristr煤n Frostad贸ttir described laid well before the storm arrives. By Thursday, the General Assembly had approved its first declaration , aimed at replacing fragmented initiatives with sustained, coordinated, and properly funded action. The UN has acknowledged that overshooting 1.5掳C is . For business, the stakes are large: estimates corporate business interruption losses from climate hazards at more than $1.1 trillion a year, most of which never appears as a line on a balance sheet.

But knowing a risk exists is not the same as being able to act on it. A flood forecast only becomes a business decision when someone can put a value on what sits in its path: the asset, the revenue that depends on it, and the cost of protecting it.

That is the gap most companies face. Everyone is reporting climate risk, yet far fewer are deciding with it. That is why came together this week, connecting Jupiter’s physical climate risk intelligence with 麻豆原创’s enterprise data and reporting foundation. The starting point is disclosure that holds up to scrutiny. The direction of travel is climate risk that informs where companies invest, protect, and build.

The real question about AI is permission

If one subject dominated the week, it was AI. Views were sharply divided: Stiell warned that AI leaders are when it comes to their license to operate, while speakers from all across business and academia argued that AI is too important to give up and will ultimately help tackle climate change.

Both sides are responding to something real. But the issue that will decide AI’s path is not capability, which is already abundant, but whether the people who host AI, power it, and work alongside it keep agreeing to it. Every model runs on land, electricity, water, and cooling. Every data center needs a community willing to have it, and every rollout needs a workforce willing to adopt it. Groups weighing a new data center don’t separate the water question from the jobs question, and companies can’t afford to either.

Governments are starting to set expectations. T眉rkiye’s COP31 presidency launched the Antalya , with the COP31 President-Designate saying companies are expected to be transparent about their energy use and to power their operations with clean energy. is due before the summit, which will have a dedicated day on technology and AI.

That pressure applies to every company’s climate commitments, 麻豆原创’s included. We have run our own offices and data centers on 100% renewable electricity since 2014, we consider environmental impact at the design stage of our AI architecture, and we have started sharing the footprint of our cloud services with customers, with a beta view for selected products now in 麻豆原创 for Me. While none of that makes the challenge go away, it makes it visible, and measuring a footprint is where accountability starts.

The week also showed the other side of the ledger. Working from connected data, AI can weigh cost, carbon, availability, and regulation in a single decision, which no planning team can do by hand at scale. Instead of checking whether every product is compliant, companies can ask which products are at risk, why, and what design or supplier change would fix it.

“The question for sustainability leaders is no longer how to manage AI’s impact. It’s how to shape where AI investment goes,” said Sophia Mendelsohn, chief sustainability and commercial officer at 麻豆原创. “AI can weigh cost, carbon, and risk together, but only if sustainability is in the data it works from. Leave it out, and AI will optimize it away.” 

Governance is the other half of the answer. Speed won the first phase of AI adoption. The next phase will go to organizations that can show how an automated decision was reached and who is accountable for it. In 2025, 麻豆原创 reviewed against its principles and escalated 44% as high-risk for senior review. Where no ethically acceptable trade-off could be found, the project did not go ahead in its original form.

Implementation runs on systems

The organizers asked delegates to move from ambition to implementation. The main obstacle to that remains disconnected data. When sustainability information sits apart from procurement, manufacturing, and finance, even good AI produces confident wrong answers: recommending a lower-carbon supplier, for instance, that is not approved for the market in question. The organizations making progress treat sustainability data with the same discipline as financial data, with the same quality standards, audit trail, and controls.

Industrial manufacturer HARTING, for example, connected its sustainability data to the systems it already runs, from manual work to AI-assisted mapping across its material portfolio.

Nowhere does that discipline matter more than in the supply chain.

“Every climate target eventually becomes a supply chain decision: what you source, where you make it, how you move it,” Mendelsohn said. “That’s where commitments are won or lost, and that’s where the data has to be.”

The road to Antalya

New York Climate Week was the last major business gathering before COP31 in Antalya in November, and the COP31 presidency used the week to launch a global pledge to raise electricity’s share of final energy consumption to , and a on how AI is powered and measured.

Pledges set the direction, but the pace is set by business decisions. Away from the main stages, the conversations with customers last week sounded different from a few years ago. The questions focused on what carbon costs, where supply chains are exposed, and whether the numbers will hold up to an auditor. Those are commercial questions, and they have commercial answers. The tools, the economics, and the business case are largely in place. What remains is connecting them inside the processes where decisions are actually made.

That is truest of all for AI, the technology that dominated last week. It will scale only as far as the energy, data, and trust beneath it allow. In other words, AI’s future runs through sustainability, and the companies that act on that now will set the pace for everyone else.

To learn more about how 麻豆原创 can support, visit .

Monica Molesag is head of Sustainability Communications at 麻豆原创.

In early July 2026, partner Bluestonex invited customers to an immersive hands-on workshop day at the Bluestonex AppHaus Oswestry, Shropshire, UK. The focus was placed on two industries: retail and supply chain. During the event, attendees watched inspiring demos of new 麻豆原创 AI capabilities powered by the and took part in interactive sessions to experience industry innovation and learn along the pragmatic, human-centered innovation approach.

Different showcases for retail and supply chain

Two different showcases shed light on different applications and scenarios from the retail and supply chain industries.

The first showed how data can be surfaced and acted on meaningfully via the use of AI and smart application functions including 麻豆原创 Business Data Cloud (麻豆原创 BDC) and Bluestonex鈥檚 flagship master data management and governance solution, Maextro. The second showed different supply chain and warehouse use cases from the retail and manufacturing industry, where the participants could experience the demo with physical mobile devices and interactive stock movements as if they were in the warehouse setting itself.

Two work streams to explore AI-infused innovation

After learning about the art of the possible brought about by the latest 麻豆原创 Business AI solutions of the Autonomous Enterprise, the workshop attendees split into two work streams to explore different aspects of AI-infused innovation:

1. The first group went through a process of rapid ideation and prototype development with a fast-paced innovation approach. Participants got hands-on, creating both low-fidelity and high-fidelity designs. To do so, they were shown how different AI tools, such as , are changing the way applications are built.
2. To explore the skills needed for successful AI adoption and to address the culture shift, the second session used LEGO庐 Serious Play庐 and explored the human side of AI transformation. The method is used for skill-building exercises to then tackle a central challenge: building a model that represents how AI could fundamentally change an existing business. The participants learned a set of guiding principles for AI that they can apply in their day-to-day business.

麻豆原创 AppHaus Network partners acting as front-runners

The workshop is a good example of how 麻豆原创 partners such as Bluestonex act as front-runners when it comes to exploring the and adopting AI in a meaningful way. During the workshop, experts helped customer teams identify appropriate use cases, design a concept for an AI agent, and create a real business solution that can be built with the latest 麻豆原创 technology, encouraging and enabling them to conquer this new environment with all its opportunities. The guiding workshop principle was to create value in their respective company context.

The feedback given by some of the participants seems to acknowledge that:

• “Wow. Mind blown! Great team showing the art of the possible. Excellent.”
• 鈥淩eally great day overall. I always love the real-world scenarios. The hands-on sessions were a highlight, and it’s a great visual display of what’s possible.”
• “Great way to generate ideas, meeting other people, engaging format. The real-world scenarios stood out.”
• “The space itself鈥攖he Bluestonex church鈥攚as a highlight. Love the 麻豆原创 Experience Center content on the big, beautiful screen.”

