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

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

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

A unified foundation for spend data

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

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

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

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

What鈥檚 included at launch

Several capabilities are available from day one, including:

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

An operating model discussion

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

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

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

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

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

This model marks the next leap in tabular AI, giving organizations access to unmatched tabular AI predictions on structured business data without model training or tuning. For 麻豆原创 customers, TabPFN-3.5 Plus is now available in 麻豆原创 AI Core.

Critical enterprise business decisions,聽such as cash flow forecasting, payment delays or聽supplier risk scoring, run on structured, tabular data. While LLMs聽excel at language and knowledge,鈥痶abular foundation models聽(TFMs)聽are purpose-built for聽structured data and聽can accurately predict business outcomes based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk and more.聽

With TabPFN-3.5, 麻豆原创 customers can work with data as it exists in their systems. Missing values, mixed data types and inconsistent fields are handled by the model, returning聽accurate聽predictions without preprocessing.聽This聽newest model from Prior Labs鈥� family of tabular AI models聽uses in-context learning to make predictions from raw tabular data, handling columns with thousands of distinct values, such as product codes or customer identifiers, and mixed data types natively, without the trial-and-error configuration that traditional models require.聽TabPFN-3.5 Plus聽is the most聽accurate聽and scalable tabular foundation model available today, based聽on聽TabArena聽and聽BeyondArena, two聽external聽benchmarks designed to evaluate predictions over real-world data.聽

鈥淩eal-world data is rarely perfect.聽Datasets often聽contain聽complex relationships and varying conditions that make traditional machine learning difficult. TabPFN-3.5 is聽specifically聽built for these challenges, providing industry-leading accuracy and scalability for tabular data with less manual effort鈥攎aking it the聽most effective聽tabular AI model in the industry聽to date,鈥澛爏aid Philipp Herzig, Chief Technology Officer, 麻豆原创聽SE.聽鈥淲ith TabPFN-3.5聽and the 麻豆原创-RPT model family, customers get聽accurate聽predictions from labeled business data in minutes, with no training聽required.聽For us,聽tabular AI is not a supporting feature of the Autonomous Enterprise, it is the foundation.鈥澛�

For more detailed information and to learn how to get started on 麻豆原创 AI Core, see the , and view the .

麻豆原创 completed its acquisition of Prior Labs in July 2026, bringing one of the world鈥檚 leading TFM research teams into the 麻豆原创 family. Prior Labs will continue to operate as an independent entity, with 麻豆原创 having previously committed to invest more than 鈧�1 billion to scale it into a globally leading frontier AI lab for the structured data that underpins the world鈥檚 businesses.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Alex Vaught, +1 (206) 678-5712, alex.vaught@sap.com, PST 
; press@sap.com

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

Unlike traditional esports tournaments that play out over a few hours, Race to World First pushes teams through days and sometimes weeks of continuous competition. Players face extreme cognitive load, limited recovery time, and the constant pressure to execute flawlessly against the most complex encounters ever designed in gaming. At that level, performance is no longer just about mechanics. It’s about endurance. And endurance creates a data challenge.

Performance analytics beyond the game

Over the last several years, Team Liquid has become one of the most innovative organizations in esports.

Together, we have built data platforms, advanced analytics capabilities, and AI-powered solutions such as Joule Agents that help players, analysts, and coaches access insights faster than ever before. As we discussed our next innovation journey with the team, one question emerged quickly: What if coaches could see not only how players are performing in the game, but also how they are performing as human beings?

Historically, game statistics, biometric measurements, and wellness data were often analyzed independently, limiting the ability to understand how they influence one another. Therefore, the next evolution of esports analytics is not collecting more data. It is connecting data.

For Race to World First, we helped Team Liquid bring gameplay analytics, player wellness information, and biometric signals from wearable devices together in 鈥攁nd relationships that were previously difficult to identify became visible.

This sounds simple, but it fundamentally changes how coaching decisions are made.

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One unified view of the entire team

For the first time, Team Liquid鈥檚 coaches, analysts, and performance experts can see how physical readiness, stress indicators, recovery metrics, and in-game performance interact鈥攊n one view instead of separate dashboards and spreadsheets.

During Race to World First, waiting is often not an option. Fatigue develops in real time, and physical and mental stress accumulate throughout the competition. With live data streams, Team Liquid’s coaching staff can now monitor conditions as they unfold and identify early warning signals before they impact performance.

AI that amplifies human expertise

Data becomes even more valuable when AI helps make sense of it. That is where comes into play. By connecting previously isolated datasets, Joule analyzes trends across gameplay, wellness, and biometric information simultaneously. Coaches receive proactive recommendations from the underlying data.

Imagine receiving insights such as: “Players鈥� reaction times are getting longer, and stress indicators are elevated. Consider a recovery period before the next progression attempt.” Or: “This tactic has historically underperformed with the current roster configuration. Alternative strategies have delivered stronger results under similar conditions.”

AI does not replace human expertise; it amplifies it by giving Team Liquid鈥檚 coaches access to information that would otherwise remain hidden across thousands of data points.

Proving ground for human performance

This collaboration represents something bigger than esports. Competitive gaming has become one of the world’s most sophisticated environments for exploring human performance鈥攊ts scale, intensity, and speed create a unique proving ground.

Team Liquid鈥檚 challenges are not so different from those organizations face in business: bringing fragmented data together, turning information into action, and helping people make better decisions under pressure. It is the question we started with, asked at a larger scale: How are people performing, not only within systems and processes, but as human beings?

Race to World First raises the stakes. By combining connected data in 麻豆原创 Business Data Cloud with AI-powered insights from Joule, we are exploring what becomes possible when technology helps organizations understand performance holistically鈥攎ost importantly, how to help people perform at their best when it matters most.

In esports, success will still be measured in milliseconds. Sustaining that precision for weeks is a different challenge, one determined by how well a team is supported by connected data, intelligent analytics, and a deeper understanding of the people behind the numbers.

Because in the end, championships are won by teams. Data simply helps those teams unlock their full potential.

Benjamin Blau is chief process and information officer at 麻豆原创.

Since its launch in 1996, the 麻豆原创 Certification program has provided learners at different stages of their careers with a trusted way to validate their 麻豆原创 solution skills, while helping organizations identify relevant expertise across 麻豆原创鈥檚 evolving technology portfolio. The numbers tell part of the story. In the first year alone, nearly 1,000 professionals earned their certification. Since then, hundreds of thousands of individuals have earned an 麻豆原创 Certification each year as the program has expanded alongside 麻豆原创鈥檚 portfolio and global ecosystem. But the lasting impact of 麻豆原创 Certification extends beyond the numbers. For three decades, it has evolved alongside the 麻豆原创 portfolio, providing a trusted way to validate their expertise as technology and customer needs have changed.

Built to evolve

A defining characteristic of 麻豆原创 Certification has been its ability to evolve alongside 麻豆原创 technology. Since 1996, the program has grown from a handful of 麻豆原创 R/3 exams to credentials spanning the full 麻豆原创 portfolio. By the 2000s, new certifications covered 麻豆原创 NetWeaver, 麻豆原创 Business One, and 麻豆原创 SuccessFactors solutions. When 麻豆原创 S/4HANA launched in 2015, certification tracked every step of the transition. Each shift in the technology landscape brought a new wave of credentials, built to validate exactly the expertise the market needed most.

Access expanded, too, moving from 麻豆原创 training locations to test centers around the world and, later, to fully remote delivery. Digital badges made credentials easier to share across professional networks and in the context of career opportunities. As cloud solutions introduced faster release cycles, the program evolved to match, introducing regular renewal assessments to help learners keep their skills and credentials current.

鈥淐ertification has been part of my 麻豆原创 journey for many years. For me, it has always been about continuing to learn and making sure my skills evolve as 麻豆原创 technology evolves. I have experienced the changes in 麻豆原创 Certification firsthand鈥攆rom on-site exams and webcam-proctored, multiple-choice exams to today鈥檚 scenario- and system-based assessments. From my first certification in 2008 to ABAP Cloud certification in 2024, Integration Developer in 2025, and 麻豆原创 Generative AI certification earlier this year, continuing to learn and validate my skills has helped me take on new challenges and keep moving forward. Even after many years working with 麻豆原创, there is always something new to learn.鈥�

, Software Engineer and 麻豆原创 Integration Consultant at Deloitte

Certification reimagined

As 麻豆原创 Certification entered its next chapter, 麻豆原创 introduced a to validating 麻豆原创 expertise by expanding how skills are assessed beyond traditional multiple-choice exams to include practical, performance-based assessments. Candidates navigate real challenges in live 麻豆原创 systems and can use AI tools during exams by design, not exception. It is certification that reflects the profession it is meant to validate.

As part of 麻豆原创’s commitment to equip 12 million people with AI-ready skills by 2030, 麻豆原创 Certification can provide a way for professionals to validate the real skills they need for an AI-first world. Certification now measures how candidates solve real business challenges, not just what they know.

