For , Race to World First is a test of endurance as much as execution. The competition can require teams to compete continuously for weeks against some of the world鈥檚 most complex raid encounters, where fatigue, stress, recovery and coordination can influence performance while coaches still need to make decisions in real time.

That challenge has traditionally been difficult to solve because critical information often sits in separate systems. Gameplay metrics, combat logs, wearable-device signals, player wellness information and operational data can each provide insight. However, when analyzed in isolation, they leave coaches with an incomplete view of how player condition and in-game execution affect one another.

The solution helps Team Liquid bring those signals into a unified, AI-enriched analytics environment. Coaches, analysts and performance experts can view physical readiness, stress indicators, recovery metrics, scheduling context and in-game performance together, helping them identify patterns that are hard to detect across disconnected tools and spreadsheets.

The solution extends the connected data foundation with AI-powered recommendations. By analyzing trends across gameplay, wellness and biometric information, Joule can help coaches surface early warning signs and compare tactics against historical performance patterns. With Joule, coaches can also evaluate whether recovery breaks, role adjustments or strategy changes may help the team sustain precision during progression attempts.

鈥淎s we prepare for Race to World First, having player performance, health and game data in one place changes how we approach coaching and strategy,鈥� said Dr. Jesse Hart, Senior Director of Sports Science and Analytics, Team Liquid. 鈥溌槎乖� Business Data Cloud allows us to make decisions backed by data rather than intuition during one of the most demanding competitive periods of the year.鈥�

麻豆原创 and Team Liquid have collaborated since 2018 on esports performance, analytics and AI innovation, including solutions designed to support competitive preparation and real-time decision-making. 麻豆原创 Business Data Cloud marks the next evolution of that work by showing how connected data and AI can help teams turn fragmented information into action under pressure. The same challenge exists beyond esports: organizations across industries need trusted data, intelligent insights and human expertise working together so people can perform at their best when it matters most.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Martin Gwisdalla, +49 6227 7-67275, martin.gwisdalla@sap.com, CET
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 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.

Moving to 麻豆原创 Cloud ERP Private as part of the RISE with 麻豆原创 journey, the company will consolidate its enterprise resource planning (ERP), business process intelligence, and service capabilities in a unified cloud-based subscription model. This creates the foundation to support further growth, optimize processes and integrate new technologies.

鈥淢oving to 麻豆原创 Cloud ERP Private is a strategic decision that goes well beyond IT infrastructure. With RISE with 麻豆原创, we are creating the foundation to run HARTING as a more agile, data-driven business鈥攕tandardizing our global operations, accelerating the integration of new technologies and unlocking the potential of AI across our processes. This contract marks a pivotal step in how we intend to grow and compete over the next decade,鈥� said Philip Harting, CEO, HARTING Technology Group.

麻豆原创 Cloud ERP Private will enable HARTING to transition to a future-proof, scalable and innovation-driven ERP environment. Cloud transformation will allow HARTING to consistently automate and standardize operations and infrastructure, direct IT resources toward value-adding topics and innovation, and continuously benefit from updates and new functionalities. At the same time, RISE with 麻豆原创 opens additional potential in the area of artificial intelligence and data-driven business models, with integrated AI functionalities and new automation options enabling faster, more substantiated data-based decisions. As a long-standing consulting partner, NTT DATA Business Solutions AG will accompany HARTING on its transformation path.

鈥淚ndustrial companies are under real pressure to move faster, operate leaner and integrate AI into their core processes as a present-day advantage. HARTING’s move to 麻豆原创 Cloud ERP Private with RISE with 麻豆原创 reflects exactly the kind of decisive, long-term thinking that separates technology leaders from those who wait, and positions them to scale efficiently and respond to market change with confidence,鈥� said Dirk Haeussermann, Managing Director, 麻豆原创 Germany.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Mariana Guerrero, +1 786 255 6334, mariana.guerrero@sap.com, ET
麻豆原创 麻豆原创 Room; press@sap.com

Top image courtesy HARTING Technology Group

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

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.

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.

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.

The company鈥檚 history goes back more than 100 years, and what began as a small textile shop in Aarhus, Denmark, is now an international retailer with 鈧�12 billion in revenue.

麻豆原创 has supported Salling Group for over 20 years and is central to its operations, said Alan Jensen, CIO and executive vice president at Salling Group. Recently, the company has modernized its ERP system to 麻豆原创 S/4HANA Cloud via RISE with 麻豆原创.

With this cloud-based infrastructure in place, the retailer is ready to begin its AI transformation.

Improving everyday life

Salling Group鈥檚 reason for introducing AI is threefold: improve customer experience, simplify for employees, and boost operational efficiency. 鈥淲e want to make everyday life better for our customers by having the right product for the right price every time they need it,鈥� Jensen said. 鈥淲e also want to make every day better for our employees, so the tools and systems they work with are intuitive and easy to use.鈥� This aligns with the company鈥檚 purpose to improve everyday life for customers, colleagues, and the communities it is a part of.

The company views AI as a key enabler, focusing on how to turn AI into real business value for customers, employees, and the company overall. One such area where AI will have real impact on the retailer is logistics, Jensen said. Currently, Salling Group uses in its 29 distribution centers. The application helps manage high volumes of goods and run sustainable, risk-resilient operations via digitalized warehouse processes in the cloud. For Salling Group, this means on-time delivery to stores and efficient supply chain operations.

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What鈥檚 next

Salling Group is using 麻豆原创 solutions and AI to modernize its retail operations end-to-end, with a clear emphasis on customer experience, employee productivity, and supply chain excellence鈥攚hile ensuring new technologies deliver tangible business outcomes.

When it comes to 麻豆原创, Jensen is looking forward to seeing how the Autonomous Enterprise will further Salling Group鈥檚 success with AI.

鈥淭he Autonomous Enterprise looks very exciting,鈥� Jensen said. 鈥淭here is definitely a lot in how we can improve the way we run our business every day, so we need to be curious and see how we can use it.鈥�

鈥淲e started by fixing the basics,鈥� said Siani, Transformation and Technology director at Ipiranga, one of Brazil鈥檚 largest fuel distributors.

Ipiranga operates almost 6,000 service stations nationwide. Headquartered in Rio de Janeiro, the company has more than 6,200 B2B customers and 1,500 convenience stores. Ipiranga generates $120.7 billion in revenue, yet despite its size and market leadership, the company faced a critical problem: repeated project failures.

Working on the root cause

鈥淏efore we could start investing in new technology, we needed to take a good look at why some of our initiatives were not generating the expected value for the business,鈥� Siani said, speaking at the TAC Insights conference for in Toulouse. 鈥淲e needed a rigorous RCA.鈥�

RCA, or root cause analysis, is a structured approach to problem-solving that focuses on identifying the underlying causes of issues rather than just addressing surface-level symptoms.

鈥淚nstead of making new investments, we decided to dig deeper and identify the causes,鈥� she explained. 鈥淲hen we examined the company鈥檚 history, RCA confirmed that the high level of customization was limiting our ability to deliver at the required speed. Our inability to adopt the best technologies and functional best practices was directly impacting the company鈥檚 ability to evolve and drive business growth.鈥�

Speaking the language

From the outset, the Technology team recognized that the case for the ERP transformation rested on demonstrating how inconsistent processes and the lack of standardized best practices hindered the organization’s ability to respond quickly and remain competitive in the market.

鈥淲hen we went to the Board to secure the budget for the digital transformation, we didn鈥檛 start by talking about technology,鈥� Sian sharedi. 鈥淲e started by discussing what the business needed to achieve its strategic objectives faster. Then, we listed the obstacles preventing that progress along with the technology capabilities required to remove those barriers.鈥�

By translating technical challenges and opportunities into business language, Siani helped Ipiranga鈥檚 leadership understand that innovation without a strong foundation would not take the business to the level of efficiency required.

鈥淏y identifying these root causes, we were able to avoid a common trap,鈥� she explained. 鈥淚nstead of investing in new technology without fixing the foundation, we shifted our strategy. We refocused on聽 tools to process integration, governance, and operational discipline.鈥�

Getting approval for a big investment

Ipiranga partnered with 麻豆原创 to assess critical processes across operations, finance, and commercial operations.

“麻豆原创 brought in business experts to pinpoint how a heavily customized ERP system was slowing decision-making and limiting visibility and innovation,” Siani said. Rather than focusing on 麻豆原创 functionalities, they asked fundamental business questions: What does your financial process look like? How does your order-to-cash process work?”

Armed with these insights and a clear understanding of what was slowing decision-making, Siani returned to the board with a clear message: “Fix the foundation, or transformation will fail.”

As a result, she got approval to implement a new ERP system, driven by business value and ROI. What ensued was a massive integration effort, with a targeted go-live scheduled for December 31, 2026.

With support from Accenture and 麻豆原创, Ipiranga adopted a clean core strategy, ensuring minimal customization and long-term scalability. Overall, 104 legacy systems were analyzed, 64 systems will be integrated and 40 decommissioned, and over 750 interfaces are being built. Of course, all developments had to pass strict governance gates to guarantee the clean core.

People driving change

Technology may enable transformation, but people make it successful. Over 350 professionals from a variety of business and technical teams are involved in the ongoing project. Crucially, leadership played a direct role. With executive sponsorship and transparent communication, resistance to change has been minimal.

鈥淲e were careful not to impose change, but to explain the impacts clearly and discuss them with a multidisciplinary team,鈥� said the IT expert, who is also a rowing champion. 鈥淲e made sure to prepare teams early and embed change management at every phase.鈥�

The team prioritized initiatives with the highest return on investment (ROI) and paused non-essential projects. They also selected world-class partners鈥攊ncluding Accenture as the implementation partner and Amazon Web Services (AWS) as the hyperscaler鈥攖o help ensure a high-quality, successful transformation.

The company鈥檚 transformation is now in the middle of a critical milestone (SIT1). The expected ROI is over $40 million, but more importantly, the company has built something far more valuable than a new system.

鈥淲e will have a solid operational foundation, integrated, scalable ,and efficient processes, and a culture that understands how transformation goes beyond technology. Technology is simply the path,鈥� Siani said.

Ipiranga鈥檚 journey offers a powerful reminder that digital transformation is not about tools; it鈥檚 about assertive fundamentals. RCA can reveal issues technology alone cannot fix. It demonstrates that preparation is as important as execution, and that clean core strategies reduce long-term complexity.

“In effect, the path to successful transformation doesn鈥檛 start with innovation. It starts with clarity, discipline, clear goals that together determine the correct technology,鈥� Siani concluded.

Managing logistics operations has become increasingly challenging in recent years. The coronavirus pandemic and its repercussions caused unprecedented disruption across global supply chains, and today, hostilities in the Strait of Hormuz are severely hindering shipping traffic.

In situations like these, the only option many companies have to protect their manufacturing and delivery timelines from delays and uncertainties is to carry larger inventories. This inevitably leads to higher costs.

Now, a combination of container tracking technology and real-time data is revolutionizing supply chain visibility, enabling businesses to keep their inventories at efficient levels. Hapag-Lloyd, the world’s fifth-largest shipping company, provides live position data for its containers. 麻豆原创 uses this information in its ERP systems and 麻豆原创 Business Network Global Track and Trace cloud solution, connecting it to the shipment and material data that is stored there. Which means that, as well as being an 麻豆原创 customer, Hapag-Lloyd is an 麻豆原创 supplier and partner, too.

Karsten Schmidt, director and product owner of the Live Position and Track & Trace tools at Hapag-Lloyd, and Sven York Pohl, chief expert for Digital Adoption at 麻豆原创 SE, discuss how the collaboration between the companies began, how it is benefiting users already, and what opportunities lie ahead.

Q: Tracking technology has advanced rapidly in recent years. What sparked the wave of innovation we鈥檝e seen here?

Pohl: During the coronavirus pandemic, we experienced serious delays in the movement of goods and materials. And we realized that logistics processes need to be much more transparent.

Schmidt: Until recently, we could only track certain milestone events in sea shipping, such as a container arriving at a port or being loaded onto a ship. We knew when a container had been loaded onto a ship or when it left the Port of Hamburg, but if it failed to arrive in Munich as planned, we had no way of knowing what had happened to it.

Q: So how do you track containers today?