Looking back from two perspectives

Richard Henry, head of Growth, 麻豆原创 BTP, Data, & Innovation at Bluestonex, looks back: 鈥淭he addition of the latest demo content powered by the 麻豆原创 Experience Center to Bluestonex鈥檚 industry workshop model marks another step in our mission to help customers realize the Autonomous Enterprise. By combining the latest innovation stories, industry experiences, and 麻豆原创 Business AI with proven 麻豆原创 AppHaus methodologies, we move beyond inspiration to action. Through hands-on workshops, design thinking, and immersive experiences, organizations can explore, validate, and prioritize real business opportunities. In the end, our focus remains the same: we want to turn innovation with the latest 麻豆原创 solutions into practical outcomes and adoption, all for the benefit of our customers.鈥�

Tobias Gollwitzer, senior director and regional lead EMEA, 麻豆原创 AppHaus Walldorf, concludes: 鈥淭his workshop is just another proof of the exceptional agility, the dynamic approach, and attitude of our 麻豆原创 AppHaus Network partner in the UK. The team invites and motivates 麻豆原创 customers to be among the first to conquer new 麻豆原创 industry-specific technologies, grasp and move towards 麻豆原创鈥檚 Autonomous Enterprise solutions to their best advantage.鈥�

Imke Vierjahn is communications lead for 麻豆原创 AppHaus Network.

麻豆原创 has updated the , reflecting the fast-changing AI landscape and lessons drawn from deep-dive interviews with more than 50 international experts. The revision reflects concrete changes made to strengthen the ethical safeguards, accountability, and safety measures within enterprise AI adoption, addressing the substance of business leaders’ concerns, not just the perception of them.

Our approach to AI rests on three words: relevant, reliable, and responsible. For 麻豆原创, ethical deployment isn’t a regulatory checkbox or a marketing layer; it’s the foundation that makes the Autonomous Enterprise possible, where intelligent agents coordinate and execute complex workflows while humans remain firmly in control.

Three disciplines for responsible AI

Ethics sit at the very center of 麻豆原创鈥檚 broader social sustainability agenda, where responsible AI depends on three disciplines working together, not in isolation:

1. Ethics define our values. We set clear moral boundaries for how technology should serve humanity.
2. Security protects our systems and data. We safeguard core enterprise assets against emerging cyber threats and vulnerabilities.
3. Compliance ensures legal alignment. We are proactively preparing for global regulatory standards like the EU AI Act.

Together, they help teams assess whether an AI use case should proceed, what safeguards it requires, and who is accountable for its performance after deployment, ensuring that as business processes become automated, human dignity, fairness, and agency remain intact.

Ethics underpin the Autonomous Enterprise

As AI shifts from assisting people to acting on their behalf, the governance question becomes more urgent. The Autonomous Enterprise represents a repositioning where AI agents no longer just make recommendations, but execute business processes end-to-end, across finance, supply chain, procurement, HR, and customer experience. As CEO Christian Klein put it, “For the mission-critical processes of our customers, ‘almost right’ just isn’t good enough.”

That’s precisely why ethics and governance can’t be an afterthought bolted onto agentic AI. They have to be the foundation it runs on. Every action an 麻豆原创 agent takes is logged and traceable, so organizations can always know what an agent did, why it did it, and what data it used. Autonomy without that foundation is just an experiment; autonomy built on it is an operating model organizations can trust at scale.

Translating principles into practice

The 麻豆原创 Global AI Ethics policy, grounded in UNESCO’s Recommendation on the Ethics of Artificial Intelligence, governs the development, deployment, use, and sale of every AI system across the company. 麻豆原创’s updated AI Ethics Handbook translates these principles into day-to-day practice. It helps give engineering and product teams a one-stop reference for applying the policy’s guiding principles, from fairness and non-discrimination to human oversight and sustainability.

To bridge the gap between high-level principles and day-to-day engineering, we embed strict operational checkpoints into our product life cycle:

• Mandatory AI ethics impact assessments: Every 麻豆原创 AI use case must undergo a rigorous evaluation before reaching deployment, based on 10 AI ethics principles. Use cases receive a risk score and the 麻豆原创’s AI Ethics Office helps product teams enforce mitigation measures and safeguards in place. Depending on the risks involved, some use cases go through several review steps and may be escalated to the 麻豆原创 Global AI Ethics steering committee for a final decision.
• Human oversight by design: .
• Internal accountability: Compliance is non-negotiable across 麻豆原创. All employees acknowledge the policy, and the rules apply universally, including to internal, self-developed AI applications built using tools like Hyperspace AI or Claude Code.

The process provides clear guardrails that are visible to 麻豆原创鈥檚 stakeholders, including customers and employees.

Leading the industry in AI governance

The demand for transparent governance is urgent, and independent evaluations highlight significant gaps in corporate readiness worldwide. In benchmark assessing 2,972 companies, 麻豆原创 was among the only 12.4% found to maintain an explicit policy ensuring human oversight in AI systems.

麻豆原创鈥檚 leadership in ethical AI has earned external recognition: the company was named a leading positive example in the . 麻豆原创 is also an active participant in the World Economic Forum鈥檚 , convened by the Global Alliance for Social Entrepreneurship in partnership with the Schwab Foundation. Working alongside leading technology companies and social innovators, 麻豆原创 is helping shape responsible AI deployment to accelerate social impact and extend the benefits of enterprise AI to organizations tackling the world鈥檚 most complex challenges.

The broader landscape shows how much ground the industry still has to cover. Data from the shows that only 36% of responding companies have a dedicated or integrated AI policy. paints a similar picture: just 27% of global businesses currently factor their AI footprint into their broader sustainability strategies, a notable gap given the surging energy demands of AI data centers.

Unlocking sustainable growth

Ethical AI goes beyond risk mitigation and regulatory readiness鈥攊t drives tangible economic value. By ensuring fairness, transparency, and robust data privacy, organizations protect their brand reputation while empowering their workforce to perform at their highest level.

When AI coordinates, executes, and optimizes routine business processes within an ethical context, employees are freed from manual tasks to focus on strategy, creativity, and human connection. This synergy between human potential and artificial intelligence forms the backbone of sustainable enterprise growth.

Organizations that understand this will reap the rewards as regulations tighten and societal expectations increase. However, ethical AI only becomes a reality when we work together. We look forward to continuing to work alongside our customers and partners as we embrace one of the most important challenges and competitive advantages in the AI era.

Read the . Find out more about the .

Matthias Medert is head of Sustainability at 麻豆原创.

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EssilorLuxottica shares how 麻豆原创 supports its global business processes and enables continuous innovation.

From iconic brands to advanced eye care solutions, discover how cloud technology helps the company operate at scale and enhance customer and professional experiences worldwide.

Read to learn more.

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

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

Shining stars: the standout achievements

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

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

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

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

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

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

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

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

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

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

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

The voice of the customer

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

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

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

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

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

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

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

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

With a global retail network that includes Sunglass Hut, LensCrafters, Vision Express, and Apollo, the company operates at the intersection of medical technology, consumer fashion, and retail.

From eyewear to smart wearables

In recent years, EssilorLuxottica has extended its brand into wearable technology, blending eyewear with digital capabilities. Smart glasses equipped with cameras, microphones, speakers, and integrated display technologies represent a new frontier where utility, lifestyle, and connectivity converge.

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This evolution is not just about hardware innovation. It reflects a broader ambition: transforming eyewear into an intelligent interface between users and digital services. The company鈥檚 collaboration with 麻豆原创 brings that vision into sharper focus.

Powered by AI, connected to the backbone

EssilorLuxottica runs its retail network of 15,000 stores on 麻豆原创 S/4HANA Retail and is co-innovating with 麻豆原创 to pilot AI-driven use cases based on Joule. The idea is to help any retailer bring 麻豆原创鈥檚 AI capabilities directly into smart glasses, creating a hands-free interface between employees and enterprise systems. Using voice interaction, store associates can access inventory and availability data in real time, receive contextual customer insights, and identify opportunities for more personalized service, such as cross-sell and upsell recommendations. In this way, a spoken request can become an immediate action in the backend system.

Empowering humans

EssilorLuxottica is initially exploring two practical applications for the technology: supporting technicians in the field and empowering associates on the retail floor. For field operations, wearable AI can provide real-time guidance and documentation support, helping technicians navigate complex tasks. In stores, smart glasses could give associates immediate access to customer profiles, order history, inventory data, and ordering capabilities鈥攁ll through natural voice interaction.