鈥淔or 30 years, 麻豆原创 Certification has helped people demonstrate the skills they need to grow their careers and help organizations get more value from 麻豆原创 technology. What has kept it relevant is our willingness to evolve. As AI changes how people work, certification must change with it, moving beyond what someone knows to validating what they can actually do.鈥�

Diana Roesner, Head of Certification Transformation

Three decades after the first 麻豆原创 Certification exams, the program continues to evolve alongside 麻豆原创 technologies and customer needs. As the reimagined certification approach continues to take shape, 麻豆原创 Certification remains focused on validating the skills needed today while preparing learners for the future of enterprise technology.

To learn more about 麻豆原创 Certification, explore available certifications, or learn more about certification reimagined, visit .

Timo Schuette is global vice president for 麻豆原创 Product & Solution Learning.

At first glance, the space looks like a curated boutique. Clothing racks, soft lighting, and attentive staff set the scene. Pick up an item, and nearby displays can respond. Teams can also follow fitting-room activity and the sales floor in real time.

At the center is the Retail Innovation Lab by NYFW Collections and 麻豆原创, featuring fashion label RE/DONE. 麻豆原创 and N4XT Experiences, which operates NYFW Collections, built the lab as part of our . The store is open from September 11-30. For three weeks, visitors can experience connected retail in SoHo.

The new store is a progression of a three-day pop-up developed with fashion brand Public School New York in February. This season we are expanding the Retail Innovation Lab format to a full-fledged RE/DONE store, open to the public for three weeks. At the heart of it is a private “Command Center,” connecting the sales floor with the operations behind it. The Retail Innovation Lab doubles as an expansion of our 麻豆原创 Experience Centers, offering 麻豆原创 customers and prospects the chance to explore 麻豆原创 software and partner solutions in a real-life setting.

This approach reflects our co-innovation model, which aims to turn collaborative ideas into customer-facing experiences faster so visitors can experience the technology in action and understand its value firsthand. 麻豆原创, N4XT Experiences, and participating fashion brands contribute with their respective expertise. Together, we are developing a store concept that serves as a test bed for retail brands of various sizes.

From display to decision

Start your visit with a scan and opt in to a digital profile. As you browse, nearby displays can update with product details. A styling surface suggests complete looks and complementary pieces. Together, these features connect product discovery and styling in one experience.

In the fitting room, connected technology recognizes each item and size. An associate can respond quickly with another size or suggestion. When you’re ready, choose how to buy. Use your phone, ask an associate, or visit a traditional point of sale. Afterward, a personal digital wardrobe saves looks and keeps the experience going.

Behind the sales floor, the Command Center gathers movement, fitting-room, size, and sales signals. Teams gain a real-time view of customer activity and store performance.

Consider unexpected interest in one size: combined with inventory, merchandising, and replenishment information, that signal can reveal demand earlier. Teams can respond with greater precision. The store becomes a richer source of insight for assortment, inventory, and demand planning.

Co-innovation at the heart of industry

The lab also shows how our approach to co-innovation works. Customers and industry experts contribute their knowledge of operations, markets, and opportunities. 麻豆原创 brings more than 50 years of experience in business processes. It also brings the data, applications, and technology that connect them. Together, they ground innovation in the realities of retail and fashion.

In retail, that collaboration points toward autonomous commerce. Connected customer journeys and intelligent operations can work together within human-defined guardrails. The lab makes this direction tangible on the sales floor.

The same principle guides 麻豆原创’s approach to Industry AI, co-innovated with customers and industry experts. Their real-world knowledge gives AI context for industry processes and business decisions. AI can then understand what a signal means inside a business. It can also connect that signal to the surrounding processes.

That context differs by industry. In fashion retail, customer interactions can guide merchandising and demand. On a factory floor, production data can help teams improve operations. In consumer products, changing demand can inform planning and supply decisions. Each industry helps shape the AI built for it.

That’s the power of Industry AI. It brings an industry’s own knowledge into AI.

From industry expertise to intelligent action

Industry knowledge becomes more valuable when it moves businesses from insight to action. Companies can decide faster and orchestrate more processes across their operations.

That creates a path toward the Autonomous Enterprise. People define ambitions and guardrails; intelligent systems support them and execute more processes with speed, context, and precision. Human expertise stays central. It shapes the decisions and boundaries that move industries forward.

NYFW gives this collaboration a compelling stage. The lab shows how customer experiences and operations can connect in real time. Autonomous commerce brings that connection into focus for retail. Industry AI carries the same principle across industries.

Together, business knowledge, data, and AI create new ways for customers to act on insight.

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

To better drive success, our unified service release model will continue to deliver expanded capabilities seamlessly. This update demonstrates the real-world impact of our strategy, especially as businesses increasingly embed AI into their core operations.

Realizing value: documented progress and strong customer adoption

Our core mission remains to provide guidance that is simple, predictable, and directly connected to measurable business outcomes. We are delighted to see this approach gain substantial momentum. Hundreds of organizations have adopted our Advanced Success Plan and Max Success Plan, validating that deeper engagement leads to stronger, long-term results. Our customers鈥� success is our priority. We bring our entire portfolio together to help create a seamless experience, custom-fit to customer needs.

Empowering businesses with next-generation capabilities

To help organizations navigate in today鈥檚 dynamic market, we鈥檝e focused our services on three strategic growth drivers:

• Powering the Autonomous Enterprise: We help streamline processes and boost efficiency by pairing intelligent tools with expert guidance. This includes AI-powered capabilities like Joule for 麻豆原创 for Me for proactive insights and agentic case resolution to help deliver faster, more intelligent support.
• Enabling targeted business transitions: We deliver clear, structured pathways that help businesses confidently unlock 麻豆原创鈥檚 newest AI capabilities and innovations across specific lines of business. These tailored migration paths can simplify transition and integration processes.
• Driving front-office impact with productivity AI: Our focus can deliver measurable productivity gains for front-office teams through deeply embedded AI capabilities, enhanced release guidance, and strategic engagement planning aligned with business outcomes.

Expanding and integrating support for every stage of the 麻豆原创 journey

Our is streamlined into three success plans (Foundational, Advanced, and Max) with three supplemental offerings (development services, application management, and professional services), all designed to complement each other to support continuous adoption, innovation, and transformation at every stage of the journey. The focus is on clearly defining what each offering provides, when to use it, and the specific outcomes it can deliver.

Success plans: evolving as the primary engagement model

Our success plans continue to strengthen their position as they offer expanded solution area coverage and AI-powered capabilities. The Foundational Success Plan has full 麻豆原创 solution area coverage, providing clearer pathways to Advanced Success Plan and Max Success Plan engagements. New expert-led AI services are now available, with enhanced 麻豆原创 Build coverage to support AI maturity journeys. Furthermore, upgraded release guidance tooling helps improve planning consistency, and automated provisioning for Max Success Plan customers can streamline the engagement process.

The tangible impact of these integrated AI capabilities is already evident, with Joule for 麻豆原创 for Me seeing strong adoption with nearly 200,000 users year-to-date, while agentic case resolution uses AI agents to help automatically handle and resolve support cases, working to reduce resolution times and minimize the need for manual escalation.

Development services: unlocking the full potential of 麻豆原创 BTP

In the current release, our development services have been expanded to help unlock the full potential of 麻豆原创 Business Technology Platform (麻豆原创 BTP).

New extensibility packages offer structured guidance and best practice frameworks for custom applications and integrations to enable faster delivery and consistent quality. Furthermore, all managed development engagements formally embed clean core principles, which help make custom solutions upgrade-safe and future-proof. In addition, AI-assisted tooling is now standard in eligible engagements, working to further reduce timelines and improve code quality.

Application management: sustaining continuity and performance

Application management, which handles the day-to-day operations and ongoing optimization of a customer鈥檚 live 麻豆原创 environment, continues to evolve as a critical pillar to help ensure continuity and performance across 麻豆原创 environments.

麻豆原创 enables customers to operate the Autonomous Enterprise with confidence by continuously governing, securing, and optimizing autonomous applications and platform services. AI-assisted monitoring and intelligent incident remediation help proactively identify risks, reduce resolution times, and improve operational resilience. Through an integrated adopt-to-operate approach, 麻豆原创 can connect adoption, success planning, and application management into a seamless lifecycle experience, helping customers accelerate value realization, maximize business outcomes, and sustain transformation success.

Professional services: structured implementation and transformation

Our professional services can deliver targeted, one-time project expertise, including end-to-end implementations, complex migrations, upgrades, and system optimizations.

With the new release, 麻豆原创 is simplifying its professional services portfolio to support transformation journeys by aligning with success plans and integrating proven expertise into scalable engagement models. Refreshed outcome-based services help expand coverage across 麻豆原创 solutions and are designed to align with the Advanced Success Plan and Max Success Plan. Furthermore, formalized handoff protocols between success plans and professional services help foster seamless continuity of project context.

A partnership for continuous success

This unified portfolio release underscores 麻豆原创鈥檚 collective strength and shared commitment to help transform customers鈥� 麻豆原创 experience with a streamlined support portfolio built for impact:

• Modular choice: Easily scale services to match exact goals.
• Ongoing optimization: Continuously unlock software potential with proactive guidance.
• One connected system: Enjoy a frictionless, cohesive experience across the full suite.