Schmidt: After the pandemic, we decided to equip all our dry containers with IoT tracking devices that continuously transmit location data. By mid-2024, we had fitted these devices to 90% of our container fleet. Since then, we have been able to track more than 2 million containers worldwide and provide position data every 15 minutes for shipments on land鈥攁nd every six hours for shipments by sea. This means that our customers can check the precise location of 鈥渢heir鈥� containers online in real time.

Q: How did the collaboration between Hapag-Lloyd and 麻豆原创 begin?

Pohl: I was involved in an 麻豆原创 transformation project at Hapag-Lloyd, which meant I had frequent meetings with Karsten. We realized early on that position data had immense potential, both for Hapag-Lloyd as a shipping company and for its customers. If you know exactly when an intermediate product will arrive, or how many days late it will be, you can plan your downstream production steps with pinpoint precision. And this obviously works best if position data is visible not only on a separate website, but also in the applications you use to manage your production and other business processes. So Karsten and I began discussing how we could feed position data into 麻豆原创 ERP systems.

Q: Which 麻豆原创 applications use this data?

Pohl: The 麻豆原创 Business Network Global Track and Trace cloud solution offers companies transparent shipment information in real time along the entire supply chain. And it enables this data to be embedded natively in back-end systems. Which means, for example, that you can integrate automated alerts directly into 麻豆原创 S/4HANA that show where a container is currently located. The data can also be leveraged in 麻豆原创鈥檚 cloud-based supply chain collaboration platform, 麻豆原创 Business Network for Logistics, to seamlessly connect shippers with logistics services providers and carriers. And, aside from ensuring visibility, real-time data such as that provided by Hapag-Lloyd maximizes supply chain security and drives sustainability by enabling businesses to track materials from their source to the finished product.

Q: Who benefits most from Hapag-Lloyd’s tracking data?

Schmidt: Logistics managers, definitely. Because they need precise information about where their containers are located and when they will arrive. The tracking data also provides insights into how sustainable a transportation chain is. Warehouse planners benefit too, because they can keep inventory levels as low as possible, without them dropping too low. And as well as enabling carriers to plan their routes with maximum efficiency, tracking data can also help streamline and optimize payment processes.

Q: Would you say that the current conflict in the Middle East has highlighted the value鈥攁nd necessity鈥攐f using tracking technology to plan shipping routes?

Schmidt: Absolutely. A huge number of containers have been transported from the Persian Gulf across Saudi Arabia to the Red Sea port of Jeddah to be shipped on from there. Our container tracking solution keeps me constantly updated on the land route I have chosen and on where my container is currently located.

Q: What happens if the container is held up somewhere? How does the system help?

Schmidt: That鈥檚 what we鈥檙e working on right now. Usually, when customers want to know where their container is, they call our customer service number. But with 15,000 containers held up in the Persian Gulf, we were unable to handle the sheer volume of inquiries we received by phone. So we gave all our customers access to our live positioning service, even those who had not booked it. The solution we implemented to deal with this exceptional situation showed us the potential that was there going forward to simplify our customers鈥� processes鈥攁nd our own.

Pohl: Looking ahead, 麻豆原创 users will be able to ask their software what options they have when shipments get held up, and what alternative modes of transportation are available to them.

Schmidt: If, for example, a ship is delayed and misses its scheduled rail connection, the system will provide a predictive service, telling the user whether onward transportation by truck is possible and how much longer the journey will then take.

Pohl: We will also use Joule, the flagship AI brand that is built into 麻豆原创鈥檚 cloud portfolio, for this service, so that users will be able to ask AI to suggest specific actions.

Schmidt: Obviously, when goods are transported over long distances, and especially by sea, you can never rule out delays entirely. But what we can do is remove that feeling of helplessness and of having no room to maneuver when events occur that are out of my control.

Top image via Hapag-Lloyd

Those investments are critical. But they weren鈥檛 the finish line. They were the foundation. And today, they鈥檙e paying off in new ways as organizations adopt AI to drive measurable business outcomes.

But there鈥檚 another part of that foundation which is becoming just as important: data. The organizations best positioned to compound their value from AI are those that have invested in data foundations that give AI the trusted business context it needs to reason, recommend, and act.

Now, in conversations with customers across the Americas and around the world, I鈥檓 seeing a new pattern emerge. The discussion is shifting from where AI can be applied to what happens when intelligence becomes embedded into the core of how the business operates.

While every transformation is different, three common shifts keep coming up.

1. From AI use cases to intelligent business processes

The first wave of AI adoption focused on proving value. Organizations identified high-impact use cases, delivered measurable results, and built confidence that AI could make a difference.

That work isn鈥檛 finished. But increasingly, customers are asking how intelligence can become part of the business processes employees use every day. We鈥檙e already beginning to see what this looks like in practice.

For example, HR Path Brazil, a Brazilian company specializing in recruiting and managing talent for international firms, is using Joule embedded in 麻豆原创 SuccessFactors HCM to automate routine HR interactions. It is helping employees find the information they need faster while allowing HR teams to focus on more strategic work. The company has reported a seven percent reduction in standard HR support cases and two hours of聽HR support workload eliminated each week, which quickly adds up.聽It鈥檚 one example of how embedded and connected AI is becoming part of how work gets done.

2. From measuring AI use to measuring the business outcomes it creates

One of the biggest changes I鈥檓 seeing is how organizations define success. ROI is becoming a given and that is reflected in the data, especially in Oxford Economics research out just this week. It showed that organizations investing in AI expect to see an average return of 21% this year but increasing to 38% in two years. And as agentic AI continues scaling, it is projected to deliver $17.6 million in returns, more than quadrupling last year鈥檚 estimates (US$4.3 million).

This is allowing organizations to focus more on business outcomes from their AI. They are asking questions like can we shorten cycle times? Can we improve decision-making? Can we free employees to spend more time creating value? Can we become more resilient and responsive as a business?

This is an important shift because it changes the conversation from implementing technology to improving how the business performs.

3. From systems of record to the new operating system for the enterprise

The third shift is the one I believe will have the greatest long-term impact. For decades, enterprise software primarily captured transactions, standardized processes, and automated routine work. Now it鈥檚 beginning to help organizations anticipate change, recommend actions, coordinate work across functions, and increasingly execute routine decisions with human oversight.

That鈥檚 why I believe the Autonomous Enterprise represents more than the next phase of automation. It represents a new operating model for business.

Instead of people spending time connecting information across finance, supply chain, procurement, HR, and customer operations before deciding what to do next, intelligent systems can increasingly provide context, surface recommendations, orchestrate work, and help teams execute.

People remain firmly in control. But they鈥檙e supported by enterprise software that is becoming an active participant in how the business operates and executes, not simply a system that records what already happened.

Where we go from here

The organizations creating the greatest long-term advantage won鈥檛 just be the ones deploying the largest number of AI use cases. They鈥檒l be the ones that use those early successes to rethink how work gets done across the enterprise.

The journey to the Autonomous Enterprise won鈥檛 happen overnight, and it won鈥檛 replace the need for strong leadership, governance, or talented people. If anything, those become even more important.

We鈥檒l likely look back on today鈥檚 AI projects much the same way we now look back on the early days of cloud transformation; not as the destination, but as the foundation for a fundamentally new way of operating and innovating continuously.

The true winners from this shift will be the ones who continuously become more intelligent, more adaptive, and ultimately more autonomous.

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

Anyone who is looking for a prime example of how migration to the cloud can do far more than just simplification and standardization should take a closer look at Swarovski. The legendary manufacturer of precision-cut crystals, jewelry, and watches鈥攚ith its origins in Wattens, in the Austrian region of Tyrol鈥攈as transformed its IT landscape from a cost factor into a strategic tool for a digital future.

The transformation was guided by Lea Sonderegger, serving in a dual role as CDO and CIO at Swarovski, with such great success that she was awarded the special 鈥淐loud Excellence鈥� prize in the large enterprise category at the CIO of the Year ceremony held by CIO Magazine last October.

The judging panel found her brownfield approach to be especially praiseworthy: Swarovski employees use but continue to use the familiar processes and databases. A complete redesign of these processes in parallel to the migration would have been too risky and cost-intensive. It would have also resulted in a much longer project duration, Sonderegger is convinced.

25,000 tests with 600 participants

The brownfield implementation was carefully executed. Preparations took two years and involved more than 600 participants performing around 25,000 tests. Two dress rehearsals with strict governance ensured that every function and every data point was ready for the migration.

Sonderegger and her colleagues reserved a 66-hour conversion window for the go-live on April 20, 2026. During this period, all global IT processes at Swarovski were paused. During the subsequent sensitive hypercare phase, 24×7 support ensured that any issues that arose could be dealt with quickly. Thanks to these measures, the transition was seamless. After the conversion window closed, all processes resumed without problems.聽

Simplification and standardization ensure consistent data

Despite the large effort involved, this migration was merely the first step. While the switch to 麻豆原创 Cloud ERP Private created the technical foundation, it鈥檚 the subsequent investments that deliver additional added value. These investments concentrate on the incremental reduction of complexity through consolidation of fragmented solutions, the reassessment of user-specific code, and the harmonization of data鈥攁ll with the overall goal of creating a more coherent, easier-to-handle ERP landscape.

To achieve this, Sonderegger and her team are replacing user-specific applications with 麻豆原创 standard solutions step by step and only leaving custom developments in place where they offer clear advantages. 鈥淭he combination of simplification and a return-to-standard solutions improves data consistency, provides for robust, reliable processes, and, ultimately, makes our entire organization more agile,鈥� Sonderegger says.

Cloud technology is not an end in itself

By integrating key functions such as finance, supply chain management, retail, and e-commerce鈥攁nd enabling their combined use in the cloud鈥斅槎乖� Cloud ERP Private provides for reliable processes and consistent data quality all while enabling customer experiences on a wide variety of front-end solutions on this side of the ERP system.

麻豆原创 Cloud ERP Private manages a diverse product range at Swarovski across different regions and price points and integrates with the planning results provided by other 麻豆原创 and non-麻豆原创 systems.

鈥淚n all of these activities, cloud technology is never an end in itself, but rather a lever for improving efficiency, resilience, and innovative capabilities,鈥� Sonderegger says. This determination is especially important to her.

It鈥檚 not an IT project, it鈥檚 a business transformation

Ultimately, Sonderegger and her team succeeded in executing the project on time and on budget because its scope was clearly defined, and strict discipline in change management prevented mission creep. In addition, the company benefited from the experience of its implementation partner, 麻豆原创 Consulting, and its unrestricted access to 麻豆原创 expertise.

The example of Swarovski proves that even an essential, unavoidable migration can and should do much more than just avoid risks and cut maintenance costs. The implementation of 麻豆原创 Cloud ERP Private was imperative here, because 麻豆原创 ERP Central Component (麻豆原创 ECC) had reached the end of its lifecycle.

And the implementation is showing the luxury goods manufacturer the way to the future because everyone involved in the process didn鈥檛 just consider it to be an IT project but, above all, a business transformation from day one. One that involved hundreds of experts from different fields and that enjoyed full management support from the beginning.

AI-driven demand forecasts optimize warehouse stocks

Artificial intelligence is also playing a key role in this implementation, with 麻豆原创 Cloud ERP Private as the operational backbone of an AI ecosystem that can deliver reliable, real-time data and robust, standardized transaction processes.

Swarovski doesn鈥檛 use artificial intelligence as a standalone technology, but instead as an integrated capability that complements business processes across all functions. The company is already using AI for demand forecasting, for example, and then uses the results to optimize warehouse stock levels across regions, with the aim of improving the customer experience.

And the AI agent factory initiative enables the development of AI agents that link 麻豆原创 Cloud ERP Private data with data from non-麻豆原创 systems, always with the objective of 鈥渁utomating repetitive tasks, supporting decision-making, and boosting productivity along the entire value chain,鈥� Sonderegger emphasizes.

Top image courtesy of Swarovski

WALLDORF 鈥� (NYSE: 麻豆原创) and IBM today announced that global enterprises JYSK, GBM, DIFARE Group and Plastilene Group have selected 麻豆原创 Cloud ERP Private solutions on to modernize enterprise resource planning (ERP) workloads in secure, reliable and scalable cloud environments.

Spanning retail, technology services, pharmaceutical and manufacturing, these clients are among the tens of thousands of businesses that run 麻豆原创 landscapes on IBM Power servers.