The potential was demonstrated live at this year鈥檚 麻豆原创 Sapphire Madrid, where an AI-enabled pair of glasses helped a store associate identify a returning customer, retrieve their purchase history, reorder products, and suggest relevant add-ons based on customer and purchase context, all within a seamless conversation.

Opportunities ahead: from pilot to platform

The use cases, currently in a test phase, were developed in just two weeks. They signal broader transformation potential, with the teams targeting commercialization of both pilots by the end of the year. EssilorLuxottica operates a complex global supply network that encompasses hundreds of sites and distribution centers, which presents significant opportunity for scaling AI-enabled workflows across the enterprise.

The implications extend beyond retail: the EssilorLuxottica鈥撀槎乖� collaboration illustrates how established industries can use AI and integrated business processes to create new ways of working and serving customers.

By transforming eyewear into an intelligent interface connected to enterprise systems, the company is redefining both the product and the experience around it.

Top photo courtesy of EssilorLuxottica.

As AI reshapes the way organizations attract, develop, and support talent, leaders need more than systems of record. They need intelligent solutions that help them understand what’s happening across their workforce, anticipate what’s next, and take action with confidence.

We believe this recognition reflects 麻豆原创’s continued investment in AI-powered innovation across 麻豆原创 SuccessFactors solutions and our commitment to helping organizations build a more connected, intelligent, and proactive approach to workforce management.

鈥淎I is rapidly expanding what HCM platforms can do, but buyers need providers that combine innovation with real operational depth and consistent execution,鈥� said David Wilson, CEO and founder of Fosway Group. “麻豆原创 SuccessFactors has accelerated its rollout of AI features across HCM and expanded its integration with AI agents, offering more options to HR leaders. It is a Strategic Leader in the 2026 Fosway 9-Grid™ for Cloud HR, and 麻豆原创鈥檚 Excelling Trajectory in 2026 reflects its increased market performance and customer advocacy as well as continued innovation in both AI and core capabilities.”

Building the foundation for Autonomous HCM

This recognition comes at a pivotal moment for HR.

As AI continues to reshape work, organizations are moving beyond questions of efficiency and automation and focusing on a broader challenge: how to build more agile, adaptable, and resilient workforces.

The next era of HCM will be defined not by automating processes, but also by helping organizations make better decisions. That’s why we introduced our vision for Autonomous HCM earlier this year at 麻豆原创 Sapphire.

As part of 麻豆原创’s broader vision for the Autonomous Enterprise, Autonomous HCM brings together trusted workforce and business data, embedded AI, and HR processes to help organizations connect workforce decisions more closely to business outcomes.

As skills requirements evolve and business priorities shift, leaders need more than workforce data. They need the ability to understand implications, evaluate options, and act quickly.

At its core, Autonomous HCM is about closing the gap between knowing and doing. By bringing together workforce data, business context, and AI, organizations can move from understanding workforce challenges to taking action on them.

Traditionally, organizations have relied on reports, dashboards, and manual processes to identify workforce issues and determine next steps. While these tools provide valuable insights, they often leave leaders to interpret information and coordinate actions across disconnected systems and processes.

Autonomous HCM envisions a different approach. By combining AI, workforce intelligence, and business context, organizations can surface recommendations, automate routine work, and help employees, managers, and HR teams take action directly within the flow of work.

Whether identifying emerging skills gaps, recommending learning opportunities, supporting hiring decisions, streamlining employee support, or helping managers respond to workforce trends, AI can help organizations move more quickly from insight to action and achieve better outcomes.

Turning AI into action

Over the past year, 麻豆原创 has continued to invest in innovations that help organizations connect workforce insights with better decisions and outcomes, including:

• , helping employees, managers, and HR teams get work done more efficiently
• , bringing workforce and business data together to support better decision-making
• , helping streamline HR service delivery and employee support
• The acquisition of SmartRecruiters, extending our connected HCM approach to talent acquisition by linking hiring decisions more closely to workforce planning, skills intelligence, and the broader employee lifecycle

Next month at , we’ll share new innovations and customer stories that demonstrate how organizations are using our solutions to adapt more quickly to change, create more agile workforces, and move toward the vision of Autonomous HCM.

Delivering meaningful outcomes

The journey toward Autonomous HCM is already underway for many organizations.

is using 麻豆原创 SuccessFactors solutions and Joule to transform payroll and employee support experiences. By implementing AI capabilities, such as Explain Pay, the company has reduced payroll processing time by 90%, from five days to four hours, and expects to reduce payroll support costs by 80% while delivering faster, more personalized assistance to employees.

is embracing AI as part of its broader workforce transformation strategy. Through 麻豆原创 SuccessFactors solutions and Joule, the company is using AI to support talent acquisition, skills development, and employee growth while creating a more personalized and accessible employee experience. The company is also advancing its journey toward becoming a skills-based organization, using AI to help connect people with learning and development opportunities.

These examples highlight how organizations are using AI not just to automate work, but to improve decision-making, enhance employee experiences, and create measurable business value. They also demonstrate how the journey toward Autonomous HCM is already taking shape across organizations today.

Looking ahead

AI is creating new opportunities for organizations to rethink how work gets done and how people can thrive.

Our focus remains on helping organizations connect workforce insight with action through trusted data, embedded AI, and end-to-end HR processes. By bringing together workforce and business data in a unified foundation, 麻豆原创 SuccessFactors solutions help organizations build toward Autonomous HCM and create better outcomes for employees and the business.

We are grateful to our customers whose trust, partnership, and innovation make recognitions like this possible.

听and how 麻豆原创 SuccessFactors solutions are helping organizations build the foundation for Autonomous HCM and the future of work.听

About the Fosway 9-Grid™
Fosway Group is Europe鈥檚 #1 HR industry analyst. The Fosway 9-Grid™ is a five-dimensional market analysis model that helps organisations compare solutions based on Performance, Potential, Market Presence, Total Cost of Ownership and Trajectory.
The Fosway 9-Grid™ for Cloud HR provides a unique assessment of the principal Cloud HR options available to organizations in EMEA. The analysis is based on extensive independent research and insights from Fosway鈥檚 Corporate Research Network of over 250 organizations, including BP, HSBC, PwC, Sanofi, Shell, and Vodafone.
Visit the Fosway website at .

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

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

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

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

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

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

Takeaway 2: digital transformation has moved from funding to execution

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

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

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

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

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

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

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

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

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

Takeaway 4: strategic influence is becoming more operational

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

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

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

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

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

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

Takeaway 5: the skills equation has changed

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

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

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

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

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

The next chapter is about proof

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

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

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

Payroll presents a unique paradox. It鈥檚 one of the business functions most suited to AI and automation because it鈥檚 highly structured, data-intensive, and compliance-driven. At the same time, it鈥檚 one of the most trust-sensitive functions in the enterprise because even small errors can have immediate consequences for employees’ financial well-being and confidence in their employer.

Payroll professionals often joke that nobody notices payroll when it goes right, and everyone notices when it goes wrong. Behind that observation is an important reality: every payroll run becomes a moment of trust between employer and employee. That trust has measurable consequences. According to new research* by 麻豆原创鈥檚 , 38% of employees worldwide have experienced a payroll error. These employees are significantly less likely to trust both their organization (13% lower trust) and their payroll function (17% lower trust). Trust declines even further with each additional payroll error experienced.

As National Payroll Week takes place across both the and the , we have an opportunity to recognize the payroll professionals who earn and maintain that trust every day, while reflecting on the transformation taking place across the payroll function itself.