Transforming your business shouldn’t be complicated. At 麻豆原创, we partner with you to turn complex transformation into measurable, lasting business results. Explore our newly evolved 麻豆原创 Services and Support portfolio to see how it can help maximize value and drive your strategic goals forward.

Dr. Uwe Grigoleit is senior vice president of Customer Evolution & Portfolio 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.

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

Building a platform for growth

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

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

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

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

Transformation is about people as much as technology

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

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

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

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

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

Preparing for what’s next

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

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

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

The road ahead: Success built on a strong foundation

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

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

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

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

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

We believe this recognition reflects our ongoing commitment to helping organizations navigate an increasingly complex world of work through innovation, global scale, and AI that helps connect workforce decisions to business outcomes. 

A new era of HCM 

Organizations today face unprecedented workforce challenges.

Skills requirements are evolving rapidly. Workforces are becoming increasingly distributed. Business priorities shift faster than traditional planning cycles can accommodate. At the same time, leaders are being asked to make workforce decisions with greater speed, precision, and confidence. As these pressures increase, the role of HR and HCM technology is fundamentally changing.

Organizations no longer need systems that simply record workforce data or automate HR processes. They need connected, intelligent systems that can help anticipate workforce needs, surface recommendations, remove friction from everyday work, and help people make better decisions. This is why we announced our vision for Autonomous HCM at 麻豆原创 Sapphire in May.

As part of 麻豆原创’s broader vision for the Autonomous Enterprise, Autonomous HCM brings together trusted workforce and business data, embedded intelligence, AI, and HR processes to help organizations respond more effectively to changing workforce needs. The goal is not simply to automate more tasks. It’s to help organizations understand what’s happening, determine what to do next, and execute with greater speed and confidence. Achieving this requires trusted workforce and business data working together to provide the context needed for better decisions and better outcomes. 

Bringing Autonomous HCM to life

Over the past year, 麻豆原创 has continued to invest in capabilities designed to help organizations move more seamlessly from workforce insight to workforce action. From new Joule and AI agents to and , these capabilities help connect workforce intelligence, decision-making, and execution across HR processes. Our acquisition of SmartRecruiters extends this approach to talent acquisition, helping connect hiring decisions to workforce planning, skills intelligence, and the broader employee lifecycle. Next month at at 麻豆原创 Connect, we’ll share new innovations and customer stories that further demonstrate how 麻豆原创 SuccessFactors can help organizations automate work, adapt more quickly to change, and drive better workforce outcomes, ultimately moving towards Autonomous HCM.

Creating measurable impact

Organizations around the world are already working towards this reality.

has embedded AI capabilities within 麻豆原创 SuccessFactors solutions to support employee development, goal setting, recruiting, and career conversations. By giving employees and managers access to AI-assisted tools and insights, Timken is simplifying HR processes, improving employee development conversations, and enabling more informed workforce decisions.

is demonstrating how AI can help organizations move from workforce insight to workforce action. With 麻豆原创 SuccessFactors solutions, the company has streamlined recruiting processes across more than a dozen industries, reducing recruitment duration by 75% and improving hiring efficiency fourfold. AI-generated job descriptions, competency-based interview questions, and workforce insights are helping create a more efficient, consistent, and skills-based approach to talent management.

These examples demonstrate an important shift. AI is no longer limited to providing information. It’s helping employees, managers, and HR teams make better decisions and take action more quickly and effectively.

Looking ahead

We are grateful to our customers whose continued trust and innovation make this recognition possible.

As we look ahead, our focus remains on helping organizations connect workforce insight with action, enabling leaders to make better decisions, respond more quickly to change, and create better outcomes for employees and the business. about our position in the 2026 Gartner庐 Magic Quadrant™ for HCM Suites for 1,000+ Employee Enterprises.

Gartner, Magic Quadrant for HCM Suites for 1,000+ Employee Enterprises, By , , , , , , 31 August 2026 
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner鈥檚 business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. 
Gartner and Magic Quadrant are trademarks of Gartner, Inc., and/or its affiliates. 

Last week, 麻豆原创 customers gathered at the Swiss Smart Factory in Biel, Switzerland, to experience firsthand how robots and 麻豆原创 software can work together to solve real business challenges.

Embodied AI refers to AI agents that interact with the world through a physical body鈥攅nabling machines to autonomously perceive, understand, reason, and act in real environments. By connecting these agents to and 麻豆原创 Business AI Platform, 麻豆原创 brings business context into that physical execution: robots that don’t just carry out tasks, but understand the business decisions those tasks serve.

Warehouse automation, asset inspection, and material handling are just some of the business scenarios where embodied AI is beginning to create value. Bringing those scenarios to life requires more than a robot. It requires business context from 麻豆原创 applications, integration expertise to connect systems and robots, and the right robots to execute the task.

鈥淕enerating market interest for embodied AI and transforming it from an exciting technology topic into a practical 麻豆原创-connected business value demanded a new format,鈥� explained 麻豆原创 Switzerland CTO Alexander Finger, who was a key driver behind the event.

Unlike traditional innovation jams, an embodied AI jam requires robots and a space where people can safely work with them side by side.

The Swiss Smart Factory provided exactly that environment for 麻豆原创 Switzerland to host the event. Bringing together customers, robot manufacturers, system integrators, and 麻豆原创’s embodied AI experts created a unique opportunity to move from discussion to hands-on experimentation and real-world use cases.

Viable use cases in days, not weeks

Embodied AI may well be all about hardware and software, but Finger says accelerating progress is ultimately about bringing people together. At the jam, customers and partners were paired with system integrators and robot manufacturers aligned to their business challenges.

While some teams explored how inspection drones could connect to solutions such as 麻豆原创 Asset Performance Management, others investigated how humanoids could support processes with 麻豆原创 Digital Manufacturing.

The result was a level of progress that typically takes weeks to achieve.

鈥淔inding where embodied AI creates real business value鈥攁nd shaping a solution to deliver it鈥攖ypically takes weeks of distributed back-and-forth,鈥� said Lukasz Ostrowski, head of the embodied AI initiative at 麻豆原创. 鈥淭hree days of dedicated, focused time with customers changed that. We could test ideas, challenge assumptions, and iterate in real time until we arrived at something concrete that neither side could have defined alone. What we learn with each customer like this doesn’t stay with that customer鈥攊t shapes how we build for the rest of the industry.鈥�

The physical dimension makes embodied AI tangible

For Finger, embodied AI only becomes meaningful when customers can experience it firsthand.

Seeing a robot perform tasks informed by business processes and objectives makes the potential business value far easier to understand than a slide deck or demo alone.

This is why the Swiss Smart Factory plays such an important role; it provides a safe environment where customers, robot manufacturers, system integrators, and 麻豆原创’s embodied AI experts can work and explore embodied AI in action together.

As of January 2027, 麻豆原创 Switzerland will become a member of Swiss Smart Factory, enabling it to host future embodied AI jams as well as shorter discovery formats like those already used for other AI customer-facing events.

While 麻豆原创 Customer Experience Labs show customers how 麻豆原创 applications, data, and AI can solve business challenges, the Swiss Smart Factory adds a physical dimension. It gives customers a hands-on environment to explore how robots can be connected, act in a business context, and create tangible business value.

Bringing embodied AI to more customers globally

鈥溌槎乖� is richer when we talk to customers,鈥� Finger concluded, reflecting on the success of the jam. Beyond the speed with which teams developed use cases and architectural proposals, one outcome stood out: customers left the event wanting to continue the conversation and further explore their embodied AI ambitions with 麻豆原创.

As 麻豆原创 Switzerland expands its offerings of embodied AI events, more customers will be able to experience embodied AI firsthand and explore how robots, 麻豆原创 applications, and business processes can work together to create value.

The top team鈥檚 rowers focused entirely on maximizing individual power. If the boat slowed, they rowed harder in isolation, disrupting the oars鈥� synchronized rhythm and creating water drag. Meanwhile, the junior rowers knew they were individually weaker, so they rowed in harmony.

Enterprises have faced countless variations of this problem: implementing systems that maximize productivity at the individual or team level but actively hinder the wider enterprise. Many organizations are experiencing something similar with AI today.

AI and sub-optimization

Sub-optimization is a systemic failure that occurs when the performance of a specific part of a system is maximized, inadvertently hampering the performance of the entire system. There are three intertwined themes: intensity, context, and prediction, which, taken together, explain how AI can sub-optimize an organization by making individuals and local systems stronger while straining the broader organization.

Research reinforces this disconnect between individual or even company-wide AI adoption and the value it delivers. shows near-universal enterprise AI adoption: 89% of respondents report regular AI use in at least one business function, but only 37% report an earnings before interest and taxes (EBIT) impact from AI at the enterprise level. Even worse: only six percent of companies can be categorized as high performers that already capture significant organization-wide value from AI.

What makes it so hard to move from AI adoption to measurable value capture? I believe there are three themes that influence a company鈥檚 ability to benefit its entire organization.