According to a from the IBM Institute for Business Value (IBV), modernizing ERP workloads is essential for driving AI adoption and business growth, with companies embedding AI into ERP systems achieving up to 27% higher ROI. Leveraging 麻豆原创 Cloud ERP Private on IBM technology can support customers as they scale on-premises ERP environments to the cloud and accelerate AI-enabled business workflows.

The platform delivers a flexible hybrid cloud environment, which can help organizations:

• Reduce the total cost of ownership (TCO) of cloud ERP operations, supported by the ability to scale granularly to match business demand and leverage IBM鈥檚 global cloud infrastructure, designed to be highly resilient.
• Migrate to 麻豆原创 Cloud ERP Private and to the 麻豆原创 Business Warehouse (麻豆原创 BW) application as part of the 麻豆原创 Business Data Cloud (麻豆原创 BDC) solution, securely, quickly and with minimal disruption, including support for hybrid cloud and multicloud deployments.
• Mitigate operational and security risk, through IBM Power鈥檚 enterprise-grade resilience and integrated IBM Cloud security and compliance protection.

Modernizing ERP Workloads in More Secure, Scalable Cloud Infrastructure Across Industries

Known for its high security, scalability and reliability, IBM Power servers are ranked as one of the top servers for uptime and availability among 麻豆原创-certified infrastructure, engineered for fewer disruptions and faster migration, supported by the highly resilient and secured IBM Cloud platform. These clients are rapidly migrating on-premises 麻豆原创 software systems to the cloud, modernizing business processes and becoming more agile:

• JYSK, the international home furnishing retailer based in Denmark, is advancing its global modernization journey with IBM and 麻豆原创. With more than 3,600 stores in 50 countries, the retailer needs its 麻豆原创 software landscape to be more secure, scalable and future-ready to enable it to keep up with the demands of its global business. JYSK has a long history with IBM technologies and continues to work with IBM to advance in their RISE with 麻豆原创 journey.
• DIFARE Group, a leading pharmaceutical manufacturing company based in Ecuador, required a robust, secure and scalable infrastructure to modernize its 麻豆原创 software landscape and support critical business operations. As long-term users of IBM Power servers, DIFARE Group continues to place its confidence in IBM technology and has expanded into RISE with 麻豆原创 on IBM Power Virtual Server to help move to the cloud faster and more cost effectively.
• Plastilene Group, an innovator, developer and manufacturer of flexible film solutions in Colombia, chose IBM technologies to modernize its 麻豆原创 software landscape. With the ability for the solution to deliver better TCO, Plastilene can continue its focus on growth and regional diversification.
• GBM, a leading IT services company in Central America and the Caribbean, is focused on improving agility, scalability and real-time insight to better support its customers. By leveraging IBM technology curated for 麻豆原创 Cloud ERP Private to help gain reliability, security and high performance, GBM is creating a strong foundation to adopt 麻豆原创 software innovations and drive continuous transformation across the organization.

Industry-Leading 麻豆原创-Certified Infrastructure Enables Cloud Modernization

鈥淎s enterprises modernize, the journey to 麻豆原创 Cloud ERP Private is dedicated to helping on-premises customers of 麻豆原创 ERP tailor their transformation and bring business applications, data and AI together with 麻豆原创 Business AI Platform. Some of these customers are now modernizing their cloud ERP landscapes and advancing their cloud ERP digital transformation strategies with 麻豆原创 solutions on IBM Power Virtual Server,鈥� said Lalit Patil, CTO for RISE with 麻豆原创 and Head of Cloud Lifecycle Engineering and Operations, 麻豆原创 SE.

鈥淥rganizations across industries are accelerating their move to 麻豆原创 Cloud ERP Private and require a trusted cloud platform designed for mission鈥慶ritical workloads,鈥� said Hillery Hunter, General Manager for IBM Power, CTO, IBM Infrastructure. 鈥淏y combining the security, scalability and resiliency of IBM Power and IBM Cloud with the transformation capabilities of 麻豆原创 Cloud ERP Private, we are committed to helping clients move forward with confidence on their modernization journeys.鈥�

IBM is a full lifecycle strategic partner of 麻豆原创, providing end-to-end consulting and technology solutions for 麻豆原创 customers including hybrid cloud, automation and agentic AI. IBM and 麻豆原创 recently progress across AI and agentic capabilities to help accelerate enterprise transformation, including an expanded collaboration through the Agent2Agent (A2A) interoperability standard to perform complex multi-agent services for clients. IBM Consulting Advantage can now manage Joule Agents, which work directly with IBM鈥檚 watsonx Orchestrate agents.

For more information about 麻豆原创 Cloud ERP Private on IBM Power Virtual Server, visit: .

Visit the . Get 麻豆原创 news via  and .

Media Contacts:
Scott Malinowski, +1 (617) 538-6297, scott.malinowski@sap.com, ET
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This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
漏 2026 麻豆原创 SE. All rights reserved.
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The agreement, concluded at the end of 2025, marks a significant step in migrating Nokia鈥檚 麻豆原创 landscape to the RISE with 麻豆原创 journey. By adopting RISE with 麻豆原创 Methodology, Nokia will follow a structured, end-to-end approach to migrating its ERP landscape covering processes, data, applications and operating models while gaining continuous access to innovation and embedded AI capabilities delivered through 麻豆原创鈥檚 cloud ERP portfolio.

Nokia has selected Microsoft Azure as the cloud platform underpinning the transformation, providing the global scale, security and performance required to support the company鈥檚 most business-critical enterprise workloads. 鈥淣okia鈥檚 decision reflects a clear commitment to business-led transformation,鈥� said , Global President Customer Success Europe, APAC, Middle East and Africa and Member of the Extended Board, 麻豆原创 SE. 鈥淩ISE with 麻豆原创 Methodology provides Nokia with a structured road map, integrated toolchain and continuous access to innovation. It enables the company to modernize its ERP landscape while keeping a clean core and building a strong foundation for enterprise AI.鈥�

A Structured Approach to ERP Transformation

RISE with 麻豆原创 is designed as a comprehensive business transformation framework rather than a point solution. It combines a standardized transformation methodology, integrated tools and expert guidance to help organizations move from legacy ERP environments to RISE with 麻豆原创.

麻豆原创 will operate and manage the 麻豆原创 S/4HANA software environment in the cloud, allowing Nokia to shift focus from infrastructure management to business outcomes. The approach supports process standardization, operational simplification and ongoing innovation, rather than a one-time system migration.

Nokia has been on a business and technical transformation journey with its next-generation 麻豆原创 S/4HANA software environment, covering finance and key logistics capabilities, supported by 麻豆原创 solutions and applications. These include 麻豆原创 S/4HANA for central finance, 麻豆原创 Master Data Governance, 麻豆原创 Extended Warehouse Management, 麻豆原创 Global Trade Services and 麻豆原创 S/4HANA Cloud for advanced ATP. AI-enabled functionality embedded in 麻豆原创鈥檚 cloud applications will be progressively adopted as part of the journey.

鈥淭his agreement builds on our existing work with 麻豆原创 and Microsoft and supports Nokia鈥檚 ambition to secure how we run our core business operations,鈥� said Marek O膷kay, VP, Global Head of IT Procurement & Vendor Management, Nokia. 鈥淏y applying RISE with 麻豆原创 Methodology on Microsoft Azure, we are strengthening a structured and future ready path for business growth 鈥� one that simplifies our ERP landscape, enables continuous innovation and strengthens our commitment for AI driven processes.鈥�

Microsoft Azure as the Cloud Foundation

Microsoft Azure will serve as the cloud foundation for Nokia鈥檚 RISE with 麻豆原创 journey, aligning with Nokia鈥檚 broader cloud and data strategy. Nokia already operates parts of its 麻豆原创 landscape on Azure, and consolidating workloads on a single hyperscale platform is expected to deliver benefits in performance, security latency and operational resilience.

As part of the agreement, Microsoft will collaborate closely with 麻豆原创 and Nokia throughout the transformation, supporting migration activities and ongoing optimization.

鈥淭his collaboration demonstrates how cloud platforms, enterprise applications and AI can come together to support complex, global business transformations,鈥� said Joacim Damgard, CVP, Europe North Microsoft. 鈥淏y running 麻豆原创 S/4HANA on Azure within the RISE with 麻豆原创 journey, Nokia is creating a scalable and secure foundation for continuous innovation.鈥�

Building on a Longstanding 麻豆原创 Relationship

Nokia has been an 麻豆原创 customer for decades. In recent years, the company has been consolidating multiple ERP systems into a unified 麻豆原创 S/4HANA software landscape as part of its next-generation ERP program.

The move to RISE with 麻豆原创 helps secure that journey, providing a structured methodology to help accelerate transformation, reduce complexity and unlock cloud native capabilities.

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About 麻豆原创

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

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麻豆原创 is therefore one of only a few providers in Germany that will be offering a cloud environment whose key security components have received corresponding authorization to use from the BSI 鈥� and currently the only provider whose platform will be able to support both 麻豆原创 applications and customer-specific applications in a high-performance, VS-NfD-compliant environment in the near future.

The authorization to use applies to workloads running on 麻豆原创 Cloud Infrastructure in 麻豆原创鈥檚 own data centers in the Walldorf/St. Leon-Rot region, which are operated exclusively by security-cleared personnel. The authorization to use represents an important milestone and forms the basis for the subsequent full BSI approval 麻豆原创 is working toward, including the recertification of 麻豆原创 Cloud Infrastructure according to ISO 27001 based on the German IT-Grundschutz framework.

The evaluation process was completed in approximately 12 months and was characterized by close and constructive cooperation between the BSI and 麻豆原创 鈥� a sign of the growing collaboration between public authorities and industry in the field of IT security.

麻豆原创 Cloud Infrastructure: A Fully Sovereign Cloud Infrastructure from Germany

麻豆原创 Cloud Infrastructure is an Infrastructure-as-a-Service (IaaS) platform fully developed and operated by 麻豆原创, based on open-source technologies.

It is designed to provide customers : data sovereignty, operational sovereignty, technical sovereignty and legal sovereignty.

The foundation is a fully sovereign cloud region of 麻豆原创 Cloud Infrastructure, comprising three independent availability zones in physically separated data centers in Walldorf/St. Leon-Rot. This sovereign cloud platform is further reinforced by a , demonstrated through certifications including ISO/IEC 27001 based on IT-Grundschutz for the data centers, EN 50600/ISO/IEC 22237, TSI Level 3+ and C5 Type II. Additionally, 麻豆原创 Cloud Infrastructure has conducted a self-assessment against the BSI’s C3A (Criteria enabling Cloud Computing Autonomy) catalog, confirming compliance with all digital sovereignty requirements.

The VS-NfD authorization to use adds an important building block for security-critical use cases with the highest requirements.

As a deployment option within , 麻豆原创 Cloud Infrastructure is an integral part of 麻豆原创鈥檚 portfolio for digital sovereignty. Together with the 麻豆原创 Sovereign Cloud On-Site offering and Delos Cloud, 麻豆原创 provides customers with demanding regulatory requirements the freedom of choice, control, and robust security they need.

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漏 2026 麻豆原创 SE. All rights reserved.
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This momentum is aligned with consumer audio brand Victrola鈥檚 journey. Since its creation in 1906, has evolved into a global company known for its iconic record players, modern audio products, and mission to bring lifelong music memories to everyone

As it expanded into new sales and distribution channels, the company began to outgrow the limits of its existing ERP system. While its 麻豆原创 ERP Central Component (麻豆原创 ECC) system was stable and served the business well over the years, Victrola needed more scalability and flexibility to match its ambitions. According to Adam Schneider, SVP of Digital Strategy at Victrola, the goal was not simply to maintain operations, but to create a foundation that could support innovation. 鈥淲e needed something that fueled our creativity at Victrola,鈥� he said. And 麻豆原创 Cloud ERP was the right fit.

Victrola鈥檚 鈥渓et鈥檚 go鈥� mentality

A move to the public cloud offered more than a technical upgrade; it empowered Victrola to adapt. In that spirit, the team approached the transformation as a complete reimagining of its system with a cloud-first mindset.

鈥淲e have a 鈥榣et鈥檚 go鈥� mentality in every single meeting we do. You鈥檒l see a lot of fist bumps at Victrola and we love it. It really keeps that change front and center,鈥� Schneider said.