As payroll teams navigate growing complexity, they’re being asked to do more with less while maintaining the accuracy, compliance, and reliability employees depend on. Employees increasingly expect faster, more flexible payroll experiences, including more frequent pay cycles and greater visibility into earnings and deductions, while tax laws, wage rules, leave requirements, and reporting obligations continue to evolve across countries, states, and local jurisdictions. At the same time, today’s workforce is more dynamic than ever, with employees changing roles, locations, schedules, and compensation arrangements in ways that can create downstream payroll impacts. Managing these changes manually is becoming increasingly difficult and raising the risk of errors, delays, and compliance gaps, positioning payroll not simply as an administrative process, but as strategic infrastructure supporting employee experience, compliance, finance, and business operations.

This combination of rising complexity and rising expectations is driving the next evolution of payroll: autonomous payroll.

Scaling trust in an era of complexity

Autonomous payroll is designed to help organizations navigate growing payroll complexity while maintaining accuracy, compliance, and confidence at scale. By combining AI, intelligent automation, integrated data, and real-time payroll monitoring, organizations can move beyond reactive payroll operations and proactively identify issues before they affect employees.

The goal isn’t to remove people from payroll, but to enable them to focus on higher-value activities by reducing manual effort and increasing visibility. Our global research suggests that employees do not see AI and human involvement as mutually exclusive. In fact, nearly half (49%) say they would trust AI in payroll more if they still had access to a human when AI fails. Additionally, 43% of employees say that the ability to request a human review of AI-generated outcomes would increase their trust in the use of AI in payroll. Trust is built not only through accuracy and efficiency, but also through the confidence that employees can escalate concerns, seek clarification, and access human support when needed.

Emerging capabilities such as AI-powered payroll agents and continuous payroll are helping organizations move from processing payroll to actively managing it. These capabilities can assess payroll readiness, detect anomalies, validate changes, and monitor workforce events across HR, payroll, and time data. By identifying how changes in roles, locations, schedules, compensation, or leave may affect payroll outcomes, organizations can anticipate impacts and resolve exceptions before they become costly errors. The result is improved reliability, stronger compliance, and greater trust across the workforce.

The shift is significant. Rather than finding errors at the end of a payroll cycle, organizations can increasingly investigate and resolve them throughout the cycle. But technology alone isn’t enough. As payroll becomes more autonomous, organizations must ensure intelligent systems operate with appropriate oversight, transparency, and accountability.

Automation requires accountability

As AI becomes more embedded in payroll operations, the conversation shifts from adoption to accountability. Trust becomes paramount, raising important questions about where AI can operate autonomously, where human review adds value, and where human accountability must remain visible.

Our global research shows that employee comfort with AI varies by payroll task. Employees are most comfortable with AI supporting calculation-heavy activities such as payroll calculations and anomaly detection, while they prefer human involvement for tasks requiring judgment, investigation, or employee interaction. Final decisions on payroll disputes remain the area where employees most strongly prefer a human. These findings reinforce an important point: the goal is not to replace human expertise, but to apply AI where it adds the most value while preserving human oversight where trust and judgment matter most.

The most successful applications of AI in payroll won’t simply automate tasks. They’ll strengthen trust by improving consistency, reliability, transparency, and the overall payroll experience employees depend on.

In other words, the future of payroll isn’t human or AI. It’s human expertise amplified by AI.

Building the foundation for Autonomous HCM

The implications of autonomous payroll extend well beyond payroll operations.

At 麻豆原创, we believe autonomous payroll is a key enabler of Autonomous HCM. Trusted AI experiences depend on trusted data, connected processes, and embedded intelligence, and payroll plays a critical role in making that vision possible.

Every hiring decision, compensation adjustment, promotion, workforce change, or organizational initiative ultimately affects payroll. As a result, payroll remains one of the most trusted and comprehensive sources of workforce data across the enterprise.

Realizing the full potential of autonomous payroll requires a unified foundation across HR, payroll, and time data. When organizations establish a single source of truth, they can reduce complexity, improve payroll accuracy, strengthen compliance, and enable AI to deliver more meaningful insights and recommendations.

This foundation is essential because AI cannot solve payroll challenges if it鈥檚 simply layered on top of fragmented systems and disconnected processes. Instead, organizations need connected data and integrated workflows that allow intelligence to operate across the entire workforce lifecycle.

When payroll, HR, and time data come together on that foundation, organizations can create more intelligent workforce experiences, make better decisions, and increase organizational agility.

Looking ahead

National Payroll Week is an opportunity to celebrate the professionals who keep one of the most important business functions running every day. It’s also an opportunity to recognize how dramatically that function is evolving.

For decades, payroll was viewed primarily as an administrative necessity. Today, it is increasingly recognized as a strategic capability that influences employee experience, compliance, operational resilience, and organizational trust.

The organizations that lead in the years ahead won’t simply process payroll more efficiently. They’ll create payroll operations that are more intelligent, more connected, and better positioned to support both employees and the business.

As payroll continues its evolution from a transactional function to a strategic business capability, the opportunity is not simply to automate existing processes. It’s to build payroll operations that can scale trust, resilience, and confidence in an increasingly complex world.

Because when payroll works, it does more than deliver pay. It helps build confidence in the organization behind it.

Discover how helps organizations simplify payroll, reduce complexity, and accelerate their journey to autonomous payroll.

*Data from a global survey of 1,576 full-time employees in July 2026.

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

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

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

You can鈥檛 manage what you can鈥檛 see

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

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

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

Someone has to own it

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

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

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

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

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

Cost per token is the wrong scorecard

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

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

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

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

Guardrails should target waste, not adoption

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

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

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

From cost control to value governance

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

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

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

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

has long used frustration-free, quiz-style games to create these connection moments for people living with Alzheimer鈥檚 and dementia. Now, with help from 麻豆原创 and , the organization is exploring how AI can personalize its games at scale.

鈥淲hat we saw, as AI was coming in, was that we could take two or three more steps to really personalize the experience and trigger positive memories for each individual,鈥� Bruce Elliott, CEO and cofounder of Memory Lane Games, says. 鈥淏ut, as a six-person startup on the Isle of Man, a little island in the middle of the Irish Sea, it was a daunting task. We had a brilliant vision, but to bring it together we needed support.鈥�

Collaboration for good

Through the MovingWorlds鈥� platform, Memory Lane Games collaborated with 麻豆原创 and EY to move beyond generic reminiscence content, creating an AI prototype that is designed to help transform personal details, family photos, and life experiences into memory games.

This project was part of , a MovingWorlds cooperation between 麻豆原创, EY, and Microsoft that provides social enterprises鈥攖hat often have limited resources, complex technical needs, and a lack of access to partners鈥攚ith strategic coaching, pro-bono consulting, expert-led workshops, and access to a global partner ecosystem at no cost.

For employees at companies like 麻豆原创 and EY, these engagements are equally transformative. Real-world problem-solving with social enterprises is among the most effective forms of experiential learning, helping to develop skills that benefit both the individual and the organization.

Through this program, Memory Lane Games gained access to the technical support needed to explore how generative AI could deepen personalization in its games. To start, the project team鈥擶ade Tsai, global client technology architecture leader at EY, and 麻豆原创 developers Robin Baeurle and Michael Zadikowitsch鈥攍ooked at what the organization had done in the past and how it could be transformed by generative AI. The intersection of personalization and images was where the team landed.

Turning memories into personalized games

The foundation of the project team鈥檚 prototype is a persona profile that the AI can reference. A caregiver or loved one creates a profile for the person living with dementia with basic information like age bracket, gender, first language, places they鈥檝e lived, what they did for work, and cultural background as well as other details like past vacations, favorite foods, hobbies, pets, and more. The goal is to capture the culture of the individual, not personal data. Using that persona profile, the AI suggests topics and then generates games by pulling in open-source images and writing multiple-choice questions.

For example, a demo of the Memory Lane Games prototype showed the creation of a persona profile of a 75-to-80-year-old woman who lived in Savannah, Georgia, was an elementary school teacher for 35 years, vacationed on Hilton Head Island and in the Blue Ridge Mountains, had a tabby cat named Magnolia, and enjoys Motown and soul music, gardening, birdwatching, and cooking Southern classics like pecan pie and shrimp and grits. The AI prototype generated several games for her: Savannah鈥檚 Historic Squares, Blue Ridge Mountain Getaways, Savannah鈥檚 Southern Kitchen Favorites, and Magnolia the Cat and Backyard Birds.