Intensity

AI tools often don鈥檛 reduce work; they intensify it. A found that employees who heavily use AI worked faster, took on a broader range of tasks, and worked longer hours, often without being asked. So, what appears to be higher productivity in the short run is actually silent workload creep and mounting pressure as employees manage new AI workflows and do more with less. that the most mentally taxing form of AI engagement was oversight; AI tools that require direct monitoring increased feelings of being overwhelmed by the volume of information at work.

It is easy to see why AI can feel intense: tasks that once required days can now be prompted into existence almost immediately. People start more things; they do more analysis and write more memos. However, like an eight-lane highway that suddenly narrows to a single-lane toll booth, individuals must still consume all this output. This bottleneck only compounds at the organizational level, as all employees produce more than ever, leaving both individuals and the organization as a whole struggling to keep up. Creation has scaled. Absorption has not.

The solution isn鈥檛 necessarily to use less AI, but to change where and how AI shows up. AI should understand user intent and surface the insights needed to answer the question, rather than generating static assets or requiring you to switch between different apps and systems.

If your question creates more things, it鈥檚 not helping absorption. 麻豆原创鈥檚 answer is , a central workspace across 麻豆原创 and non-麻豆原创 systems that uses AI agents to handle tasks.

Ask, 鈥淲hich stores run out of 65鈥慽nch TVs in the next 72 hours, and where is stock I can move?鈥� It will pull data across systems and orchestrate agents to act on the user鈥檚 behalf. In this case, 麻豆原创鈥檚 answer is autonomous action combined with a highly individual user experience for that specific situation, not more assets to be absorbed. If employees can avoid juggling systems and consuming assets, they can spend more time exercising judgment on actions that matter. This is how AI can alleviate intensity.

Context

Most AI systems understand the world, but not the enterprise in which they operate. There is a difference between a system of record鈥攖ransactions, master data, process logic鈥攁nd tacit knowledge鈥攅mails, chats, unwritten rules. And enterprises run on both. If AI only sees the system of record, its answers might be technically correct but contextually wrong because they don鈥檛 reflect the organization’s lived practice.

Even the most ostensibly basic questions require company context. Asking 鈥淲hich suppliers can I source coconuts from?鈥� requires knowledge of an organization鈥檚 process landscape across procurement, supply chain, compliance, finance, and other domains. This type of enterprise knowledge is usually scattered across process models, policies, chats, spreadsheets, and applications, so it鈥檚 tough to maintain. And even if they find it, agents cannot turn it into action without procedural knowledge of the involved people鈥攖he unwritten rules, decisions, and steps鈥攖hat make a process executable.

preview continuously captures institutional knowledge and makes it usable for both people and agents. It turns written inputs, chat inputs, process knowledge, policy guidance, and application logic into reusable building blocks that AI agents can consume. Blocks are captured once, governed centrally, and reused across the company. So when someone asks Joule Work about coconuts, the answer is driven by the company鈥檚 memory and reflects actual rules the process owners agreed upon鈥攆or example: “Only source from Brazil; others require formal exception approval.”

麻豆原创 Company Memory is not a one鈥憈ime implementation; it鈥檚 continuous. In this way, company knowledge behaves like infrastructure, ensuring agents act contextually, not just correctly, as policies and teams change.

Prediction

Business decisions are fundamentally prediction problems that rely on structured data. Most organizations use LLMs, which are great at unstructured data like text but for architectural reasons not so great at working with and generating the structured numerical data that underpins good predictions. Delay prediction, forecasting, anomaly detection, stock optimization, and credit risk are everyday operating questions that depend on structured, tabular data and forward-looking judgment.

Asking LLMs for reliable forecasts on enterprise tables is simply the wrong tool for the job. At the same time, traditional custom machine learning approaches are too slow for many real-time questions: after extracting data, sending it to specialists, and waiting weeks, the question often has changed by the time the answer comes back. This combination means predictive capabilities are either restricted to specialists or rendered inaccurate by generic LLMs; in either case, the organization鈥檚 decision-making is weakened.

Reliable forecasting and risk assessment should be a system property, not an individual hack. 麻豆原创-RPT-1.5 and TabPFN 3 are models that excel with tabular data and will integrate with Joule Work and 麻豆原创 Business Data Cloud for forward-looking questions directly on live tables.

麻豆原创-RPT-1.5 for 麻豆原创 data and TabPFN 3 for any tabular data are specialized prediction engines for structured data. They enable decision-makers working in the core systems to ask, 鈥淪hould I reroute volume? What鈥檚 the probability of on鈥憈ime delivery? What鈥檚 the cost delta across scenarios?鈥� and get answers grounded in real enterprise data.

Availability across the organization eliminates specialist bottlenecks and better equips the enterprise to handle uncertainty through prediction. This enables informed top-level decisions that really move the needle for a company.

AI for the benefit of the whole organization

AI has already proven it can make people more capable, but that does not automatically help the wider organization. AI shouldn鈥檛 be about optimizing isolated tasks; it should be about reshaping how work, knowledge, and decisions flow through the company.

Joule Work, 麻豆原创 Company Memory, and 麻豆原创-RPT-1.5/TabPFN 3 are great examples of how 麻豆原创 designs system-level capabilities. They offer a unified engagement layer, a living institutional memory, and a prediction engine for structured business data that elevate AI from individual-level hacks into a collective benefit for the enterprise.

This is AI that bridges the individual-to-institutional value gap, moving from simply getting AI into the company to generating value throughout the company.

Florian Kunzke is global director of AI Strategy at 麻豆原创.

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

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

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

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

Powering a global sporting goods business

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

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

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

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

One connected foundation for a connected customer experience

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

Built for the pace of sport

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

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

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

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

Alex de Minaur

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

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

For most enterprises, the integration environment they built over the last decade was never designed for what’s being asked of it now. Point-to-point connections, legacy middleware, fragmented tooling across dozens of systems: these were manageable constraints when the work was batch processing and scheduled dataflows.

Agentic AI changes the equation entirely. Autonomous agents need real-time access to data across the full application estate, consistent governance, and an integration layer that can scale without becoming a bottleneck. The organizations that have already modernized their integration foundation are finding that they have a meaningful head start.

麻豆原创 commissioned IDC to conduct an in-depth analysis of organizations using 麻豆原创 Integration Suite, including its advanced event mesh capability. 麻豆原创 Integration Suite is 麻豆原创’s flagship integration platform as a service, delivered on 麻豆原创 Business AI Platform, the unified foundation for 麻豆原创’s AI, data, and integration capabilities.

For this Business Value White Paper, IDC conducted in-depth interviews with eight organizations across manufacturing, consumer products, energy, fintech, healthcare, retail, and transportation 鈥� enterprises with an average of 48,529 employees and $14.53 billion in annual revenue, operating across the U.S., Germany, Denmark, India, and the UK. The results were quantified from actual outcomes, not a modeled composite.

What the IDC Business Value White Paper found: 麻豆原创 Integration Suite customers are achieving a 368% three-year return on investment with an eight-month payback, generating an average of $47,900 in annual benefits per integrated application, or $9.36 million per organization.

Numbers that matter

The IDC Business Value White Paper documents measurable impact across the full breadth of what integration touches in a modern enterprise:

• Integration speed and scale
  • 78% more application integrations
  • 41% less time to complete per application integration
  • 99% more application messages processed
• Operational reliability
  • 58% fewer unplanned outages
  • 35% faster to resolve integration errors
  • 19% fewer integration errors
• Business process automation
  • 49% more business processes automated
  • 21% efficiency gains for business process teams
  • 32% less time to onboard a business partner
• Developer and team productivity
  • 29% improvement in development team productivity
  • 31% faster development life cycle for new applications
  • 32% more efficient application integration teams

These aren’t projections. They are the average outcomes across eight real enterprises, documented through in-depth interviews by IDC analysts Shari Lava, group vice president for AI, Data, and Automation, and Matthew Marden, research vice president for Business Value Strategy.

More than an 麻豆原创 platform

One finding in this study deserves particular attention for customers and partners evaluating 麻豆原创 Integration Suite in mixed-vendor environments: on average, 64% of integrated applications in the study are non-麻豆原创, and 82% of integrations touch at least one non-麻豆原创 application.

麻豆原创 Integration Suite is not an 麻豆原创-only platform. It is the integration backbone for the full, heterogeneous application environment that modern enterprises actually operate, one where 麻豆原创 and non-麻豆原创 systems must work together reliably at scale.

One study participant described what that means in practice: “麻豆原创 Integration Suite is our main connection to the outside world. Every time anyone needs to connect to our 麻豆原创 systems, it goes through [麻豆原创] Integration Suite. It’s our main front door for API access, for data access, for collaborating with us, and for transferring data in and outbound into our 麻豆原创 systems.”

Built for the AI era

The timing of this study matters. Organizations are moving fast on agentic AI, deploying autonomous agents that need to orchestrate, monitor, and act across their entire application estate in real time. Integration is no longer a back-office concern; it is the operational layer on which AI-driven automation either succeeds or stalls.

As part of 麻豆原创 Business AI Platform, 麻豆原创 Integration Suite brings together API management, event-driven architecture through advanced event mesh, and AI-native capabilities 鈥� giving organizations the governance, observability, and real-time connectivity that agentic AI workloads require. That includes native support for MCP, LLM connectivity, and agent-to-agent orchestration, making it a ready foundation for autonomous enterprise processes.