The project team prioritized change management, openly communicating the reasons for moving to the cloud and the expected benefits. Giving the 鈥渨hy鈥� made a big difference, Schneider said, and everyone was 鈥渆xcited for something new.鈥�

It was also important for the team to engage with and get buy-in from company leaders, who were concerned about disruption during Victrola鈥檚 peak sales quarter. To address these concerns, the team ensured the transformation was a business-led project, involving about 75% of Victrola鈥檚 leaders, Schneider said.

Another way the team worked to prevent operational disruption and build confidence in the stability of the new system was to involve a partner with deep expertise in 麻豆原创 Cloud ERP. The right partner would also fit seamlessly into Victrola鈥檚 unique, candid, music-oriented culture, Schneider said, which it found in .

鈥淲e wanted someone that could really provide that business knowledge on the public cloud,鈥� he said. 鈥淔rom a timing perspective, we couldn鈥檛 afford to have people figuring out the project as they鈥檙e on the project, so it really was the expertise of the public cloud that was a huge part in this.鈥�

With a winning team assembled, Victrola鈥檚 move to the public cloud was well-supported and bolstered by stakeholders across the company, 麻豆原创, and Reply.

Fine-tuning the foundation

Victrola’s covered order-to-cash and finance processes as well as large warehouse operations. The implementation itself was strategically timed to avoid overloading the team during the critical Q4 sales period, and it took about six months in total. Schneider said that the business adapted to the new system gradually: within the first month confidence grew, and by four months post-launch operations normalized with ongoing fine-tuning.

Victrola chose not to migrate historical data due to changes in organizational structure when it came to data management and the greenfield nature of the cloud system. This decision avoided technical debt and complexity. In addition, legacy customizations were largely eliminated through fit-to-standard workshops.

鈥淲e were looking for the ability to adapt with us, and we wanted a system that could be as simple or as complex as we needed it to be,鈥� Schneider said.

With the new cloud system, Victrola experiences greater trust in data accuracy and reporting agility. Financial processes have improved significantly, with profit and loss reporting time going from four hours to just 10-15 minutes. Faster closes and easier margin analysis contribute to Victrola鈥檚 improved agility, more informed decision-making, and stronger business performance.

鈥淲e鈥檙e more trusting in our data and that鈥檚 because we went through that exercise of really retooling what our landscape looked like and what our foundation looked like,鈥� Schneider said. It鈥檚 because of that re-engineering of data and transactions that Victrola has eliminated over 250 hours of finance-related work.

The next track

Now that Victrola is running on the public cloud, the sky is the limit when it comes to growth and innovation. 鈥淲hen we think about our AI strategy, I鈥檓 no longer scared of our system,鈥� Schneider said. 鈥淲e鈥檙e now applying an AI strategy to a state-of-the-art system that 麻豆原创 very much supports.鈥�

When asked what advice he鈥檇 give to companies contemplating a move to the cloud, Schneider said that there is never a perfect time to start, but it鈥檚 better to welcome transformation rather than fear it.

鈥淚 don鈥檛 think there is ever a perfect time to do these transformations,鈥� he added. 鈥淚 do think once you get into them, you start to really embrace them and feel good about it and say 鈥業t鈥檚 happening, let鈥檚 go.鈥欌��

Interested in learning more about how 麻豆原创 supports the transition from 麻豆原创 ECC to 麻豆原创 Cloud ERP? .

Photo courtesy of Victrola.

He mentioned driverless cars in San Francisco, autonomous drones conducting warfare, and robots that are trained to care for humans as real humans would. Speaking at the recent TAC Insights sponsored conference featuring , Weber admitted he finds it all rather scary, but also very exciting.

For an energy company operating critical infrastructure, this pace of technological change is not just fascinating or frightening鈥攊t creates a responsibility to adopt innovation in a controlled, resilient, and purpose鈥慸riven way.

Riding the waves

Weber sees these developments as a continuation of various “big waves” of technology that keep touching our hearts and minds as they shape the world around us. Who can imagine the world without the internet? Who can deny that the mobile phone didn鈥檛 revolutionize the consumption of IT when people started expecting the same ease of use in the workplace?

鈥淎I is creating the same response,” Weber explained. “ChatGPT makes my life easier at home solving gardening issues, so I expect it to make my life easier at work.鈥�

One of E.ON鈥檚 biggest challenges is closing the widening gap between the rapid pace of technological innovation in the outside world and the organization鈥檚 internal ability, shaped by its structure and DNA, to absorb and implement these changes effectively.

This tension became evident when leadership questioned whether sustained IT spending at large scale was justifiable. It soon became clear that continuous investment is the price of system stability, affordability, and resilience in a digitized energy system if E.ON is serious about becoming the leading playmaker in Europe鈥檚 green energy transformation.

To achieve this ambition, the company has defined three strategic priorities鈥攇rowth, sustainability, and digitalization鈥攔ecognizing that falling behind in digital capabilities would carry far greater long-term costs.

鈥淏ringing the system up to speed requires internal readiness. It means we must think deeply about investments, prioritization, and most importantly, people and culture,鈥� said Weber. 鈥淥ne thing is sure: we won鈥檛 be going back to what was normal speed before.鈥�

Becoming strategic

E.ON operates across three domains: energy grid, customer solutions, and energy infrastructure solutions. 聽This broad scope creates a high level of operational complexity, requiring scalable, transparent, and collaborative ways of working across the organization.

To meet these challenges, E.ON is strengthening its internal capabilities and investing in its people. By expanding in-house expertise, the company has welcomed over 1,000 specialists, including more than 500 in data and 300 in cybersecurity, fostering greater ownership, collaboration, and innovation across the organization.

This move reflects a broader philosophy. IT is no longer just a support function; it is foundational to pioneering the energy transition and delivering competitive advantage.

As E.ON鈥檚 transformation unfolds against a backdrop of rapid technological evolution, AI is at the heart of the current inflection point. Technologies like AI-powered assistants and automation tools are not novelties; they are actively redefining how customers interact with services. E.ON recognizes this shift and is embedding advanced technologies directly into its core systems, rather than treating them as add-ons.

Closing the gap

Weber explained that digital transformation at E.ON means putting the right technology into the core of the business to better serve its 47 million customers.

It starts with platform standardization, followed by cloud ERP transformation and the 麻豆原创 S/4HANA migration. Instead of building fragmented custom solutions, this strategy allows the company to integrate leading technologies into a cohesive architecture, ensuring scalability while avoiding unnecessary complexity. These basic investments in foundational infrastructure have delivered tangible results, including an 77% reduction in IT downtime within five years.

A key lesson from E.ON鈥檚 journey is the importance of embedding digital capabilities into the heart of operations. 鈥淲e鈥檝e moved away from isolated innovation hubs such as digital labs or experimental ‘garages’ in favor of integrating digital tools directly into business processes,鈥� Weber explained.

While innovation is essential, E.ON places equal emphasis on governance and control. Managing a digital ecosystem at this scale requires strong oversight to ensure security, consistency, and cost discipline. The company implemented centralized governance structures, including standardized contracting and unified IT system management to help maintain control without stifling innovation.

Equally important is investment in people. Through targeted training and capacity building initiatives, employees are empowered to turn new technologies into measurable business impact.

Harnessing AI

As with many companies, AI is at the center of E.ON鈥檚 forward-looking strategy, but the company is approaching it with deliberate caution. Rather than rushing to build proprietary platforms, E.ON is leveraging partnerships with established technology providers while maintaining flexibility in its IT portfolio. This approach allows the company to explore the potential of AI in customer service automation, predictive maintenance, and operational optimization without overcommitting to unproven solutions.

鈥淚n essence, our experience highlights a broader truth about digital transformation,鈥� said the IT expert. 鈥淪uccess really depends on balance. We absolutely must push innovation forward, but not at the expense of stability, cyber security or governance.鈥�

Equally, digital tools alone are not enough. Without proper training and alignment with business needs, even the most advanced technologies can fail to deliver value. E.ON addresses this through a “BizDevOps” mindset, ensuring that digital initiatives are an integral part of business goals and supported by the right capabilities.

In summary, E.ON鈥檚 transformation illustrates what it takes to modernize at scale in a complex, highly regulated industry. By doubling down on IT investment, bringing expertise in house, and adopting a disciplined yet forward-looking approach to innovation, the company has positioned itself for the future of energy.

The result is not only improved system performance or reduced downtime. It鈥檚 a fundamental shift in how technology drives business success, turning technology into a cornerstone of making new energy work鈥攔eliably, affordably, and at scale.

This was the exact situation facing Stephen Hawkes, director of Information Technology for the City of Charlottesville, Virginia. But instead of just managing the present, he and his team decided to build the future.

The perfect storm of challenges

For the City of Charlottesville, it was a perfect storm of challenges converging at once. At the heart of it all was a ticking clock: its aging, on-premise legacy system nearing its end-of-support date. This was more than a technical issue, it was a foundational risk to its operations.

At the same time, the expectations of its own employees were skyrocketing. “Everyone is an expert now,” Hawkes explains, pointing to the powerful smartphones and intuitive apps we all use daily. 鈥淐ity employees expected the same simplicity and modern design from their workplace software, but the old systems are causing friction and frustration.鈥�

This frustration was compounded by significant workforce constraints. Like most public sector organizations, Charlottesville found it difficult to compete with private sector salaries. “We are never going to be able to compete on pay,” Hawkes admits. This made recruiting and retaining skilled talent a constant battle.

And looming over everything was the growing shadow of cybersecurity threats. With AI-powered attacks becoming more sophisticated by the day, protecting the city’s data and infrastructure was a monumental task for a small IT team.

The quest for a modern solution

Inaction was not an option. The city needed more than just a simple upgrade. It needed a fundamental shift. It embarked on a bold, 14-month quest with a full digital transformation to move operations to .

This was its answer to the storm. By migrating two decades of data to the cloud, it built a new, resilient foundation for the future.

The impact on employees was immediate and profound. The new, web-based 麻豆原创 Fiori interface delivered the modern, intuitive experience everyone had been waiting for. “That’s what we’re probably most excited about,” Hawkes says. 鈥淲ith potentially powerful new AI capabilities at their fingertips, the city鈥檚 team can now exceed expectations, instead of struggling to meet them.鈥�

This new technology also became a powerful tool in the battle for talent. Hawkes sees the integrated AI tools as a “great leveler,鈥� enabling logical, problem-solving thinkers to perform complex data analysis without needing a specialized computer science degree. This widens the talent pool and empowers the existing workforce. And with the implementation of 麻豆原创 SuccessFactors solutions, its HR professionals now have modern tools to improve recruitment and retention.

Perhaps most importantly, the move gave the city a powerful ally in the fight against cyber threats. While Hawkes is proud of his internal team, he knows they can鈥檛 be on guard 24/7. “We’re not, [but] they are,” he says of 麻豆原创’s global security operation. “That gives us some ease.”

Wisdom from the journey: lessons for fellow leaders

A journey of this magnitude is never without its lessons. When asked what advice he鈥檇 offer his peers, Hawkes shared three crucial pieces of wisdom.

First, he stressed the absolute necessity of executive buy-in. For years, the project struggled to get off the ground due to leadership turnover. It wasn’t until the city manager gave the definitive ‘let’s move forward’ that the quest could truly begin. That sponsorship is the key that unlocks everything else.

Next, he highlighted the importance of choosing the right partner. A transformation project is too complex to undertake alone. Hawkes credits the success of going live on the exact day they had planned 14 months earlier to the deep trust and true partnership they had with their system integrator.

Finally, he spoke about the critical, and often underestimated, element of change management. You can have the best technology in the world, but if your people aren’t prepared for it, the project will falter. “We were very intentional about our change management,” Hawkes recalls, emphasizing that planning for the human side of the transition is just as important as the technical one.

The City of Charlottesville鈥檚 story is a testament to what鈥檚 possible when vision, strategy, and technology align. It鈥檚 a narrative of turning daunting challenges into defining opportunities and building a government that鈥檚 ready for tomorrow.

To get the full, firsthand account of this incredible transformation, .

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

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Grupo UMA, a leading motorcycle assembler and distributor, is accelerating manufacturing growth across Colombia and Central America with 麻豆原创 Cloud ERP Private.