鈥淎t the start, we didn鈥檛 know how to solve this problem of extracting metadata from images, or even how to collect images, where to find them, and how to create these games,鈥� Zadikowitsch says. 鈥淎nd then we started experimenting and trying things out鈥攇enerating questions and then finding images, which didn鈥檛 work well, then finding images and then generating questions, which worked better.鈥�

The prototype is currently in the testing stages. Once it is deployed, there is the possibility to expand game personalization with photos submitted by a caregiver or loved one.

Tsai explains that current AI models can identify objects in images, but directing them to extract metadata and EXIF data, which contains the exact date, timestamp, and GPS coordinates indicating when and where the photo was taken, can take it a step further.

The layers of information stored within a photo file can be a treasure trove for Memory Lane Games鈥� AI prototype. 鈥淲e can use that information as a hint to extrapolate what else we can pull from the surroundings that could help reshape that memory experience,鈥� Tsai says. 鈥淚t鈥檚 going beyond where that photo is taken.鈥�

AI with tangible human impact, not just productivity gains

While the AI prototype helps Memory Lane Games create personalized games more efficiently, its greater promise is human: helping people living with dementia and loved ones connect through joyful memories.

鈥淥ften in the context of AI, the typical audience is people working at other companies, not, for example, people with dementia,鈥� Baeurle says. 鈥淭here鈥檚 a lot of potential that goes to waste in not realizing that these audiences can also benefit from digital solutions and, specifically, AI-powered solutions.鈥�

For the project team, that human outcome was the guiding principle. 鈥淲hatever we build, it should spark joy,鈥� Baeurle adds.

鈥淐aregivers are really busy. Family members are really busy. Care staff in care homes are very busy. If we can take one photo from a family and a couple lines of text and create six to 10 questions that can really pull out all of those memories, trigger a positive memory, and start those wonderful stories鈥攖hat鈥檚 what [this collaboration] has been able to help us deliver,鈥� Elliott says.

AI makes it possible to create memory games not only about a city, but about a neighborhood, a local landmark, or another detail closely tied to someone鈥檚 life. The more personal the prompt, the more likely it is to encourage conversation and connection.

鈥淲e know that social isolation is one of the top modifiable risks for dementia in older age, so the more we can get people talking and those neurons firing, just by letting them talk about something they want to talk about, is powerful,鈥� Elliott says.

The future of AI-enabled memory care

What this collaboration between Memory Lane Games, 麻豆原创, EY, and MovingWorlds showed is that AI has a place in memory care. 鈥淭he team demonstrated that AI can help create engaging [memory] games. I think this was the goal,鈥� Zadikowitsch says.

Tsai attended the United Nation鈥檚 International Telecommunication Union (ITU) this summer, after winning the 鈥淎I for Good for Entrepreneurship鈥� category in EY鈥檚 inaugural AI for Impact Challenge with this Memory Lane Games project.

The project team is confident that this is just the beginning for AI-enhanced memory care and has already come up with more use cases: an AI companion optimized for dementia that runs on speech-based interfaces and emotional and voice intelligence, adding more languages since dementia patients often revert to their first language, and using generative AI to create images of places for which no photos exist.

鈥淲e鈥檝e always taken a very simple yet scalable approach. And this [project] has taken both of those: kept it simple for the user but made it infinitely scalable. And I think that鈥檚 where the real magic of AI is,鈥� Elliott says. 鈥淎I is the key to us scaling, and we needed this collective help, this collaboration, in order to test those interesting hypotheses. And we鈥檝e seen positive early results.鈥�

For Memory Lane Games, that magic is not technology for technology鈥檚 sake. It is the possibility of helping more people reconnect with the stories, places, and people that make life feel familiar and spark joy.

MovingWorlds has supported more than 3,000 successful projects across 110 countries, unlocking more than $50 million in pro-bono consulting expertise for social enterprises. If you are a social enterprise in need of skilled support in AI or another line of business, for support on the MovingWorlds’ platform. 

NTT DATA is a $30+ billion global leader in AI, digital business and technology services, serving 75% of the Fortune Global 100.

The 麻豆原创 solutions will help NTT DATA replace multiple legacy HR systems with one unified platform for people data and processes, strengthening decision-making, employee experience and workforce planning. 麻豆原创 SuccessFactors solutions will serve as the system of record for people and talent data, working alongside NTT DATA’s existing employee service platform and specialist workforce planning tools. 麻豆原创 Business Data Cloud will connect this data with insights across other business functions.

鈥淭he initiative reflects NTT DATA’s view of talent as a strategic differentiator and AI as a capability that should be embedded across enterprise organizations,鈥� said Stijn Nauwelaerts, Chief People Officer, NTT DATA, Inc. 鈥淯ltimately, this is about creating an environment where our people feel empowered to do their best work, wherever they are in the world.鈥�

The deployment builds on a strategic partnership between 麻豆原创 and NTT DATA spanning more than 36 years, during which NTT DATA has collaborated as an 麻豆原创 platinum partner, global service partner and global reseller for 麻豆原创. In 2025, NTT DATA adopted 麻豆原创 Cloud ERP Private solutions to modernize its core systems.

麻豆原创 SuccessFactors solutions will now be deployed internally at NTT DATA over a 12-month period, with the company applying its own 麻豆原创 expertise to design and roll out the platform. This will create a single, authoritative source of HR data and processes, laying the foundation for faster, more consistent HR services across the organization. Leading its own implementation will also strengthen NTT DATA’s ability to guide clients through AI-driven HR transformation, with firsthand experience of the solutions it delivers.

鈥淣TT DATA is demonstrating how AI and cloud technology can redefine the employee experience,鈥� said Thomas Saueressig, Chief Customer Officer and Member of the Executive Board of 麻豆原创 SE. 鈥淲ith a unified, intelligent HR platform, the company will unlock new levels of productivity and scale a people strategy that supports a connected workforce worldwide.鈥�

Visit the . Get 麻豆原创 news via  and .

Media Contacts:
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麻豆原创 麻豆原创 Room; press@sap.com

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In an interview, 尝颈茅苍补谤诲 talks about the evolution of 麻豆原创’s proprietary programming language and ABAP platform, and discusses what the innovations announced at 麻豆原创 Sapphire will mean for customers.

Q: In our first conversation, you explained what ABAP is, what the future looks like, and why agentic AI will change the market. Let’s start from there and then dive deeper. How does ABAP interact with other development technologies in today’s 麻豆原创 landscapes?

Sonja 尝颈茅苍补谤诲: ABAP has been around for over 40 years and has been evolving ever since. With the era of agentic AI, we are now reaching the next stage of that evolution. The modern version of the development model is ABAP Cloud, which has been developed from the outset according to the guiding principles of openness and comprehensive support for the business logic.

We have robust, well-defined APIs that are interfaces to the outside world and offer a full environment with Open Data Protocol (OData) services, modern front ends, and an integration layer. This enables the development of modern web applications. This is in addition to 麻豆原创 Business Technology Platform (麻豆原创 BTP) extensions and an external system like 麻豆原创 Integration Suite, which can also be integrated.

We currently use Eclipse as a development environment. Starting in Q2 2026, ABAP development听is听also possible in Visual Studio Code, which is a milestone. The community was quite vocal in requesting this and it was an open door for us. Visual Studio Code is gaining ground and is currently the preferred IDE (integrated development environment) for many developers. Many AI extensions on the market being optimized for it first.

We have redesigned the architecture and are pursuing an open IDE strategy where we can also offer further IDEs for ABAP development. By opening it up, we now have a rich third-party ecosystem that we can leverage for AI, such as GitHub Copilot and Amazon Q.