Study participants described this directly. One said: “The real value of [麻豆原创] Integration Suite is the combination of its different capabilities: cloud platform integration, API management, and advanced event mesh. It’s the full set of these capabilities that makes it so important for us.”

Another captured the business case for reliability in AI-driven environments: “What we’re starting to see with 麻豆原创 Integration Suite is a level of reliability and standardization in the integrations we’re building, which enables positive levels of further scalability. We no longer have that kind of point-to-point integration, which had been a risk and a source of technical debt for us.”

Business case is clear

A 368% three-year ROI and eight-month payback is a compelling headline. But what sits behind those numbers is equally significant: enterprises can connect more of their application estate, automate more of their business processes, resolve failures faster, and onboard partners more quickly 鈥� all on a platform that is ready for the AI workloads they are investing in right now.

“We can innovate more with 麻豆原创 Integration Suite because it’s easier and the developers are happier working with the tool. It gives good results, so we continue to build on the foundations we have already created.”

That is the right foundation for what comes next.

.

Sid Misra is chief marketing officer of Technology Foundation for 麻豆原创 Business AI Platform at 麻豆原创.

Source: IDC Business Value White Paper, sponsored by 麻豆原创, The Business Value of 麻豆原创 Integration Suite (Doc #US54820926-BVWP, August 2026) , IDC Business Value Snapshot, sponsored by 麻豆原创, The Business Value of 麻豆原创 Integration Suite (Doc #US54820926-BVS, August 2026)

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. 

Across industries, businesses are already experimenting with AI to automate tasks, improve productivity, and create better customer experiences. The real question now is how do we move from experimentation to enterprise-wide transformation that delivers meaningful business outcomes?

At 麻豆原创, our answer is clear: the next wave of transformation will be driven by聽Industry AI. This belief is also reflected in the evolution of our own organization, as聽Customer Innovation Services evolves into Customer Industry Solutions. This is more than a name change; it represents an expanded mission and a recognition that the future of enterprise AI will be shaped by the combination of technology, deep industry expertise, engineering excellence, and customer-centric innovation.

The opportunity ahead is immense. Analysts estimate that generative AI alone could create between $2.6 trillion and $4.4 trillion in annual economic value globally, while global spending on AI is projected to exceed $630 billion by 2028. Yet, realizing this value will require enterprises to move beyond experimentation and deploy AI in ways that are deeply relevant to their industries.

For years, enterprises have pursued digital transformation through broad platforms and horizontal capabilities that could be applied across functions and sectors. AI has followed a similar trajectory. Large language models (LLMs) and general-purpose AI tools have demonstrated remarkable capabilities and unlocked entirely new possibilities. However, as organizations move beyond pilots and proofs of concept, one thing is becoming increasingly clear: generic AI can only take us so far.

鈥淚n the enterprise world, context is everything. The future of AI lies not in generic intelligence but in intelligence that understands industries, business processes, and how enterprises create value,鈥� said Dominik Metzger, Global Head of Industry AI. “This is where Customer Industry Solutions plays a pivotal role, bringing together deep industry expertise, customer insights, and engineering excellence to bridge the gap between innovation and real-world business impact.”

A manufacturer seeking to optimize its supply chain faces challenges that are fundamentally different from those of a retailer personalizing customer experiences. A bank navigating regulatory requirements operates in a vastly different environment than a life sciences company accelerating research and development. Every industry has its own processes, data models, regulations, and ways of creating value.

This is precisely why Industry AI represents the next frontier of enterprise transformation. The Industry AI portfolio combines the power of AI with deep domain expertise and business context. It understands not only language, but also the nuances of industries and the realities of how businesses operate. It can address industry-specific challenges and deliver outcomes that are measurable, scalable, and relevant to the enterprise.

Building on our strong foundation of customer co-innovation, the Customer Industry Solutions organization brings together deep industry expertise, customer insights, and engineering excellence to accelerate Industry AI at scale. Importantly, we are also bringing together the strengths of customer innovation and forward-deployed engineering.

This combination is powerful. Customer innovation teams bring a deep understanding of business challenges, industry processes, and customer outcomes. Forward-deployed engineering brings the ability to rapidly build, deploy, and operationalize solutions in complex enterprise environments. Together, these capabilities enable us to bridge the gap between breakthrough innovation and real-world business impact.

Our role is not simply to help customers adopt new technologies. It is to work alongside them to address complex business challenges, rapidly translate ideas into solutions, and help move organizations from AI experimentation to enterprise-wide transformation.

Industry AI also changes the way innovation itself happens. The most valuable insights often emerge from solving real customer challenges. They come from understanding pain points on the ground, identifying opportunities to simplify complexity, and applying AI in ways that create tangible business value. This requires closer collaboration among customers, industry experts, engineers, and product teams than ever before.

This is another critical role that the Customer Industry Solutions organization will play. By working closely with customers across industries and regions, and by systematically capturing insights from the field, we can help inform future product development and accelerate the adoption of industry-specific AI capabilities at scale.

Every customer engagement becomes an opportunity to learn, refine, and build solutions that can benefit entire industries.

Sindhu Gangadharan is head of Customer Industry Solutions at 麻豆原创.

Now, agentic AI could fundamentally change that equation, creating a different model for 麻豆原创 transformation: one built around faster delivery, lower costs, and greater customer self-sufficiency.

That was the central argument put forward during a recent examining what its participants described as 鈥渄eshoring,鈥� which uses AI agents to rethink work that organizations previously distributed between expensive onshore resources and lower-cost offshore teams.

For Stuart Browne, founder and CEO of , an independent 麻豆原创 consultancy that spent the past seven years helping companies chart their journeys from 麻豆原创 ECC to 麻豆原创 S/4HANA, the opportunity starts with questioning assumptions the industry has accumulated over decades.

鈥淭he only way we deliver this in the future is by changing the way we鈥檝e delivered it in the past,鈥� he said. 鈥淪o we鈥檝e got to find new ways of accelerating the migration, and I think AI is probably the best bet for that.鈥�

Why not faster, better, and cheaper?

The traditional technology transformation triangle says organizations might have projects that are faster, better, or cheaper, but generally only two of the three.

Browne challenged that premise by drawing an analogy to NASA鈥檚 鈥渇aster, better, cheaper鈥� approach to missions. He argued that 麻豆原创 transformations should similarly reconsider the assumption that improving one dimension requires sacrificing another.

鈥淲hy can鈥檛 you choose all three of these things?鈥� he asked.

That question becomes increasingly relevant as companies confront the remaining volume of 麻豆原创 S/4HANA migrations while simultaneously dealing with economic pressure, scarce 麻豆原创 skills, and the complexity of global transformation programs.

Ranjeet Panicker, senior vice president and head of Business Transformation at 麻豆原创, framed the challenge around the complete life cycle of an 麻豆原创 project, from initial discovery and analysis through design, build, and ultimately run.

At every stage, customers are asking the same questions: How can the project be completed faster? How can it cost less? How can my organization extract more value from the investment?

From offshoring to 鈥渄别蝉丑辞谤颈苍驳鈥�

For decades, mechanisms for reducing delivery costs have been moving work offshore. The economics were relatively straightforward as certain activities were moved to locations where labor costs are lower.

Browne argued that those savings can obscure another problem. While offshore resources may cost less, distributing work between offshore and onshore teams can increase the overall volume of work through handoffs, communication issues, rework, and coordination.

Agentic AI introduces another possibility. Instead of asking where human labor should be located, organizations ask whether some of that labor needs to be performed manually at all.

鈥淲hy can we not reduce the volume of work and reduce the cost of work?鈥� Browne asked.

That question led to the concept of deshoring鈥攔eplacing portions of location-based delivery with AI agents capable of performing or accelerating specific 麻豆原创 transformation activities.

After analyzing roughly 180 typical activities involved in an 麻豆原创 ECC to 麻豆原创 S/4HANA migration, Browne鈥檚 research concluded that AI could potentially produce about a 60% cost reduction by compressing effort and allowing tasks previously requiring highly experienced people to be performed by less experienced workers augmented by AI.

Some of the most attractive candidates are highly skilled activities that consume significant amounts of time, including writing functional and technical specifications, performing fit-gap analysis, and analyzing custom code.

鈥淚f your run rate is a million a month,鈥� Browne noted, 鈥渆liminating months from a program can dramatically change its economics.鈥�

The 80% solution with humans handling the last mile

AI may be capable of producing what Browne characterizes as an 80% solution within hours. It means experienced people review, challenge, and refine that work rather than spend time manually producing everything from scratch.

鈥淲hat AI produces shouldn鈥檛 be fully trusted without review,鈥� he said. None of this means eliminating experienced 麻豆原创 professionals. Instead, the emerging model redistributes where their expertise is applied.

The objective is not autonomous transformation. It accelerates the majority of the work while concentrating human expertise on the 鈥渇inal mile鈥� that includes judgment, validation, design decisions, and other activities where experience adds the most value.