Operating in multiple countries and serving thousands of riders every month, the company needed standardized, automated, and data鈥慸riven processes to support expansion while maintaining quality and customer experience. By implementing 麻豆原创 Cloud ERP Private, Grupo UMA streamlined core business processes across finance, production, and logistics, replacing manual work with real鈥憈ime insights and end鈥憈o鈥慹nd process integration. Global operating models, automated workflows, and transparent cost visibility now enable faster decision鈥憁aking and scalable growth across the region. The impact: production increased from approximately 11,000 to 17,000 motorcycles per month, with a clear path to 20,000. Across markets including Guatemala, El Salvador, Costa Rica, Honduras, Nicaragua, and Colombia, Grupo UMA is improving supply chain efficiency, optimizing production capacity, and delivering consistent quality with solutions such as 麻豆原创 Extended Warehouse Management (麻豆原创 EWM). Beyond operations, teams are shifting from manual, operational work to a more strategic, innovation鈥慸riven, and analytics鈥慺ocused approach鈥攈elping the organization evolve into a truly data鈥慸riven enterprise.

Discover how 麻豆原创 helps manufacturers in Latin America scale faster, operate smarter, and deliver better customer experiences with cloud ERP and read the full customer story on the 麻豆原创 News Center.

The approach enables the company to scale AI use cases across the business, accelerate decision-making and deliver measurable operational impact. By combining a governed data foundation with the Joule solution and this foundation, Ericsson is creating the enterprise architecture needed to make AI trusted, repeatable and scalable across its global operations.

Ericsson, which celebrates its 150th anniversary this year, provides mobile network infrastructure across 180 countries, with more than 40% of the world鈥檚 mobile traffic passing through its networks. As AI becomes central to both its technology road map and how it runs the business, Ericsson has prioritized building a strong, governed data foundation to support scalable and trusted AI.

鈥淥nce you scale AI, it stops being an AI problem鈥攁nd becomes a data problem,鈥� said Esra Kocat眉rk Norell, Vice President, Customer Experience, Enterprise IT at Ericsson. 鈥淭hat鈥檚 why we invested early in a business data fabric. With 麻豆原创 Business Data Cloud, we can define what data means once鈥攆rom revenue to market structures and access rules鈥攁nd apply it consistently across the enterprise. That鈥檚 what allows us to scale AI in a way that is trusted, repeatable and delivers real business value.鈥�

At the core of Ericsson鈥檚 approach is a federated data architecture that allows data to remain in place while centrally managing business semantics, governance and lifecycle policies. This reduces duplication, simplifies integration and ensures that consistent business definitions can be applied across both 麻豆原创 software and non-麻豆原创 environments.

By focusing on high-impact use cases and organizing around end-to-end business processes rather than isolated solutions, Ericsson has moved beyond pilot projects to scaled deployment. Today, more than 85,000 users are live on unified Joule, supported by strong executive sponsorship and governance.

Ericsson is advancing its transformation on two parallel fronts. The first is modernization, including its transition to the RISE with 麻豆原创 journey, the use of side-by-side extensions on 麻豆原创 Business Technology Platform and a clean core approach that enables faster innovation without disrupting its ERP backbone. The second is what the company defines as 鈥渋nnovate and transform,鈥� focused on unlocking tangible business value from data and AI to improve decision-making, increase efficiency and enable new forms of value creation.

麻豆原创 and Ericsson are also collaborating on AI co-innovation initiatives. One example is an intelligent goal recommendation capability developed within the 麻豆原创 SuccessFactors portfolio. The solution generates contextual, business-aligned goals for employees, improving execution and reducing administrative effort. The capability is now being scaled more broadly, demonstrating how co-innovation can create value beyond a single organization.

鈥淓ricsson鈥檚 approach shows how leading companies are moving from AI experimentation to execution by focusing on data, governance and business context,鈥� said Manos Raptopoulos, Global President Customer Success Europe, APAC, Middle East and Africa at 麻豆原创 SE. 鈥淭ogether, we are helping organizations unlock the full potential of AI at scale.鈥�

Looking ahead, Ericsson expects its business data fabric to support increasingly advanced AI scenarios, including automated decision-making, improved productivity and new digital business models, while continuing to strengthen customer experiences in a rapidly evolving telecom landscape.

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Ulrika Wass, +46 73 827 1074, ulrika.wass@sap.com, CET
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This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ.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.  

The objective of this collaboration is to digitalize procedures, improve efficiency and deliver better services to municipal employees and citizens in the areas of finance, revenue management and human resources.

The Madrid City Council has been working with 麻豆原创 software for two decades. It began in 2004 with the implementation of the first solutions in the areas of finance and HR, and in 2020 launched its public administration modernization project with the migration to the private cloud. This process is now advancing further with the adoption of the RISE with 麻豆原创 journey and 麻豆原创 Business Technology Platform (麻豆原创 BTP). The former is a comprehensive journey that combines the elements needed to migrate to the private cloud under a single contract: 麻豆原创 S/4HANA, infrastructure and managed services. The latter is the platform for integration, extension and application development.

A New Public Management Model

The adoption of these technologies represents a true revolution in the way municipal procedures are managed, from budgeting, execution and control of revenues and expenditures to the comprehensive management of human resources. This approach makes it possible to move beyond traditional models based on fragmented systems toward unified management with real-time information and digitalized processes.

The transformation has a particularly significant impact in the tax domain, as part of the project includes the integration of tax and revenue management solutions from 麻豆原创 into the city鈥檚 financial platform. This enables municipal revenues to be managed as a natural extension of the financial system, eliminating isolated developments and facilitating an end-to-end view of the full cycle, from taxpayer registration and assessment to collection and inspection. As a result, operational efficiency is improved while strengthening financial control and budget planning capabilities.

Currently, two-thirds of the City Council鈥檚 tax revenues are already managed within this environment, including Property Tax (IBI), the Urban Waste Tax for Business Activities (TRUA), Capital Gains Tax (IIVTNU) and the Terrace Tax (T2 2023). The next step will be to incorporate the Motor Vehicle Tax (IVTM) and the Economic Activities Tax (IAE).

The project has been developed using a phased methodology. During the first year, the City Council carried out a cleansing and harmonization of master data from its previous management systems (GIIM and +TIL), cross-checking identities with police databases, tax addresses with the Spanish Tax Agency (AEAT) and addresses with the municipal street registry. This process generated taxpayer 鈥淕olden Records鈥� and enabled, for example, an efficiency rate of 98.02% for Property Tax (IBI) in 2024. Data quality continues to be maintained for all new registrations.

According to Juan Corro, IT Manager of Madrid City Council (IAM), 鈥溌槎乖� technology offers us an extraordinary opportunity to accelerate our digital transformation and make the vision of a more efficient, innovative and citizen-centric local government a tangible reality. This project marks a paradigm shift: we are moving from managing paper files and isolated systems to managing information and processes in an integrated and intelligent way, with a 360-degree view. As a major capital city, Madrid has both the responsibility and the opportunity to position itself at the forefront of administrative modernization, serving as a benchmark for other municipalities.鈥�

Carlos Lacerda, Senior Vice President and Managing Director of 麻豆原创 Southern Europe, stated: 鈥溌槎乖� remains firmly committed to the Spanish public sector, which we have supported in its modernization processes for decades. This project is a benchmark for advanced digital administration and demonstrates how technology can act as a strategic enabler to simplify processes, integrate information and strengthen real-time data-driven decision-making, laying the foundation for a more agile, innovative and service-oriented public administration.鈥�

Benefits for the Administration and Citizens

The project is delivering benefits both in terms of internal efficiency and management, as well as citizen services:

• End-to-end process digitalization and a 鈥減aperless鈥� administration: The 鈥減aperless鈥� administration model has been consolidated, enabling the full digitalization of HR processes from start to finish. Requests are managed entirely through the municipal intranet. Internally, public employees can review and approve procedures with full traceability and in just a few steps, reducing processing times and errors caused by duplicate data. The result is a more agile, efficient and nearly 24/7 service that improves both the employee experience and citizen services.
• Operational efficiency and improved decision-making: Automation and AI capabilities integrated into the ERP system allow the City Council to significantly improve efficiency and productivity. Routine processes such as bank reconciliations and budget allocations are automated through rules and machine learning. In addition, the use of robotic process automation and services on 麻豆原创 BTP facilitates the automatic execution of repetitive tasks across systems. This reduces manual workload, minimizes errors and frees up time. Real-time analytics improve decision-making and, together with mobile and remote access to applications, enable more agile and flexible management.
• A more sustainable and efficient model: The implementation of RISE with 麻豆原创 enables the City Council to move toward a more sustainable and economically efficient IT model, based on subscription and pay-per-use principles. This approach reduces upfront investments, provides greater budget predictability and optimizes total cost of ownership. By scaling deployments according to municipal needs and paying only for required resources, the city improves responsible management of public funds while generating potential long-term savings.
• Greater adaptability and evolution: The City Council now has a flexible platform ready to evolve alongside technological, regulatory and social changes. The municipality will be able to align with national and European digital agendas, incorporate AI and advanced analytics capabilities, and evolve toward a smart administration model where data becomes a strategic enabler of better public policies.
• Continuous innovation: 麻豆原创 BTP is the innovation platform that integrates internal systems and third-party solutions, eliminating information silos. It also enables the rapid adoption of new technologies and responsiveness to changing needs and supports the City Council not only in modernizing processes but also in continuously evolving and launching innovative public administration initiatives.

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Media Contact:
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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 2025 Annual Report on Form 20-F.
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That idea framed a , where Thomas Saueressig, chief customer officer and member of the Executive Board of 麻豆原创 SE, and Jan Gilg, global president of Customer Success & Americas and member of the Extended Board of 麻豆原创 SE, set out the company鈥檚 case for the Autonomous Enterprise.

Their message was clear: As AI moves from promise to practice, customers are no longer asking whether it matters; they are asking how to make it deliver measurable results across the business.

鈥淓very day, billions of people wake up trusting that the world simply runs,鈥� Saueressig said.

But making that happen is anything but simple. Saueressig pointed to the hidden complexity behind everyday routines 鈥� from power grids balancing supply and demand in real time to global supply chains moving goods across countries and continents. Enterprise operations, he argued, are the invisible backbone of modern life, even if most people never see them.

Gilg picked up that thread by focusing on the pressure customers now face as they try to translate AI ambition into business value. Excitement is high, he said, but so is urgency.

Customers want to scale AI across the enterprise and connect it to core processes where it can have tangible impact. But according to Gilg, the real obstacle is not the AI itself. It is the enterprise landscape around it.

鈥淭he elephant in the room: AI in the enterprise is complex,鈥� he said, pointing to the disconnected applications and fragmented data many organizations still contend with.

That challenge led directly to 麻豆原创鈥檚 vision for the 鈥� one in which AI is embedded into business processes, connected through trusted data, and governed in a way that makes it reliable at scale.

The Autonomous Enterprise vision

鈥淚t鈥檚 this need for trusted, seamless integration that led us to our vision for the Autonomous Enterprise,鈥� Gilg said.

He presented it not as a future concept, but as a practical operating model in which AI drives end-to-end execution within a trusted governance framework, with people remaining in control.

Saueressig cast 麻豆原创鈥檚 role as helping customers get there: 鈥淥ur goal is to help you become an Autonomous Enterprise step-by-step. … We are making AI value real today.鈥�

He linked that approach to RISE with 麻豆原创, 麻豆原创鈥檚 AI offerings, and the 麻豆原创 Services and Support Portfolio with its Ssuccess plans, which are designed to help customers put innovation to productive use. The emphasis, he said, is on creating value throughout the transformation journey

鈥淲hen you are fully committed to RISE with 麻豆原创, we are committed to support you at every step,鈥� Saueressig said. That commitment spans even the most complex and hybrid landscapes, he said, stressing that no customer will be left behind.

Lockheed Martin: Readiness over transformation in a high-stakes environment

That customer-first approach set up the next part of the keynote, where customers took the stage to share firsthand how they are transforming their businesses in the real world 鈥�  no theory, no abstraction, just practical experience.

Opening the customer round, Lockheed Martin positioned transformation not as an end goal, but to ensure constant readiness in one of the world鈥檚 most demanding environments.

鈥淭ransformation is not the goal. Readiness is for us,鈥� said Maria Demaree, SVP and CIO of Lockheed Martin Corporation, stressing that the stakes are 鈥渉uman鈥� when systems support national defense and allied missions. Readiness, she explained, means the ability to move 鈥渨ith speed, clarity, and confidence across the enterprise.鈥�

Through its largest transformation investment in the company鈥檚 history, Lockheed Martin is redesigning processes end-to-end, connecting fragmented systems, and embedding AI into a model-based enterprise built on 麻豆原创.