In addition, we have published an ABAP MCP (Model Context Protocol) server that lets ABAP developers benefit from the entire AI tooling and ecosystem. This is a major, important step and an impressive example of interoperability. Furthermore, we can still rely on the strength of ABAP business logic. With this approach, we are combining the open IDE strategy with a high rate of innovation.

You mentioned the ABAP MCP server. What is that exactly?

尝颈茅苍补谤诲: MCP stands for Model Context Protocol and is an open standard for AI agents to interact with external tools and systems in a structured manner. It is a universal language that AI agents use to ask questions, trigger actions, and retrieve results.

The ABAP MCP server provides ABAP development capabilities based on this protocol. Any agent that supports MCP can interact with ABAP code and our ABAP systems in an intelligent, agent-driven approach. It is a new channel through which we can make our ABAP-specific capabilities available to the outside world.

The ABAP MCP server is an important building block for everything related to agentic AI and an important technical foundation for our new Custom Code Assistant for 麻豆原创 S/4HANA transformation. The ABAP MCP server for Eclipse is available as of Q2 2026.

What are the risks of using AI in 麻豆原创 enterprise systems and how do you address them?

尝颈茅苍补谤诲: The entire AI market is incredibly dynamic, fast-paced, and characterized by different interests. As such, we need to carefully examine which solutions are durable, robust, and trustworthy enough to run the world’s business processes. We want our solutions to remain secure, compliant, and true to everything 麻豆原创 stands for.

We must put our core mission at the heart of all decisions. ABAP platform is known for its ability to run large enterprise business. Our customers and partners trust this capability The AI solutions on the market do not qualify for this through their ability to build short-lived solutions, but instead by supporting our core capabilities. That鈥檚 why it helps to take a step back, look at the big picture, and make sustainable decisions, but also to remain open to revising past decisions in response to major shifts in the market.

The second point is the accuracy of the code. AI can generate seemingly plausible code that contains subtle errors. To counter this, we rely on a combination of human review and thorough agent testing, keeping humans in the loop at every critical step. AI agents handle the quality checks and validation, with developers making the judgement calls.

Another risk is the loss of business logic during the transformation. We want to help our customers migrate their legacy applications to modern solutions. We are developing custom code management agents for this purpose. The first was released in June. It is crucial for our customers and partners to retain their business logic during the transformation.

Lastly, security and data protection are central topics. AI models always need context to be effective. In the enterprise environment, this context can contain sensitive business data. That鈥檚 why we are taking a very careful approach: ABAP AI services only operate within established 麻豆原创 compliance and trust frameworks, for example. Customers always have control over what is shared and what isn鈥檛.

Let鈥檚 talk about 麻豆原创 Sapphire in 2026. What innovations from your area were presented?

尝颈茅苍补谤诲: 麻豆原创 Sapphire is also a very important conference for . In Q2 2026, we published the first release of the ABAP development tools for Visual Studio Code, initially in the ABAP Cloud scope including 麻豆原创 Fiori app development and with integration of GitHub Copilot and Amazon Q as AI solutions. Another goal is to support classic ABAP development in Visual Studio Code; additional ABAP object types will follow throughout the year. Also, as of Q2 2026, the ABAP MCP server for Eclipse and Visual Studio Code are generally available to connect third-party solutions, particularly AI-specific third-party tools, to the ABAP system.

We also delivered the first Custom Code Assistant in Q2 2026. The feedback is promising. This enables us to automate and accelerate code migration significantly. In this approach, all communication between legacy migration tools and new agents will take place through an engagement layer. Our overall migration strategy, which we presented at 麻豆原创 Sapphire, comprises seven different agent families across all phases of a migration project, from planning to execution.

Smaller customers and those with older system versions often found it difficult to access AI tools. What is changing here?

尝颈茅苍补谤诲: The barriers to entry are getting much lower. We are switching from users-per-month billing to a usage-based model. Billing will be according to actual usage, based on 鈥淎I units鈥� with individual prices. This will help make it easier to use our AI solutions. Customers will be able to better plan how much they want to spend on AI solutions.

In addition, as of Q2 2026, we introduced a side-by-side service that enables the use of all AI solutions, regardless of release. The availability of ABAP AI will be expanded to include 麻豆原创 S/4HANA Cloud Private Edition for all releases from 2021 and later. This enables us to support most of our customers with AI capabilities, even those that have older releases.

How do ABAP and AI agents fit into 麻豆原创鈥檚 longer-term product vision?

尝颈茅苍补谤诲: We are following the overall 麻豆原创 strategy, of course. It was apparent at 麻豆原创 Sapphire that agentic AI is the new theme. In the past, we had Infrastructure as a Service, Platform as a Service, and Software as a Service. Agentic AI solutions are now being added.

I like to use three concentric circles to describe our vision: ABAP Cloud is at the center, as the modern, clean-core variant of our language for long-term stability. The second circle is the ABAP AI layer: developer tools for code explanation, code generation, and ghost texting. The outer circle is comprised of AI agents, a network of specialized agents for complex and multi-step tasks such as transformation, migration, and quality validation. They can also take on development tasks, however, significantly reducing the required effort. This can free up time for decisions regarding business logic and architecture, as well as for verifying quality.

The circles coexist and reinforce each other. The fundamental orientation of the platform and the core of the solutions remain stable. Agents and AI skills complement this in the best possible way.

What milestones should customers and partners pay attention to?

尝颈茅苍补谤诲: There are four main milestones. The development environment for Visual Studio Code and the MCP server were released in Q2 2026. This enables the development of AI agents in an open IDE ecosystem. We released the first agent for the custom code migration strategy in Q2 2026, along with the extension of ABAP AI to include 麻豆原创 S/4HANA Cloud Private Edition release 2021 and later. A second agent for clean core transformation is planned for Q3 2026. And we will continue to develop multi-agent orchestration and add more agents to the portfolio in the course of 2026.

Do you have any other takeaways to share?

尝颈茅苍补谤诲: Development should be seen as an opportunity. It鈥檚 all about development and collaboration. ABAP and developers remain a strong team. AI will not change anything here. ABAP has a long track record of successful reinvention. AI is just the next chapter, taking over routine tasks and creating freedom for what creates value: knowing your business, making architectural decisions, providing high quality, and meeting security and compliance requirements.

Customers and developers can trust 麻豆原创鈥檚 AI to be implemented reliably: with a clear road map and high quality, security, and compliance standards that are critical for enterprise systems. We work closely with the community and our customers and partners. That is a key success factor.

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Danish wholesaler Lemvigh-M眉ller transformed manual document handling with 麻豆原创 Business AI.

The company automatically processes incoming business documents, including orders, delivery notes, and invoices received as PDFs and emails, reducing manual work and helping teams focus on execution rather than paperwork. Discover how a 200-hour AI project became a scalable foundation for broader business process automation. Read the article.

For Lemvigh-M眉ller, an efficient supply chain isn’t a nice-to-have鈥攊t’s the business model. “Our company is low margin, and we are living from a very efficient supply chain,” says Frederik Aakerlund, CIO of Lemvigh-M眉ller. “We need to cut costs wherever we can, and we need to make sure our customers get our products as quickly as possible.”

Not every business partner connects via EDI (Electronic Data Interchange), the standard for exchanging business documents directly between IT systems. For Lemvigh-M眉ller, that means a steady stream of orders, delivery notes, and invoices arriving as PDFs and emails鈥攄ocuments that, until recently, had to be read and entered manually.

“Today we are receiving so many PDF files and emails that we don’t have the time to read them,” Aakerlund explains. “Basically, we don’t update our system, or we don’t find the deviations from what we expect, quickly enough.”

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Letting AI read the mail

To close that gap, the team built a use case for receiving documents from business partners that can’t be exchanged via EDI and having them read automatically by 麻豆原创 AI engines.