Custom code could be an early breakthrough

One area where Browne believes AI is already producing significant change is custom code analysis. Early 麻豆原创 S/4HANA migrations often focused on getting existing customizations into the new environment and then determining how to remediate them. Agentic AI creates another option that determines whether the customization needs to exist at all.

According to Browne, AI can now analyze an entire custom code base, reverse engineer functional specifications, and determine whether the same business requirement can instead be met using standard 麻豆原创 functionality.

Rather than migrating large amounts of legacy customization and addressing it later, customers could potentially understand their custom-code landscape and identify opportunities for fit-to-standard before even selecting a systems integrator.

鈥淵ou can actually plan the fit-to-standard of your custom code before your SI’s have even been appointed,鈥� Browne said.

The implication is significant because customers have already paid for standard 麻豆原创 capabilities that may eliminate the need for some custom functionality while creating an environment that is simpler to maintain and upgrade.

麻豆原创 is building agents across the transformation life cycle

Panicker sees similar opportunities emerging across the broader 麻豆原创 implementation life cycle.

The terminology of onshore and offshore itself may eventually become less relevant, he argued, because organizations will increasingly think about transformation work in terms of skills rather than locations. AI-led skills can be delivered through assistants and agents and applied across different stages of a cloud transformation.

麻豆原创 is targeting areas including system analysis, data management, custom code, configuration, testing, rollout, and project management.

Panicker described an assistant as a collection of agents supporting a particular topic area.

A system-analysis capability can examine the overall transition. Data-management capabilities can address data quality. Custom-code agents can support analysis, recommendations, and, in some scenarios, automated remediation. Configuration assistants can evaluate current and target states, while testing agents can help automate test scripts.

According to Panicker, these capabilities are connected with 麻豆原创 Cloud ALM running on 麻豆原创 Business Technology Platform, along with a data and knowledge foundation designed to provide the customer-specific context agents need. 麻豆原创鈥檚 overall objective, he said, is to reduce transformation effort by approximately 35%.

Testing could become the next frontier

Testing can consume substantial amounts of time, particularly in industries with security, compliance, or validation requirements. AI raises a more complicated question: How much of that testing can organizations eventually delegate to agents?

鈥淚f I can get the code to get remediated by an agent,鈥� Panicker said, 鈥渃an I allow an agent to do the testing and accept the testing?鈥�

The answer will determine where humans remain directly involved in transformation workflows. Customers must decide not only if an agent can perform a task, but whether they have enough confidence in the agent鈥檚 knowledge, context, and guardrails to delegate responsibility for the outcome.

Context separates useful agents from hype

麻豆原创 programs generate enormous amounts of organization-specific information around architecture decisions, risk registers, test scripts, requirements, and other artifacts that evolve throughout a transformation.

Browne believes connecting AI to this continuously changing body of knowledge will be essential. 鈥淭he ability to converse not with a static LLM, but to converse with that world as well, I think is what will make good agents even better,鈥� he said.

Organizations may therefore need to rethink not only how they perform 麻豆原创 work, but how they capture information. Programs traditionally run across Excel, Word, PowerPoint, SharePoint, and other repositories may increasingly need AI-native knowledge environments capable of providing agents with usable business and system context.

鈥淭hat鈥檚 where I think this will be won or lost,鈥� Browne said.

AI could also change who holds the knowledge

Perhaps one of the biggest changes involves something less technical: who possesses expertise. 麻豆原创 implementations have traditionally depended heavily on experienced consultants and systems integrators. Customers frequently lack comparable knowledge, creating an imbalance that can make it difficult to challenge recommendations or independently evaluate major design decisions.

Browne believes someone with only six months of 麻豆原创 experience could potentially move up the knowledge curve in weeks in ways that previously might have taken years.

Panicker agreed that access to knowledge is becoming far less constrained.

His own experience at 麻豆原创 was initially concentrated in technical roles. Learning the business context surrounding that expertise often required finding someone willing to explain it. AI potentially allows professionals to move outside those traditional 鈥渟wim lanes.鈥�

Challenging the accepted 麻豆原创 timeline

麻豆原创 customers and consultants have grown accustomed to transformations, migrations, and upgrades taking a certain number of months or years. Those timelines have become assumptions embedded into planning.

Both Browne and Panicker believe those assumptions now deserve to be challenged.

鈥淚t鈥檚 about compressing the time that these tasks make and connecting the decision-makers to be able to make better decisions more quickly,鈥� Browne said.

Panicker similarly argued that organizations should stop accepting conventional project durations without asking what AI capabilities have been introduced to shorten them. 鈥淲hy can鈥檛 we do this sooner?鈥� he asked.

For Browne, the shift is already underway: 鈥淚鈥檓 not suggesting for one moment that we can press a button and deliver a whole program. Complexities around testing, change management, and design decisions remain.鈥� But he believes customers should stop treating agentic transformation as something waiting over the horizon. 鈥淭hat world is here now,鈥� he said.

Panicker鈥檚 message was equally straightforward: don鈥檛 wait on the sidelines: 鈥淟ean in, be curious about the technology. Ultimately, the sooner you can get to the outcome that you鈥檙e driving from a business standpoint, the more value you can bring.

The honest answer is that most GTM teams don’t have an AI problem. They have a decision-flow problem.

I’m seeing this across some of the world’s largest enterprises. And my clearest takeaway is that the organizations pulling ahead are not doing the same GTM motion but faster. They’re doing a fundamentally different one.

Here are five moments in the customer journey where that shift is already showing up in results.

1. Segmentation: From demographics to business signals

Most GTM organizations still segment the way they always have based on characteristics like industry, company size, geography, and more. AI changes the input. Rather than asking who a prospect is, it asks what they’re signaling right now. This includes more nuanced signals like operational pressure points, purchasing patterns, and growth indicators embedded in their business data. Reps aren’t chasing more leads; they’re chasing the right ones, and that shows up in how many of those signals turn into real opportunities.

2. Engagement: Relevance replaces volume

Cold outreach reply rates have dropped to near-historic lows across enterprise sales. The answer can’t be to send more outreach. It must be to do smarter outreach. When AI has access to full business context such as what’s happening inside an account operationally, financially, and commercially, it can help teams generate outreach grounded in what matters to a buyer at that moment. That’s not personalization at the persona level. It’s relevance at the account level. What shifts is the quality of first contact. Response rates can rise while the time it takes for the first qualified meeting shrinks because the outreach reflects what a buyer is dealing with in the moment.

3. Deal execution: Clearing the invisible friction

This is the one that most organizations underestimate. In most enterprise deals, the seller isn’t the bottleneck. The system around the seller is. Approvals, pricing sign offs, quote generation, and contract routing are where time is lost and deals slip. This isn鈥檛 because the buyer hesitates, but too often because of internal complexity. AI agents can help eliminate this friction.

For example, Amadeus, working with 麻豆原创, deployed an autonomous agent that reconciles unstructured payment data, clearing around 40,000 incorrect transactions that previously required manual intervention. That kind of autonomous resolution doesn’t just reduce cost, it changes what the buying experience feels like from the customer’s side. Deals that stalled for weeks waiting on internal processes don’t have to anymore. 

4. Post-sale: Compressing time-to-value

The handoff from sales to post-sale is historically where value gets lost. Expectations set during the sale don’t always match what a customer experiences in the first 90 days. AI makes that gap visible and actionable in real time through an 鈥渁ccount brain.鈥� This can be thought of as a growing repository of context and knowledge around an account, which makes handovers much easier and, most importantly, independent of any single individual. This can shift time-to-first value and 90-day adoption rate: how quickly a new customer reaches their first meaningful milestone, and whether they’re using what they bought.

5. Retention and expansion: Proactive at scale

Net revenue retention is the most durable commercial metric and it’s the one most dependent on what happens after the sale. The historical challenge is scale. AI can have a big impact here. Continuous scoring of expansion-readiness and churn risk, triggered by behavioral and operational signals, means teams act on the right accounts at the right moment not after a customer has already made up their mind.

Expansion of net revenue retention is where the largest commercial upside in most enterprise businesses lives. Both are chronically underserved when customer success is working reactively, account by account, rather than across the full base at once. The organizations getting this right haven’t simply deployed more AI tools. They’ve been deliberate about where in the customer journey AI can add real value for customers.

At 麻豆原创, we’re applying these same principles to our own GTM organization. We’re investing in a model where a single AI-powered entry point connects our sales teams to a network of specialized agents spanning planning, outreach, quoting, content, customer engagement, and more. The goal is a shared intelligence layer built around each account to give our teams more context and consistency so they can drive even more value and better outcomes for our customers at every stage of the customer journey.

So to me, the right question isn’t “Where can we implement AI?”; it’s “Where does customer value stall because information, authority, and action are separated?”

That kind of clarity is what a more intelligent go-to-market organization can already bring. And it鈥檚 only a first glance of what else will soon be possible.

Jan Gilg is global president of Customer Success & Americas and a member of the Extended Board of 麻豆原创 SE.

Returning humans to the Moon鈥攁nd building the foundation for an enduring presence in deep space鈥攔equires thousands of people, multiple government agencies, international partners, commercial providers, and highly complex systems to work together with extraordinary precision.