Operating in a highly regulated environment with strict security and data requirements, the company is focused on reducing cycle times and improving responsiveness. Demaree emphasized that 鈥渢ransformation doesn鈥檛 start with technology. You must rethink your processes.鈥� 麻豆原创鈥檚 role, she said, has evolved from vendor to trusted partner understanding Lockheed Martin鈥檚 business and the environment it works in.

Aeropuertos Argentina: From reactive winter operations to proactive AI-driven control

Aeropuertos Argentina made history by becoming the first Latin American customer to take the 麻豆原创 Sapphire keynote stage. The company used the spotlight to share a hands-on example rooted in operational urgency and showed how a clean core and focused innovation can quickly deliver results.

Managing 90% of Argentina鈥檚 commercial flights, they need to keep airport operations running during severe winter weather. This has historically relied on manual, fragmented processes 鈥� driving up costs, safety risks, and environmental impacts. To address this, the company developed an AI agent called Smart Network for Operative Winter (SNOW) to orchestrate weather data, runway sensors, maintenance processes, and operational procedures.

鈥淲e passed from a reactive to a proactive model,鈥� said Gustavo Sabato, Chief Information Officer of Aeropuertos Argentina, highlighting expected benefits, including a 16% cost reduction and lower CO鈧� emissions. Time to value was fast: from idea to operation in 12 weeks, with rollout starting at two airports and expanding to six more this upcoming winter.

A key enabler was upgrading from 麻豆原创 R/3 to 麻豆原创 S/4HANA in 2023 and building the solution on 麻豆原创 Business Technology Platform.  While integrating multiple non-standardized data sources was challenging, the result is now that the company operates with 鈥渙nly one version of the truth,鈥� said Sabato, and requires minimal manual intervention. The company plans to scale the approach beyond Argentina and into processes at other airports they manage elsewhere, reinforcing that strong technical fundamentals are essential to turn AI into real operational outcomes.

Exxon Mobil: Clean core and solid data foundation

ExxonMobil is rethinking how its operations will remain agile and nimble amid the rapid changes driven by the global shift toward new energy sources.

Bill Keillor, Vice President of ExxonMobil Global Services Company, said the energy giant launched a business-led transformation to simplify processes and unlock data that had become fragmented after decades of customization. 鈥淥ur goal is not short-term optimization but long-term agility: standardizing on industry best practices, establishing a clean core, and becoming upgrade stable,鈥� he said.

He emphasized that both the transformation and the company鈥檚 AI ambitions depend on a strong foundation. 鈥淚f you can鈥檛 get this foundation right, you will continue to pay the price for it,鈥� he said.

Keillor closed with three pieces of advice for any transformation: be crystal clear on strategy and align leadership behind it; put strong governance in place to enable fast, consistent decisions; and choose partners who challenge you and are in for the long run.

Levi Strauss: AI at scale

As Levi Strauss accelerated its shift toward a direct-to-consumer business, it recognized that greater speed and scale would require a lean technology landscape. Jason Gowans, Chief Digital and Technology Officer, said the company started by consolidating nine ERP systems into a single global foundation with RISE with 麻豆原创, standardizing processes and establishing a clean core.

That unified backbone now supports Levi鈥檚 ambitious AI strategy, with already more than 1,000 AI agents in production across the business. The impact is already visible; one example is wholesale order processing. While 80% of orders already flow through automatically, the remaining 20% 鈥� often submitted by smaller customers through handwritten notes, emails, or unstructured documents 鈥� previously took two to five days to process manually.

鈥淣ow, with the agents that we鈥檝e built on top of 麻豆原创, that process takes 20 to 30 minutes,鈥� Gowans said. For Levi Strauss, the lesson is clear: standardization does not limit agility; it makes it possible.

Migration powered by AI

These customer examples illustrated that transformation usually follows a shared path: modernizing the core, moving to the cloud, and unlocking innovation along the way. 

麻豆原创 then showed how AI-powered agents can help customers accelerate that journey through a more integrated, AI-driven approach to transformation at scale. Migration and modernization assistants, , are designed to analyze systems, data, custom code, configuration, testing, and rollout as part of one connected process. By replacing fragmented manual work with coordinated automation, activities that once took weeks 鈥� from landscape analysis to custom-code assessment 鈥� can now be completed in a single weekend.

The world doesn鈥檛 break because of change

Gilg then widened the lens, arguing that every major technology wave brings uncertainty. But every one of these waves has in fact made the world better off by creating more jobs, new business models, and new revenue streams that people couldn鈥檛 imagine before. In the same way, he argued, enterprise software will become even more essential because of AI.

That is because the core needs of business remain the same: systems that work, people who care, and teams that collaborate. In Gilg鈥檚 framing, AI will not replace enterprise software. It will live inside it, embedded in the processes that keep companies running.

Saueressig brought the keynote back to its opening image: a world people trust to function. In a time of rapid change and unprecedented disruption, he asserted, resilience matters more than ever.

鈥淭he world doesn鈥檛 break because of change,鈥� he said. 鈥淚t breaks when change moves faster than resilience. And that鈥檚 where 麻豆原创 comes in.鈥� Underscoring the importance of people in times of change, he emphasized that beyond technology and AI, transformation remains deeply human, shaped by the people who build and use it. 鈥淭he future isn鈥檛 written by AI.  It is written by us,鈥� he said.

They are expected to manage risk, ensure compliance, and deliver strategic value, all while navigating talent shortages and increasing operational complexity. And most are doing it without the end-to-end visibility they need.

Workflows are disconnected, decision-making is reactive, and policies are inconsistently enforced. I have heard this from customers across every industry and, frankly, it is a problem that traditional approaches to procurement technology haven鈥檛 fully solved.

That鈥檚 what makes this moment different. At 麻豆原创 Sapphire, we introduced the Autonomous Enterprise, a fundamental shift in how businesses operate, with AI assistants and agents powering end-to-end execution at scale, with governance built in. Critically, this isn鈥檛 just about adding AI features to existing tools. It is about moving from AI in applications to AI on applications鈥攊ntelligence that works across your entire landscape, not just inside individual products.

Autonomous Spend Management: From concept to reality

Autonomous Spend Management is a core pillar of the Autonomous Enterprise vision, designed to address the fragmentation that holds procurement and finance teams back. By applying agentic AI across procurement, travel, expenses, and external workforce processes, we鈥檙e creating continuity where disconnection exists today鈥攊ntelligent systems that orchestrate activities, connect context, and surface the right insights at the right moment.

What this means for the people doing the work is equally significant. When AI handles routine execution, decision-makers get time and clarity back. They can intervene earlier, with better information, and focus on more strategic work that actually moves the needle.

To bring this to life, we are introducing a new set of Joule Assistants, AI-powered teammates designed to support procurement and spend management across the full life cycle:

• Category Management Assistant: Analyzes spend patterns, delivers market intelligence, and helps build sharper category strategies
• Sourcing Assistant: Manages the entire sourcing life cycle, from drafting RFPs and bids to recommending negotiation strategies
• Supplier Management Assistant: Provides comprehensive oversight of the supply base, from intelligent classification to continuous multi-dimensional risk monitoring
• Contract Assistant: Streamlines contract authoring, flags renewal opportunities, and connects supplier selection through to contract execution
• Requisition Assistant: Guides users to the right buying channel, auto-fills fields, and uses advanced trade-off analyses to help maximize volume discounts
• Buying Assistant:Helps professional buyers identify spend leakage, surface optimal suppliers, and automate order consolidation
• Receiving Assistant: Auto-creates goods receipts and service entry sheets and guides users through quality tracking so nothing falls through the cracks
• Invoicing Assistant: Handles invoice capture, duplicate detection, and payment proposals so finance teams can close faster with fewer errors
• Services Procurement Assistant: Manages the full SOW life cycle from creation through compliance tracking
• Travel Assistant: Simplifies trip planning with pre-spend estimates, streamlined approvals, and built-in compliance guidance
• Expense Management Assistant: Automates expense reporting, capturing details, flagging errors, and keeping everything compliant

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

Why connected processes are critical

Connection is just as powerful as intelligence, and that conviction runs through everything we  announced this week. AI can only do so much if the underlying processes are still fragmented.

In next-gen 麻豆原创 Ariba Buying, new Joule Agents support purchasing and policy management through a more intuitive, persona-driven experience, guiding users toward compliant, contract-linked options while improving catalog management and document traceability. Deeper integration with 麻豆原创 S/4HANA Private Cloud Edition and 麻豆原创 ERP Central Component means these capabilities work with existing ERP investments, not around them.

麻豆原创 Ariba Contracts now brings contract creation, approvals, and compliance tracking into a single unified workspace. AI-assisted drafting lets teams create contracts using natural language, while centralized visibility into terms, pricing, and key dates keeps data consistent and connected to downstream procurement processes.

We also introduced a new Joule Agent in 麻豆原创 Ariba Intake Management to automate how procurement requests are captured and routed across 麻豆原创 and non-麻豆原创 systems. And expanded supplier evaluation capabilities in 麻豆原创 Ariba Supplier Lifecycle and Performance let teams segment performance data by geography, business unit, or category 鈥� with insights feeding directly into to inform sourcing and procurement decisions.

Expanding visibility into services spend and supporting adoption

Nowhere is the need for connected processes more apparent than in asset-intensive industries. In oil and gas, mining, and utilities, external workers can make up 40% of the workforce, yet most organizations are still managing them through manual processes and disconnected systems. The risks are real: expired certifications, overpayments, and poor visibility into work billed versus work actually done.

New 麻豆原创 Fieldglass capabilities address these challenges by bringing together the full contractor life cycle, from the moment a worker arrives on site through to final payment. Organizations can now automate time tracking, verify worker credentials and safety requirements before granting site access, maintain tighter controls over equipment, and dramatically reduce the manual effort involved in invoicing.

We鈥檙e also using AI to accelerate SOW creation by automatically recommending worker roles based on the SOW description and historical buyer data, which reduces manual setup and improves consistency from the start. And to support adoption, WalkMe Premium is now integrated with 麻豆原创 Fieldglass and 麻豆原创 Ariba, providing in-app guidance for tasks such as creating statements of work, approving timesheets, and hiring candidates.

The future of spend management

Autonomous Spend Management marks a fundamental shift from managing processes to delivering business outcomes. From chasing cost savings to actively shaping resilience, margin, and growth. From reacting to events to anticipating them.

The real strategic implication is this: Spend does not happen in isolation. Every contract and invoice has a downstream effect on financial performance. When those decisions are made in context鈥攚ith AI connecting procurement, supply chain, and finance鈥攖he enterprise doesn鈥檛 just run more efficiently, it runs as one system.

That鈥檚 what we are building, and what we announced this week marks a significant step forward.

For more details on this week鈥檚 announcements, see the . For more details on the latest updates in travel and expense, please refer to the

Etosha Thurman is co-business lead and chief marketing officer for 麻豆原创 Finance & Spend Management.

While expectations for reliable, on-time delivery remain high, organizations are navigating faster鈥慶hanging demand, more complex global networks, and increasing pressure on cost and working capital. And they鈥檙e looking for ways to turn insight into action more quickly and consistently across the supply chain.

麻豆原创 has been helping organizations build more connected and intelligent supply chains for over 50 years. At 麻豆原创 Connect in October, we introduced 麻豆原创 Supply Chain Orchestration, establishing a foundation for detecting issues, coordinating responses, and connecting execution across complex supply networks.

The innovations announced this week at 麻豆原创 Sapphire extend that vision further. By introducing a new set of AI-driven assistants and agents, we鈥檙e moving orchestration toward an autonomous operating model, where planning, manufacturing, logistics, and asset operations increasingly anticipate, coordinate, and resolve without manual intervention at every step.

AI grounded in real operations

AI delivers lasting value in supply chain management only when it is embedded where work actually happens. Autonomous agents do not operate independently of enterprise applications; they rely on deeply integrated processes and trusted data. Precision, compliance, and resilience depend on this foundation. Without it, AI does not scale or earn trust.

At 麻豆原创, the Autonomous Enterprise represents a vision for how organizations will run their businesses in the future: with insight, decision-making, and execution increasingly connected, while people remain firmly in control. Autonomous Supply Chain Management is a practical step toward that vision.