“This is exactly where AI is helping us,” Aakerlund says. “It’s reading 10 20-page documents in a few seconds, updating our system, and there’s no person involved.”

Behind the scenes, the solution combines a mix of 麻豆原创 Business AI Platform, AI components, and 麻豆原创 Fiori apps, integrated with Lemvigh-M眉ller’s core 麻豆原创 system鈥斅槎乖� Cloud ERP Private, which the company adopted two years ago.

The shift in daily work is tangible. “Our users, instead of reading a lot of emails, are just working in a dashboard, finding the things they need to work on,” Aakerlund says. “We’re living the in a small part of our business.”
What started as a single use case has since become a template. “We’ve kind of made it a template for receiving business documents like orders, delivery notes, invoices, and so on,” Aakerlund notes. “Whenever we can’t get them digitally, we read them via this new system. It works for all kinds of PDFs and emails we receive from our business partners.”

Aakerlund’s advice to other companies considering AI projects: don’t start big.

“A good piece of advice could be to find the pockets of inefficiency in your company and apply AI there, instead of going for some really, really big project,” he says. The first version of the use case took just 200 hours over 10 weeks to build. “It turned out to be a reusable architecture, with reusable templates for a lot of other business processes.”

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

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

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

Start with the outcome, not the technology

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

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

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

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

Build a connected data foundation

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

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

Apply human oversight where it matters most

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

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

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

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

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

Measure value and risk together

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

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

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

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

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

It reinvented circus arts and became a world leader in live entertainment, performing for more than 365 million spectators in 90 countries. Today, the organization operates 38 shows in cities around the world, supported by over 4,000 touring artists and staff from more than 80 countries.

Each touring show functions like a moving profit center. A production may operate in Mexico City, then move to London, then Paris鈥攂ringing with it new companies, employees, assets, tax requirements, local regulations, merchandising operations, and artists from dozens of nationalities.

As Philippe Lalumi猫re, vice president of Information Technology at Cirque du Soleil, explains, 鈥淧eople underestimate the complexity of running a circus.鈥�

Orchestrating an autonomous accounts payable process

This is especially true for the company鈥檚 accounts payable (AP) department. Every year, more than 70,000 invoices flow through Cirque du Soleil, non-stop, 24/7. Around 40% of vendor inquiries are standard requests for invoice status. But answering those questions was far from simple. AP specialists had to search across systems, review invoice histories, understand payment status, determine the cause of delays, and manually respond.

The work was repetitive, time-consuming, and emotionally draining. 鈥淔rom a morale point of view, receiving e-mails from suppliers, some of them a bit impolite because they鈥檙e asking, 鈥榃hen are we going to get paid?鈥欌�攊t鈥檚 not a fun job,鈥� Lalumi猫re says.

Finding the right problem to solve鈥攑utting AI into action

As an 麻豆原创 customer for more than 25 years, Cirque du Soleil knows firsthand how to leverage 麻豆原创 technology not only to run its operations, but also to reinvent them.

Cirque du Soleil, known听as听an early adopter and leader in digital transformation, was approached by 麻豆原创听AppHaus听with a question: how could 麻豆原创 generative AI technology be used to improve your business processes?

The answer emerged through collaborative workshops involving 麻豆原创, Cirque du Soleil’s IT team, and business users across its departments. Accounts payable quickly rose to the top due to its lean structure, high volume of interactions, and clear automation potential. And the choice aligned with the broader business evolution鈥擜P automation is one of the most widely adopted AI use cases across industries.

With a clear opportunity identified, Cirque du Soleil and 麻豆原创 moved from ideation to execution, developing an AI-powered solution that automated AP processes and improved responsiveness.

AI enters stage right鈥攆rom vision to reality

The workshops led to something more than an automation project鈥攖hey led to Genato, a multilingual AI agent that now works alongside the AP team.

The polyglot agent scans the AP inbox, analyzes sentiment, identifies urgency, extracts invoice numbers from messages and attachments, connects to 麻豆原创 data to retrieve invoice status, and drafts a response for human review. If it cannot find an invoice or resolve an inquiry, it flags the issue.

鈥淭rust in the quality of the answers coming from the agent was an initial concern,鈥� Lalumi猫re says. 鈥淏ut, the team quickly realized that Genato鈥檚 information was spot on.鈥�

Together, Genato and the AP team now serve Cirque du Soleil’s diverse global supplier network more efficiently.

Lalumi猫re, however, is clear about one thing: 鈥淵es, AI is a very powerful tool, but it鈥檚 not pixie dust. Sprinkling AI into processes is not going to solve everything. There is work involved.鈥�

That work included designing听appropriate connectors听and integrating with the company鈥檚 麻豆原创 and AP systems using . This enterprise AI foundation听can bring together capabilities and technologies (think 麻豆原创听Business Data Cloud, Business Transformation Management solutions, and 麻豆原创 Business AI), unifying AI, data, process context, and governance, so customers can build, integrate, scale, and run AI that delivers business impact听while working to ensure the solution is sustainable and cost-efficient.

The impact was evident almost immediately. Generative AI began prioritizing urgent supplier requests, retrieving invoice information, drafting responses, and translating communications automatically, delivering measurable improvements across the AP organization:

• 97.92% improvement in handling priority requests
• 25% reduction in AP backlog
• 25% faster response times for non-urgent inquiries
• Improved vendor satisfaction and employee morale

The impact on employees cannot be overstated. By removing repetitive research and emotionally charged supplier follow-up from the team鈥檚 workload, Genato has become more than a tool. 鈥淲e now include Genato as a virtual team member,鈥� Lalumi猫re explains, 鈥淚t鈥檚 a paradigm shift.鈥�

That adoption is the clearest sign of success. Lalumi猫re recalls that after the prototype moved into production, one of the end users made her feelings clear: 鈥淣o, no, no, you cannot take it away from me! There is no way I鈥檓 going to live without it now.鈥� Today, she is one of the solution鈥檚 biggest ambassadors.

Collectively, the solution demonstrates how AI can be introduced into a focused business process, quickly earn user trust, and create a blueprint for broader enterprise innovation.

As Lalumi猫re puts it, 鈥淵ou鈥痗ould say we鈥檙e an 麻豆原创 shop.鈥�

The next act of AI innovation

For Cirque du Soleil, accounts payable is only the beginning.

Lalumi猫re believes that 鈥淎I is a huge and beautiful tool, but you still need human judgment to prioritize use cases and move forward.鈥� He also sees AI evolving across three layers: personal productivity with solutions such as Joule; AI applied to business processes, such as Genato; and, eventually, AI embedded in the audience experience itself. 鈥淚 think we鈥檙e at the dawn of a new era of circus arts,鈥� he says.

His advice to others beginning their AI journey is simple: start small, involve users early, and be willing to experiment. 鈥淭ry a small proof of concept and be ready to throw them away,鈥� Lalumi猫re says. 鈥淭he AI train is moving, and you have to hop on.鈥�

For a company built on reinvention, transforming accounts payable may seem far removed from the spotlight, but for Lalumi猫re, the principle is the same: innovation happens when people are willing to rethink what’s possible. Today, that mindset is improving back-office operations. Tomorrow, it may help redefine the audience’s experience.

Sid Misra is CMO of 麻豆原创 Business AI Platform Technology Foundation.
Top image courtesy of Cirque du Soleil.

Between technical deployment and actual adoption, there is often a gap. What is needed goes beyond tools鈥攊t requires enablement, communication, and organizational change management that puts people first. Experiences from 麻豆原创 customers show how this can succeed.

Global transformation, local anchoring as a guiding principle

Dulaan Punsag-Odefey cites a number that immediately makes the challenge tangible: 265. That is how many key users serve as change ambassadors at Hapag-Lloyd, distributed across six regions worldwide. The shipping company is one of the five largest in the world and is in the midst of an 麻豆原创 S/4HANA Finance transformation. The “Fast Forward” program affects 4,000 麻豆原创 users in more than 140 countries.