During the 麻豆原创 NOW event in Washington, D.C., Dr. Lori Glaze, associate administrator for NASA鈥檚 Human Spaceflight Mission Directorate, offered attendees an inside look at the Artemis program and the operational discipline behind it. I later joined Dr. Glaze for a conversation about managing complexity, sustaining momentum, and using emerging technology to support mission outcomes.

The discussion offered lessons that extend far beyond space exploration. For public sector organizations and enterprises undergoing their own transformations, NASA鈥檚 experience demonstrates how ambitious goals become achievable: one tested capability, one informed decision, and one coordinated team at a time.

Building momentum one mission at a time

NASA鈥檚 Artemis program is designed as a sequence in which every mission tests capabilities and generates knowledge for the next.

Artemis I, completed in 2022, successfully tested the Space Launch System rocket and the Orion spacecraft without a crew. Artemis II built on that foundation with the first crewed flight of the program, launching four astronauts in April 2026 for a nearly 10-day journey around the Moon before their safe return approximately nine days later.

During the mission, the crew tested Orion鈥檚 life-support and maneuvering systems, traveled farther from Earth than any humans before them, conducted scientific observations, and safely re-entered Earth鈥檚 atmosphere at nearly 24,000 miles per hour.

But the mission was not only about setting records. Every observation, test, and operational decision produced information that NASA can apply to what comes next.

鈥淓ach test flight in this program is going to inform the next steps of our mission,鈥� Glaze said.

NASA is now preparing for Artemis III, targeted for 2027. The mission will test critical rendezvous and docking capabilities between Orion and commercial human landing systems developed by Blue Origin and SpaceX. Those tests are intended to reduce risk before Artemis IV, currently targeted as the program鈥檚 first crewed lunar landing mission in 2028.

This incremental approach offers an important transformation principle: Meaningful progress does not require solving the entire future at once; it requires designing each milestone to validate assumptions, reduce risk, and create a stronger foundation for the next decision.

Standardization creates the capacity to accelerate

Speed is often associated with moving quickly. At NASA, it also means reducing unnecessary reinvention.

Glaze explained that one of the agency鈥檚 priorities is standardizing the architecture supporting future Artemis missions. Although exploration frequently involves building something that has never existed before, NASA also needs repeatable systems and processes that can support a more regular cadence of missions.

鈥淲e want to do this over and over again, so we need to standardize our architecture,鈥� she said. Standardization does not eliminate innovation. It creates the stable foundation upon which innovation can move faster.

NASA relies on an extraordinary range of technologies to support that complexity, including 麻豆原创 solutions. But technology alone does not make a mission like Artemis possible. Its value comes from how effectively it connects people, processes, information, and decisions around a shared objective.

This is equally relevant to organizations modernizing their finance, workforce, procurement, supply chain, and operational systems. When information and processes remain fragmented across different platforms, teams spend significant time reconciling data, navigating interfaces, and recreating decisions.

A connected digital backbone can reduce that friction. It provides a shared operational foundation so that organizations can scale proven processes, introduce new capabilities, and respond to change without rebuilding the enterprise each time.

Complexity demands faster, better-informed decisions

The scale of the Artemis program is difficult to overstate.

NASA must coordinate launch vehicles, spacecraft, landers, spacesuits, scientific instruments, communications, logistics, personnel, budgets, commercial contractors, and international partners. Each element has its own timeline, dependencies, and risks鈥攁nd all of them must ultimately come together at precisely the right moment.

Glaze said NASA is working to streamline decision-making by placing the right expertise closer to the work. Subject matter experts have been embedded with contractors and industry suppliers so that issues can be identified, evaluated, and resolved more quickly. The objective is to shorten the distance between insight and action.

That challenge is familiar across government. Leaders often have access to enormous amounts of information but lack a unified view of what is changing, where pressure is building, or which intervention will have the greatest impact.

Glaze identified this as one of the most promising applications for artificial intelligence: helping teams absorb large volumes of data, understand status across complex programs, and identify the areas that require attention.

For organizations, the opportunity is to move from systems that primarily document what has happened to systems that can help interpret conditions, anticipate risks, and support the next best action.

Autonomy works best when it expands human capability

NASA is already applying autonomous technology beyond administrative processes.

Robotic vehicles exploring the Moon and Mars can evaluate terrain, select safer routes, schedule scientific activities, manage communications, and avoid hazards with limited intervention from Earth. AI can also help researchers analyze the enormous scientific datasets generated by NASA missions and focus their attention on the most valuable discoveries.

These capabilities illustrate an important distinction: autonomy is not necessarily about removing people from the mission. It is about allowing technology to manage complexity at a scale and speed that enables people to make better decisions.

The same principle applies to the Autonomous Enterprise. Embedded AI can help coordinate routine processes, detect emerging issues, and recommend actions while keeping people responsible for judgment, accountability, and mission outcomes.

Partnerships turn ambition into capability

No single organization could accomplish the Artemis mission alone.

NASA鈥檚 architecture brings together government teams, commercial space companies, traditional aerospace manufacturers, international space agencies, scientific institutions, and military partners. Each contributes a specific capability to the larger mission.

For Artemis III, NASA is coordinating with Blue Origin and SpaceX on commercial landing systems. The Orion spacecraft includes a service module provided by the European Space Agency. Future lunar exploration plans also involve mobility systems, habitats, scientific instruments, and infrastructure developed through additional public-private and international partnerships.

This ecosystem is not adjacent to the mission; it鈥檚 how the mission gets done.

For public sector transformation, partnerships can provide specialized expertise and innovation that would be difficult for one organization to develop independently. But successful ecosystems require more than contracting. They require shared objectives, clearly defined responsibilities, trusted information, and mechanisms for making coordinated decisions.

Trust is the ultimate operating system

When I asked Glaze for her most important leadership advice, her answer was direct: surround yourself with smart people and trust them. 鈥淣o one person can do these things,鈥� she said. 鈥淭hey require thousands of people to achieve these amazing things.鈥�

Technology, architecture, and process all matter. But none of them can substitute for teams that understand the mission and are empowered to act.

NASA鈥檚 progress under Artemis demonstrates what becomes possible when a bold vision is supported by disciplined execution. The agency is testing before scaling, standardizing where it can, bringing expertise closer to decisions, using technology to expand human capability, and building an ecosystem around a clearly defined mission.

Whether the objective is returning to the Moon, modernizing a government agency, or transforming a global enterprise, the lesson is the same: the most ambitious outcomes are achieved when people, data, processes, and partners move forward together.

Jamison Braun is senior vice president and managing director for U.S. Public Services at 麻豆原创 America.

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:
Lawrie Benfield,聽lawrie.benfield@sap.com,聽+44 7776 515259, GMT
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麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
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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聽is聽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.

It鈥檚 about making consequential decisions inside complex processes that require a deep industry specific context of data, regulations, human workflows, and processes.

For example, tasks like deploying and coordinating thousands of field technicians to restore energy grids after a storm and reduce unplanned downtimes or like keeping critically important production lines running and service levels high despite global supply chain disruptions involve many difficult decisions.

“These problems are incredibly hard to solve,” says Dominik Metzger, president of Industry AI at 麻豆原创, because solving them demands immense organizational change, especially in highly regulated industries.

Frontier LLMs are not enough

There is a growing realization among business leaders that solving these challenges requires more than just a powerful AI language model. In these situations, AI must do more than generate suggestions. It needs to act on insights and context to support the execution of business processes. This requires agentic AI that can work with trusted business data, apply industry-specific knowledge, and take actions in ways that are reliable, explainable, and useful to the people closest to the work.

AI is most valuable when it understands the context in which decisions are made. Manufacturers need to balance demand, production capacity, supplier risk, and quality requirements. Energy companies need to manage assets, safety, sustainability, and regulatory obligations. Life sciences companies need to innovate while meeting strict compliance expectations.

To help solve the most complex problems facing large enterprise customers, 麻豆原创 is bringing together 50 years of deep industry聽expertise, leading AI engineering know-how, and customer-facing forward-deployed delivery capabilities in one organization聽to combine forward-deployed engineering with strong productization capabilities.

This enables 麻豆原创 to move beyond custom AI solutions and build, productize, and scale end-to-end AI transformations for industry-specific business problems.

Think of it this way: the Autonomous Enterprise is 麻豆原创鈥檚 strategic direction, 麻豆原创 Business AI Platform is its foundation, and Industry AI acts as a highly focused customer transformation offering, solving industry-specific challenges for individual customers to generate substantial business value.

What makes Industry AI different

The Industry AI portfolio is built on three differentiators: first, more than 50 years of 麻豆原创鈥檚 industry and process expertise across 26 industries; second, the richness of 麻豆原创 customers’ data footprint and ontologies in an existing system of record; and third, a dedicated forward-deployed engineering (FDE) workforce to solve problems that do not have off-the-shelf answers, customer by customer.

Forward-deployed engineering embeds AI specialists, such as data scientists and AI builders, directly with customers to solve high-value, industry-specific problems, rather than relying solely on packaged software. Metzger explains that forward-deployed engineering involves 鈥済etting obsessed with the problems of our customers鈥� and immersing 麻豆原创鈥檚 agentic AI developers in the challenges these customers face. 鈥淲orking directly with customers, we will build, deploy, and scale Industry AI applications to deliver tangible business value for our customers,鈥� he says.