Autonomous Supply Chain Management reflects an evolution in how planning, execution, and operations work together. People define goals and priorities, assistants orchestrate activity across domains, and agents execute the work鈥攁ll within governed, end鈥憈o鈥慹nd processes.

At 麻豆原创 Sapphire, we鈥檙e introducing , enabled by new Joule Assistants and Industry AI scenarios that apply this model to daily operations across planning, manufacturing, logistics, engineering, and asset management. General availability will be phased throughout 2026, starting now.

Joule Assistants across the supply chain

Rather than disconnected AI tools, the following assistants will be embedded directly into core 麻豆原创 supply chain applications, where deep process knowledge, semantically rich business data, and enterprise鈥慻rade governance already exist.

Each will support a distinct area of responsibility while sharing context, data, and outcomes across the supply chain:

• Asset and Service Assistant: Changes how work gets detected and dispatched, turning signals and anomalies into action rather than queue items
• Business Network Assistant: Extends this coordination outward across suppliers, logistics providers, and service partners so execution doesn鈥檛 stall at the edges of the enterprise
• Logistics Assistant: Keeps warehouse and transportation execution moving as conditions change, coordinating agents rather than waiting for human handoffs at every step
• Manufacturing Assistant: Connects shop floor signals with broader operational context so teams can act on disruptions faster
• Planning Assistant: Helps planners stay ahead of exceptions and constraints without having to manually piece together signals from across the network
• Product Design Assistant: Helps engineering and manufacturing teams stay aligned as products evolve, surfacing the downstream implications of changes before they create rework or delays

From assistants to autonomous agents

In addition to these assistants, 麻豆原创 is delivering more than 60 purpose鈥慴uilt agents across supply chain processes. These agents are designed to sense events, analyze impact, and take guided action within defined business guardrails, helping coordinate execution while keeping people firmly in control.

In manufacturing, agents such as the Production Excellence Agent and Production Master Data Readiness Agent continuously monitor production, quality, and machine signals to detect issues early and keep routings and work instructions aligned with enterprise plans. In asset and service operations, the Asset Performance Alert Processing Agent and Technician Briefing Agent are designed to assess asset conditions, prioritize work, and increase first time fix rates, helping reduce downtime and improve responsiveness.

Beyond supply chain-specific scenarios, these assistants and agents will also extend into 麻豆原创’s cloud ERP environment, including , supporting 麻豆原创鈥檚 broader Autonomous Enterprise strategy. General availability will be phased through 2026, starting now.

Building on this foundation, 麻豆原创 Industry AI adds industry-specific intelligence that complements the core assistants. Rather than standalone features, Industry AI brings together purpose-built agents, process expertise, and business data to drive measurable outcomes. This value-led approach helps organizations apply AI in ways that reflect regulated requirements, complex production models, and asset-intensive operations 鈥� accelerating information across entire industry value chains.

People remain responsible for strategy, oversight, and the decisions that require judgement. What changes is how consistently high-volume, time-sensitive coordination happens across the supply chain.

Where this shows up in practice

The Autonomous Enterprise is our vision, and the innovations we鈥檝e announced at 麻豆原创 Sapphire are concrete steps that customers can build on within current 麻豆原创 environments. They are focused on addressing value leakage caused by fragmented handoffs, delayed decisions, and manual work.

In planning, new capabilities will connect commercial decisions directly with supply planning, linking promotion and pricing plans to inventory and replenishment to reduce stockouts, minimize write-offs, and improve planning consistently. New capabilities include vendor-managed inventory, transportation load building, deployment optimization, and co- and by-product planning.

In manufacturing and engineering, updates to will strengthen compliance and traceability in regulated environments. AI capabilities in the engineering-to-manufacturing handover will help teams understand the downstream impact of design changes before they reach the shop floor, surfacing implications for bills of materials, routings, lead times, and costs directly in context.

In , new Joule Agents will support execution-level decisions across warehouse and transportation operations, validating inbound receipts, aligning labor with real workload, and helping organizations respond faster to shifting constraints. Predictive labor planning in will allow operations teams to anticipate workforce needs rather than react to gaps.

In asset and service management, a new 麻豆原创 Field Service and Asset Management solution will bring planning, scheduling, dispatching, and field execution together in a single experience, connected to so work execution, parts usage, and costs stay aligned across service, operations, and finance.

These capabilities will become available in phases through 2026, aligning with customers鈥� existing 麻豆原创 landscapes. Together, they represent incremental but meaningful progress toward more connected, automated, and resilient supply chain operations.

The path forward

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. As AI becomes embedded in execution, supply chain teams spend less time monitoring and firefighting, and more time shaping decisions, managing trade-offs, and building resilience.

This shift is bigger than any single organization. In a new white paper,聽, we explore how leading organizations are moving beyond isolated AI pilots toward AI embedded across end-to-end supply chain processes, and what it takes to get there. This article draws on multiple sources, including analytical support from McKinsey & Company.

That鈥檚 the direction we are moving, from reacting toward supply chains that anticipate, absorb, and adapt. What we鈥檙e introducing at 麻豆原创 Sapphire reflects that commitment.For more details on all announcements made this week, please refer to the .

Dominik Metzger is president and chief product officer of 麻豆原创 Supply Chain Management.

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

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

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

The shift from periodic planning to continuous steering

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

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

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

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

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

Why governed data and connected planning matter

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

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

Continuous planning in practice

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

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

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

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

The future of finance is continuous

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

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

For more details, refer to the and the .

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

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

The conversation has moved beyond what AI can do into how it should be applied, placing HR at the center of decisions that will shape people, culture, and business outcomes for years to come.

While we have often talked about the 鈥渇uture of work,鈥� the simple fact is that future is already here. The question is whether organizations are ready to operate differently.

AI requires a fundamental rethinking of how work gets done, grounded in the data, systems, and processes that run today鈥檚 organizations. And getting it right starts with one clear principle: humans must remain firmly at the center鈥攏ot as operators of process, but as leaders of judgement, strategy, and change.

What Autonomous HCM means for HR leaders

At 麻豆原创, this is the foundation of our vision for the Autonomous Enterprise, announced at in Orlando, where AI assistants can run core HR processes end-to-end, so people are empowered to focus on their most meaningful work while staying firmly in control of outcomes.

brings together agentic AI, HR applications, and real business context鈥攇rounded in deep process expertise and enterprise-grade governance鈥攖o help organizations anticipate workforce needs and respond with greater precision as business priorities change.

With the new HCM innovations announced at 麻豆原创 Sapphire, we are building on the existing breadth and depth of 麻豆原创 SuccessFactors with new AI-native functionality that amplifies how HR can help shape the business and elevate what employees are capable of.

Automate work with Joule Assistants

The first shift is automation; not as task replacement, but as a new way of working. A new generation of , delivered through Joule as 麻豆原创鈥檚 AI engagement layer, bring this to life by orchestrating agents to execute work end-to-end and support decisions in real-time.

These assistants are not just automating tasks; they are guided by employees to reduce manual effort and support a growing range of HR scenarios:

• Payroll becomes proactive, not reactive: The coordinates multiple to prepare payroll runs, identify issues early, and guide administrators to faster resolution, shifting payroll from reactive process to proactive execution. Working alongside the Core HR Assistant and , it helps organizations manage employee data, track time and attendance, and pay employees with greater accuracy and less manual work.
• Talent acquisition flows more seamlessly end-to-end: The helps keep hiring moving from intelligent matching to interview coordination, providing real-time guidance to recruiters and hiring managers. Once a candidate accepts, the takes over to support a smooth transition for new employees. These new Joule Assistants connect talent acquisition processes between and the broader 麻豆原创 SuccessFactors HCM suite.
• HR services become faster and more intuitive: The helps administrators resolve common HR questions instantly, directing employees to the right next step and reducing service center volume while improving the overall employee experience.

Reimagine the workforce with AI-driven planning

As AI becomes part of how work gets done, organizations must rethink workforce planning as a continuous leadership discipline, not a periodic exercise. Today, 62% of C鈥憇uite executives say they are dissatisfied with how well people data connects to business performance, according to , making it harder to turn strategy into action. The new workforce planning capability within 麻豆原创 Enterprise Planning supports a shift toward strategic work redesign, inclusive of both agents and people, by helping leaders link workforce decisions directly to HR, business, and financial needs.

This workforce planning capability connects data from , , and 麻豆原创 SuccessFactors, creating a unified foundation for workforce decision鈥憁aking across employees and contingent labor. Together, this moves workforce planning beyond static models. Leaders gain clear scenario insight and the ability to combine human judgment with AI to align workforce and investment decisions.

At a more granular level, constant change means business and HR leaders are often dealing with organizational changes. The new AI鈥慹nabled organizational modeling for replaces slow, disconnected modeling approaches with an integrated experience that supports scenario planning and impact analysis, enabling leaders to evaluate organizational choices with greater accuracy and alignment. With this approach, leaders can quickly explore alternative organizational structures and understand implications before changes are implemented. Whether adjusting roles, teams, or reporting lines, organizational modeling becomes a practical leadership tool, supporting thoughtful change while maintaining data integrity and minimizing disruption. The result is a clearer, more proactive approach that helps organizations make smarter workforce decisions in a constantly evolving business landscape.

Elevate people through continuous upskilling

When it comes to skills, the rise of generative AI has once again accelerated the pace of change. New jobs are emerging, new skills are required, and processes that have worked for decades are being completely reimagined.  The new Workforce Upskilling Assistant delivers personalized, AI-driven learning directly where work happens, in collaboration tools, mobile, desktop and 麻豆原创 SuccessFactors鈥攈elping organizations keep skills aligned with where the business is headed. By orchestrating multiple Joule Agents, it supports content creation and generation, adaptive micro-learning, and reinforcement, enabling leaders and managers to identify critical skill gaps and accelerate upskilling, particularly in fast-moving areas such as AI.

By delivering learning in the tools and channels employees already use, the Workforce Upskilling Assistant turns workforce and business data into timely, bite鈥憇ized learning moments. Rather than relying on scheduled courses or standalone systems, HR learning teams can quickly convert existing content to deliver learning to the right person at the right time.

A new standard for human-centered Autonomous HCM

麻豆原创鈥檚 Autonomous Enterprise vision sets a new standard for how HR leads in an AI-driven world, one where AI assistants and agents take on the work of coordination, so people can focus on leading and shaping outcomes. As AI becomes embedded into how work runs, HR is uniquely positioned to guide what matters most, moving from coordinating processes to guiding decisions, building resilient teams, strengthening trust, and ensuring the workforce is ready for what鈥檚 ahead.

That is the promise of an Autonomous HCM platform: human expertise elevated by AI, delivering meaningful impact for both people and the business.

Learn more about how 麻豆原创 is delivering Autonomous HCM by catching the replay of the HCM Innovation .

Dan Beck is general manager and chief product officer for 麻豆原创 SuccessFactors.

AI is what brings that ambition within reach. It helps companies act faster, personalize at scale, and engage in new ways. But it is also raising expectations. Every interaction now reflects how well the business runs.

When a customer places an order or asks for help, the experience depends on what happens behind the scenes. If pricing is inaccurate, inventory is uncertain, or fulfillment falls short, the experience breaks.

That is why customer experience is now defined by execution. Customers do not experience systems or intent. They experience outcomes.

Agentic AI can increase speed, intelligence, and personalization. But speed alone does not improve customer experience. It amplifies what is already there. When execution is aligned with process, data and governance, AI drives better outcomes. When it is not, AI exposes the disconnect.

Aligning experience and execution

Autonomous CX brings agentic AI directly into the processes that run the business instead of layering it on top of disconnected systems. It connects AI assistants across marketing, commerce, sales, and service onto a shared business context across 麻豆原创 CX, 麻豆原创 Cloud ERP, supply chain, and connected systems. Orders, inventory, pricing, and financials are defined once and used consistently, so decisions are based on live operational reality.

At the center of this shift are AI assistants and autonomous agents. Assistants coordinate multiple agents across end-to-end customer workflows, from discovery to fulfillment, engagement to service, and issue to resolution.

At 麻豆原创 Sapphire, we highlighted assistants that make this real across the portfolio:

• In marketing, Content Assistant and Campaign Assistant orchestrate intent understanding, content creation, segmentation, optimization, and campaign execution within governance controls.
• In commerce, Merchandising Assistant, Shopping Assistant, and Order Management Assistant connect discovery, conversion, and fulfillment to operational reality.
• In sales, Sales Assistant, Deal Qualification Assistant, and Deal Closing Assistant move sellers from signal to execution.
• In service, Case Management Assistant and Service Management Assistant improve resolution and service quality, with additional assistants purpose-built for self-service, HR service, and accounts receivable workflows.