Punsag-Odefey, organizational change management (OCM) lead at Hapag-Lloyd, explains: “Change management is anchored in our program as a strategic enabler. Not as an add-on, but as a core element.”

In practice, this means the 265 ambassadors translate global standards into local language and local practice. Controllers are expected to evolve into business partners who no longer just produce Excel spreadsheets but deliver decision-ready recommendations for sales and operations. The message to the workforce is “It will be different, but better.”

How a federal agency demonstrates effective change management

That transformation can succeed without following the textbook is demonstrated by the Bundesanstalt f眉r Post und Telekommunikation (BAnst PT), Germany’s Federal Agency for Post and Telecommunications. The agency implemented 麻豆原创 S/4HANA in just one year: greenfield, public cloud, 1,000 affected users, go-live on January 1, 2026. In parallel, the organization moved to a new administration building with a new-work concept.

Simone Kunze, specialist in the 麻豆原创 Service Center at BAnst PT, knows the phrases that come up in every organization: “Is there an official directive for this?” or “Standard won’t work for us.” What helped was honest communication and direct moderation within business units instead of token feedback sessions.

A fit-to-standard approach replaced legacy custom solutions. Key users were developed from project experts who had already built depth through workshops, user stories, and test cases. The investment in support paid off: after go-live, dozens of thank you e-mails came in regarding the new 麻豆原创 travel expense management and Fiori apps, and survey response rates exceeded 70%.

Thilo Menges from the Medical University of Lusatia (MuL) takes this one step further. His project has a unique starting point: with 3.6 billion euros in funding, an entirely new organization is being built from scratch, including 麻豆原创 technologies. Menges makes a point that many organizations do not state this clearly: “For me, change management is an investment protection measure. This is an organizational project, and people need support in change processes.”

Change management in his project accounts for 4.3% of the total budget鈥攖he largest single line item in the 麻豆原创 contract.

麻豆原创鈥檚 change management framework with its six dimensions, which MuL follows, is integrated into the 麻豆原创 Activate methodology. Particularly important are early assessments, target-group-specific communication, and the identification of trusted multipliers rather than a blanket approach.

What was also highlighted: learning does not end at go-live. Tools like WalkMe enable context-sensitive support in the flow of work, especially for infrequent processes. Enabling the organization to independently maintain and evolve these systems is critical for sustained success.

The organizations mentioned above are supported by change management consultants from 麻豆原创. More information is available .

From shadow AI to structured integration at KIT

While BAnst PT and Hapag-Lloyd are primarily transforming 麻豆原创 system landscapes, the Karlsruhe Institute of Technology (KIT) faces a different question: how do you get 25,000 students and 10,000 employees to use AI responsibly, instead of each person experimenting on their own in the shadows?

In 18 months, KIT made the journey from uncontrolled AI usage to an AI toolbox with governance rules. Rather than issuing bans, KIT focuses on enablement.

Andreas Sexauer from the Center for Technology-Enhanced Learning at KIT describes the approach as follows: a mandatory qualification module covers foundational knowledge and legal aspects before students and faculty gain access to the AI toolbox. In parallel, use case workshops run across departments, from leadership teams to the legal department. After the teaching rollout in April 2025, 31 didactic chatbots were created in the first seven days. Faculty configure them directly in the learning management system for their respective courses.

KIT also takes a pragmatic approach to costs: after initially providing free access, a budget cap per person per month was introduced.

What research confirms

Whether shipping company, federal agency, or university, all customers report similar patterns. Prof. Dr. Renate Osterchrist from the Munich University of Applied Sciences provides the scientific foundation: she analyzed 119 studies on the effectiveness of change interventions, examining six intervention fields: communication, support, involvement, reinforcement, social influence, and coercion. Key effectiveness factors in change include:

• Dialogue formats are more effective than one-way communication.
• Coaching for managers improves not only their leadership capability but also measurably enhances implementation competence. Coaching and peer exchange are also very beneficial for employees.
• The dimension of coercion had been under-researched until now: clarity in messaging about what behavior is expected proves helpful. Manipulation and political maneuvering, on the other hand, reduce commitment.
• The frequently cited claim that 70% of all change projects fail is not supported by current data.
• The statement “Honestly, I have never experienced a change where there was too much communication” further underscores the important role of communication.

When AI agents enter the picture, change becomes even more critical

What happens when not only new systems are introduced but AI agents take over parts of the work? This is precisely the question that arose when Joule Studio 2.0 was demonstrated live at the forum. With this solution, 麻豆原创 customers can create AI agents that access their business context: 麻豆原创 Knowledge Graph, process models, and domain knowledge. The agents are code-based and transparent. Developers describe the desired outcome in natural language; Joule Studio can generate the specification and executable code. Both no-code and pro-code approaches are possible.

The discussions that followed amongst 麻豆原创 customers and partners made clear that the change management described above will be essential here. When agents take over tasks, roles change, responsibilities shift, and the way humans and machines collaborate is transformed.

A full documentation of the 麻豆原创 Learning and Adoption Forum 2026, including videos, slides, and a chatbot, is .

Thomas Jenewein is business development manager for AI, Transformation, & Adoption Services at 麻豆原创.

Picture this: You are an IT manager at a mid-sized enterprise running 麻豆原创. A critical issue surfaces midweek. Your team scrambles to fix the problem, searching through documentation, reviewing the multiple support channels available, and monitoring the situation to ensure the issue does not snowball further. A business-down situation is stressful enough without having to navigate a complex support experience. With 麻豆原创鈥檚 Support Accreditation you can access a free enablement course designed to get you all the help you need. Through its structured, hands-on learning experience, this course helps equip customers, partners, and consultants to confidently get the most out of 麻豆原创鈥檚 support.

With the rapid growth of AI over the past few years, the support landscape of 2026 looks nothing like it did five years ago. Today’s support landscape includes AI-powered assistants, predictive capabilities, intelligent recommendations, and real-time engagement channels. The latest release of the Support Accreditation course can prepare learners to take full advantage of these innovations and more.

What鈥檚 new

After gathering insights from more than 40,000 learners, collaborating with internal support experts, and grounding every decision in real user feedback, 麻豆原创 has redesigned Support Accreditation from the ground up. What does the 2026 release of Support Accreditation offer customers, partners, and consultants? Upon completion, learners earn a digital accreditation badge that validates their expertise and skills in using self-service tools, accessing AI-powered support solutions, navigating support channels with clarity, enabling focused and high-quality support interactions, collaborating effectively, and maximizing the value of 麻豆原创 Enterprise Support. If you have heard about Joule in 麻豆原创 for Me, intelligent search, preventive support, incident solution matching, channel recommenders, or predictors for products, product functions, and priority鈥攖o name a few AI-driven features鈥攜ou can now explore these topics further. 

This release combines how people learn best with how support is evolving into a single, reimagined experience. Based on learner feedback, the latest release of Support Accreditation delivers:

• Human-centered learning
• Shorter, more focused learning units
• Content focused on real-world outcomes

The real benefits

Support Accreditation in 2026 isn’t just about earning the badge鈥攊t鈥檚 a credential that validates your ability to work effectively with 麻豆原创鈥檚 comprehensive support offerings. The accreditation can also equip you to: 

• Resolve issues faster by using intelligent self-service tools, AI-powered recommendations, and proven best practices to cut resolution times and queues.
• Increase self-sufficiency by reducing dependencies on traditional support interactions through 麻豆原创’s ecosystem of knowledge, diagnostics, automation, and digital capabilities.
• Improve support interactions by learning how to create higher-quality cases, communicate more effectively, and use the right channels at the right time.
• Stay ahead through continuous learning and be ready to take advantage of new capabilities.

How to get started

The Support Accreditation 2026 course is available now through 麻豆原创’s learning platform free of charge, on-demand, self-paced, and completed in around 60 minutes.

Stefan Steinle is executive vice president and head of Global Customer Support at 麻豆原创.