Lessons learned by building these tailored solutions with selected customers will be productized as a standardized platform offering for many more customers to deploy and use. This model allows fast scaling and delivery, while also building up 麻豆原创鈥檚 platform and solution portfolio of high-value agentic solutions that are close to customer needs and current industry priorities.

Getting up close with customers

麻豆原创鈥檚 decision to establish Industry AI as a focused business offering reflects the conviction that true value from agentic AI is created in close collaboration with the industry experts who face specific business challenges every day. This proximity is the fastest way to identify the most critical problems, develop and test practical AI solutions, and refine them based on real-world experience. Most importantly, it allows to deliver tangible business value and prove the impact of AI in practice.

This approach helps reduce the gap between a promising idea and a solution. On the one hand, teams can move more quickly from identifying a need to deploying a solution that delivers measurable value. It also ensures that what is built reflects real-world needs rather than assumptions made far from the customer environment.

For 麻豆原创鈥檚 enterprise customers, the promise of the Industry AI offering is not simply smarter software. It is a more practical path to the Autonomous Enterprise. Instead of asking teams to adapt to generic tools, Industry AI can help bring deep intelligence into the processes people already use and the decisions they already make, while staying connected to the business data, controls, and applications that keep organizations running.

Benefits

Examples of these benefits are easy to describe. An energy provider avoids costly downtime by identifying a likely spare part demand early and recommending relevant suppliers. A retailer adjusts inventory positions and trade promotions based on simulated scenarios and real demand signals before stockouts occur. A pharmaceutical manufacturer ensures the safe and reliable release of life-saving drugs through a highly precise, high-quality, and fully automated batch release process.

That matters because it can help organizations move faster, improve quality, and free employees to focus on higher-value work. It can also help companies turn industry knowledge into a lasting advantage, especially as AI becomes a larger part of how businesses operate.

For 麻豆原创, the formation of the Industry AI unit represents a strategic leap, combining industry-specific expertise, end-to-end offerings, and a value-based offering in a way that is clearly designed to differentiate 麻豆原创 from competitors.

麻豆原创鈥檚 differentiation from other AI platform providers and more traditional forward-deployed engineering companies is not simply about providing custom AI services: the focus is on turning industry-specific expertise into scalable, repeatable offerings that can be deployed across customers, creating a more sustainable and differentiated model for delivering AI value.

Specifically, the Industry AI offering is positioned as an all-in-one commercial package, including platform consumption and cloud services, solutions, forward-deployed engineering, and expert support, with pricing based on real customer business value and a single contract.

It is based on 麻豆原创鈥檚 unique deep industry-specific knowledge, business AI platform, and knowledge graph, enabling tailored processes for customers. In addition, 麻豆原创鈥檚 approach is grounded in customers鈥� business logic, decades of experience and enterprise grade governance, all implemented in standard products, differentiating it from some recently announced market offerings.

What comes next

As the World Economic Forum has highlighted, successful AI scaling depends not only on the technology itself, but also on practical changes to how people work, how decisions are made, and how organizations govern new capabilities.

麻豆原创 solves industry problems that generic AI鈥攅ven if super powerful鈥攃annot. Ultimately it is about enterprise and business process transformation, not AI deployment only. This then can redefine how AI transforms industries, by moving beyond isolated use cases toward autonomous, end-to-end agentic execution.

Supply chain leaders today operate in an environment where disruptions are constantly changing, customer expectations shift quickly, and the window for effective response keeps shrinking. Organizations need more than digitalization. They need platforms that connect people, processes, data, and business networks鈥攁nd that turn that connectivity into faster, better decisions.

Our vision: Autonomous Supply Chain Management

The next frontier in supply chain is not just connectivity, it is decision intelligence. At 麻豆原创, represents our vision for how organizations will run their businesses in the future, with data, business context, policy, and AI reasoning working together so that the right decisions happen faster, more consistently, and at scale, while human judgment remains central.

is a practical step toward that vision, where people define goals and priorities, AI assistants orchestrate activity across domains, and agents execute the work within governed, end-to-end processes. Enterprises rely on fully autonomous agents to run supply chain processes and decisions within guardrails defined by the organization. Reaction times to disruptions compress from days to minutes. Data collection across organizational silos happens in near real time. Rather than manually collecting and interpreting data, planners receive auto-generated scenarios that are pre-validated and ready for a decision. That speed advantage is transformative.

But the value goes beyond speed. Organizations learn from past decisions and harmonize decision-making across the enterprise, leading to consistently better outcomes over time. AI delivers the greatest value when it is embedded where work actually happens, grounded in deeply integrated processes and trusted data.

Our approach

A truly integrated supply chain platform does more than connect data鈥攊t connects decisions. That is the principle that guides how we have built the , and it is what we believe sets 麻豆原创 apart.

Our breadth across execution, transactions, business networks, finance, and AI is unique. No other vendor brings together operational systems, trusted business data, and intelligence and orchestration in a single, deeply integrated suite. That breadth matters because supply chain decisions do not happen in isolation鈥攁 sourcing decision affects manufacturing capacity, which affects logistics, which affects financial commitments. When those systems are connected, decisions improve across the board.

We organize our platform around three layers. The first is operational systems, including applications such as , , , , , , and . These are where supply chain work happens. The second is trusted business data, anchored by , which aggregates data from 麻豆原创 and third-party sources to help give organizations a unified, real-time view across the enterprise. The third is intelligence and orchestration, delivered through , our AI engagement layer, which brings together agentic AI, embedded analytics, and network-enabled execution to help organizations move from reactive problem-solving to proactive decision-making.

This architecture helps organizations standardize processes across domains, reduce dependence on fragmented point solutions, and improve coordination from sourcing through final delivery鈥攍inking operational, financial, and network outcomes in real time.

Putting innovation into practice

We see demand for capabilities that bring together operational data, business context, and AI-powered insights, and we are actively investing to meet that need. New Joule Assistants are being embedded across planning, manufacturing, logistics, asset management, and supplier collaboration, helping teams act on changes faster and reduce time spent on manual coordination.

Alongside these assistants, we are delivering purpose-built AI agents across supply chain processes, designed to take guided action within defined business guardrails while keeping people firmly in control. These capabilities are becoming available in phases through 2026, aligning with customers鈥� existing 麻豆原创 landscapes.

Delivering real results for customers

Ultimately, in our opinion, recognition like this is only meaningful when it reflects real impact for the organizations we serve. Takeda Pharmaceuticals International AG, a global leader in R&D-based biopharmaceuticals, offers a concrete example of what this looks like in practice. Using 麻豆原创鈥檚 supply chain planning assistant, Takeda has moved from manual root-cause analysis to AI-detected diagnostics and AI-suggested remediation, with automated performance tracking that enables a self-healing supply chain capability.

鈥淎I represents the key enabler for Takeda鈥檚 transformation for the future,鈥� said Rebecca Kaufmann, senior vice president and head of Enterprise Platforms at Takeda Pharmaceuticals. 鈥淲e are combining the 麻豆原创 industry and technology knowledge with our organizational real-life experience. The supply chain planning assistant will provide us with AI-detected root causes and AI-suggested remediation and also automated performance tracking resulting in a self-healing capability.鈥�

Looking ahead

Supply chains don鈥檛 become autonomous overnight. This evolution happens workflow by workflow, expanding automation where it delivers real value, while keeping people firmly in control. Supply chain teams are increasingly looking to spend less time monitoring and firefighting and more time shaping decisions, managing trade-offs, and building resilience.

We are grateful to the many customers, partners, analysts, and 麻豆原创 teams whose collaboration and insights help shape our products and strategy. While we are honored by this recognition, we view it as an important milestone for us rather than a destination. We remain committed to ongoing innovation that helps organizations transform their supply chains into strategic sources of competitive advantage鈥攁nd we thank our customers for their trust in that journey.

Devesh Mishra is GM and chief product officer for 麻豆原创 Supply Chain Management.

*Gartner, Magic Quadrant for Supply Chain Management Suites, Jan Snoeckx, Balaji Abbabatulla, Christian Titze, August 11, 2026.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner鈥檚 business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

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

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

Start with the outcome, not the technology

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

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

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

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

Build a connected data foundation

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

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

Apply human oversight where it matters most

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

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

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

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

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

Measure value and risk together

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

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

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

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

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

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聽as聽an early adopter and leader in digital transformation, was approached by 麻豆原创聽AppHaus聽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聽appropriate connectors聽and integrating with the company鈥檚 麻豆原创 and AP systems using . This enterprise AI foundation聽can bring together capabilities and technologies (think 麻豆原创聽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聽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.

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Salling Group, northern Europe鈥檚 largest retailer, shares how a modern 麻豆原创 landscape is helping support more than 2,100 stores across six countries

Learn how 麻豆原创 S/4HANA Cloud, RISE with 麻豆原创, and a strong supply chain foundation are helping the company improve efficiency, support employees, and better serve customers. Read the article.