AI-driven discovery and engagement grounded in business reality

麻豆原创鈥檚 collaboration with Google follows the same principle: connect AI-driven discovery and engagement to business execution.

Together, 麻豆原创 and Google are focused on three priorities: first, applying the latest AI models, including Gemini, to deliver high-quality customer experiences; second, supporting industry standards and open protocols to enable interoperability across ecosystems; third, enabling seamless, personalized journeys across channels and Google surfaces such as Shopping and Gemini.

By combining 麻豆原创鈥檚 governed business data with Google鈥檚 AI capabilities, assistants and agents can connect customer intent from storefronts, search, and AI-driven channels to 麻豆原创 commerce and order processes. This ensures that what customers see reflects what the business can fulfill.

This is also why 麻豆原创 is adopting and expanding how 麻豆原创 product data can power AI-driven experiences wherever customer intent originates. This keeps experiences aligned with pricing, inventory, and fulfillment in real time.

麻豆原创 Commerce Cloud innovations

麻豆原创 continues to be recognized in analyst evaluations, including the Gartner庐 Magic Quadrant™ for Digital Commerce, where 麻豆原创 has been positioned as a Leader for 11 consecutive times.

, trusted by the largest enterprises, now extends to mid-market and growing companies on 麻豆原创 Cloud ERP. The new 麻豆原创 Commerce Cloud, cloud ERP edition delivers a standardized, end-to-end approach, reducing complexity, leveraging AI natively, and accelerating time to value. It connects discovery through fulfillment via tight integration with 麻豆原创 Cloud ERP.

For digitally mature organizations, 麻豆原创 is expanding composable commerce with new and modular cart and checkout services. These services integrate with core processes such as pricing, promotions, loyalty, tax, payments, inventory, sourcing, and order management across 麻豆原创 and non-麻豆原创 touchpoints. This helps organizations modernize their architecture while maintaining end-to-end execution.

麻豆原创 is also expanding its ecosystem with Vercel to accelerate storefront development and deployment with optimized performance, scalability, and composable front-end experiences.

In payments, 麻豆原创 Unified Payment, powered by Adyen, embeds global processing directly into the commerce flow to simplify integration and improve conversion. 麻豆原创 also continues to enhance its open payment framework with pre-integrated providers, such as Checkout.com and PayPal, giving customers flexible provider choices that are easy to configure and use.

Together, these capabilities reduce total cost of ownership, speed deployment, and make it easier to deliver better experiences at scale.

Sales execution turns insight into action

Customer experience extends into sales execution, where teams need clear next steps and confidence those actions can be fulfilled.

We introduced new innovations, including field sales capabilities for retail execution processes in consumer goods companies and other field-selling environments. These capabilities provide rich mobile experiences that work offline, making it easier to plan store visits, capture in-store activity, and manage execution in real time.

Sales leaders gain connected insights tied directly to pricing, inventory, and order processes, leading to more consistent execution and better outcomes.

Scaling trusted autonomous service

Autonomous CX is strengthened through partnerships that extend execution while preserving trust and governance.

Our combines its agentic AI-driven voice and digital self鈥憇ervice with service, order, and entitlement data from 麻豆原创 Service Cloud. AI-driven automation can handle routine interactions with full context, escalating seamlessly and with continuity to service teams when human expertise is needed. This approach helps organizations scale service without breaking trust and ensures customer interactions remain connected to real business processes.

麻豆原创 is also expanding its partnership with Amazon to scale AI-driven service across voice and digital channels, enabling faster, more consistent resolution while keeping service execution grounded in real-time business data.

Industry AI in action

We are also showcasing Industry AI scenarios that demonstrate how assistants and autonomous capabilities operate in real business environments.

Autonomous Revenue Growth Management supports trade planning teams and key account managers in consumer products companies that sell through retailers, with applicability to agribusiness and wholesale distribution. Industry鈥憇pecific Joule Assistants provide AI鈥慸riven insights across trade planning and execution, helping teams identify growth opportunities, optimize commercial terms and respond more quickly to performance signals. The result is more predictable growth with fewer downstream exceptions.

Unified commerce supports merchandising and operations teams across retail, wholesale, and direct-to-consumer models. Unified commerce connects demand, inventory, and customer data across channels, with Joule Assistants guiding decisions on assortment, pricing, and placement. The result is more consistent execution and faster decisions.

The next phase of customer engagement

Across these innovations and Industry AI scenarios, the pattern is clear. AI delivers value only when it acts on shared, trusted context. When experience and execution stay aligned, speed becomes a source of trust instead of risk.

This is how 麻豆原创 is approaching the future of customer experience: as a coordinated system where every decision is visible, and every promise can be kept.

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

The CX innovations and Industry AI scenarios highlighted here are planned for general availability in Q3 2026.
The capabilities announced as part of 麻豆原创鈥檚 Autonomous Enterprise run across 麻豆原创 Cloud ERP, including 麻豆原创 Cloud ERP Private.
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.

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

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

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

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The business AI imperative

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

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

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

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

ERP as the foundation for business AI

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

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

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

麻豆原创 Business AI Platform

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

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

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

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

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

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

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

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

Industry AI: H&M and Sector-Specific Transformation

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

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

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

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

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

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

Closing: The Autonomous Enterprise

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

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

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

麻豆原创 Business Data Cloud (麻豆原创 BDC) is the data foundation of that platform, the business data fabric that anchors universal business context, serving as the trusted knowledge core聽for every enterprise application and agent.聽

The future of agentic organizations will be driven by AI with the deepest organizational knowledge. That future doesn’t start with AI models; it starts with whether your data foundation can give agents the business context they need to act autonomously. 

Today, we are introducing innovations that move organizations closer to becoming an autonomous enterprise.

Turn all your data into business outcomes 

A business data fabric architecture ensures every agent, application, and decision draws from the same trusted business context. And today, we are introducing new business data fabric capabilities that bring multi-model, unified master data, and embedded governance to your agentic foundation. 

• 麻豆原创 HANA Cloud natively available in 麻豆原创 Business Data Cloud:聽麻豆原创 HANA Cloud聽is聽now a聽core聽component of 麻豆原创 Business Data Cloud.聽As the AI database聽for 麻豆原创 BDC,聽麻豆原创聽HANA Cloud provides a聽unified聽in-memory engine聽for agents to reason across transactional, analytical, and multi-model workloads聽such as spatial, graph, and vector.聽In practice, this means agents can navigate relationships across customers and suppliers, analyze geographic dependencies, or perform semantic search in real time.聽And because every workload runs on a single in-memory engine with native workload management, inference time drops dramatically, lowering TCO and improving the predictability of AI聽costs.聽With 麻豆原创 HANA Cloud, 麻豆原创 Databricks, and 麻豆原创 Snowflake, 麻豆原创 Business Data Cloud delivers聽intelligent compute for聽every data and AI workload.
• Reltio in 麻豆原创 Business Data Cloud: With the completed acquisition of Reltio, 麻豆原创 is bringing multi-domain master data management capabilities directly into 麻豆原创 Business Data Cloud, helping customers unify, cleanse, and harmonize data across 麻豆原创 and third-party聽sources. Reltio鈥檚 AI-based entity resolution identifies and merges related records聽into a single, consistent view of business entities.聽Low-latency delivery and Model Context Protocol support enable real-time, multi-agent workflows across聽your data landscape: a procurement agent, for example, can assess supplier risk and trigger action almost instantly using trusted, real-time data. Together, this becomes a golden record system of context that Joule Agents use to deliver faster time-to-value for business AI.
• 麻豆原创 Master Data Governance natively available聽in聽麻豆原创 Business Data Cloud:聽Unified master data is only as valuable as the governance applied to it. To ensure data is AI-ready, governance must聽shift聽from regulator to value accelerator. 麻豆原创 Master Data Governance is now a core component of 麻豆原创 Business Data Cloud, governing master data and policies across聽your聽business data fabric.聽This results in聽embedded聽AI governance that accelerates agent deployment, ensuring every agent operates on data聽products聽that聽are聽verified聽and aligned to your business policies.聽
• 麻豆原创 AI Core integration with 麻豆原创 Business Data Cloud: 麻豆原创 is introducing deeper integration between 麻豆原创 Business Data Cloud and 麻豆原创 AI Core, enabling AI models to be grounded directly in trusted business data, semantics, and governance. Batch inference can now be embedded into business-ready data products, continuously enriching the data that powers Joule with predictions, classification, and聽AI聽outputs.聽聽

“This is where 麻豆原创 Business Data Cloud fits into the vision: not as a centralized system, but as an enabler of cultural change through its unique capabilities. These capabilities allow teams to preserve mission-critical business context across financial and non-financial data.”

Jannie Affeld, VP Finance Systems and ERP, Google 

Transform outcomes with Joule Agents 

麻豆原创 is bringing agentic AI directly into the business data fabric through Joule Agents, introducing new capabilities that streamline data management, analytics, and planning through a conversational experience: 

• Data product search and creation: Joule Agents simplify how users discover and create data products. With natural language prompts, users can identify relevant 麻豆原创 and third-party data sources, perform joins and transformations automatically, and apply business context and governance policies.  
• Automated planning and analytical modeling: Joule Agents enable data modelers and planning teams to generate analytical and planning models using AI. By defining dimensions, granularity, and data sources, users can automatically create models aligned with best practices. Teams can also initiate planning cycles, manage versions, and apply calculations without deep technical expertise. 
• Easily聽surface business聽insights:聽Business users聽can聽ask complex analytical questions in natural language and receive context-aware insights across lines of business. Powered by governed data products聽in 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph,聽Joule聽understands relationships, processes, and business logic聽to deliver聽more accurate and complete answers without requiring manual exploration.
• 麻豆原创 Analytics Cloud story generation: Joule accelerates 麻豆原创 Analytics Cloud story creation by transforming data models, queries, and business context into dashboards and visualizations automatically. Users can continue the conversation, drilling into KPIs, identifying drivers, and exploring trends in a single workflow. 

Extend context across your open data ecosystem

Last year, we introduced 麻豆原创 BDC Connect, a capability to share data and metadata with zero copies, preserving meaning across every cloud and platform. We are excited to announce 麻豆原创 BDC Connect for Amazon Athena, continuing our promise of openness and choice.

This enables 麻豆原创 data products to be discovered and consumed directly within AWS without replication or loss of context. As a result, teams can build analytics, applications, and AI agents faster while ensuring they operate on trusted, governed business data.

Together with existing partners across Snowflake, Databricks, Google BigQuery, and Microsoft Fabric, 麻豆原创 Business Data Cloud delivers a connected, open data ecosystem so organizations can extend business context across their entire landscape with zero copies. 

General availability is planned for H2 2026.

“Compute can happen anywhere, data can stay at the source when needed, but business context is managed once, centrally, in 麻豆原创 Business Data Cloud.”

Malin Persson, CIO at Ericsson

Get started today 

Build your trusted foundation for agentic AI with 麻豆原创 Business Data Cloud.  

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

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

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

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

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

Introducing 麻豆原创 Business AI Platform

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

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

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

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

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

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

Designing the Autonomous User Experience

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

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

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

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

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

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

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

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

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About 麻豆原创

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

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Building on their long-standing partnership, AWS and 麻豆原创 are making it easier for customers to access mission-critical 麻豆原创 data products across AWS services, including Amazon Bedrock, Amazon Quick, and Amazon SageMaker. This will allow teams to deliver self-service analytics and build AI agents across all lines of business鈥攚ithout waiting for IT teams to replicate, prepare, and provision 麻豆原创 data. 

鈥淭he next era of business will be defined by how well organizations turn intelligence into action at scale,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering. 鈥淏y bringing together 麻豆原创 Business Data Cloud and widely adopted AWS AI and analytics capabilities, customers can unlock the true potential of data and AI.鈥� 

Enabled by 麻豆原创 BDC Connect, the integration delivers near real-time, zero-copy access to semantically rich 麻豆原创 data products directly through Amazon Athena, keeping data in place, preserving its original business context, and eliminating replication delays. Customers can query and analyze this data immediately, or choose to store and transform it for use across the broader AWS environment. This gives teams a governed, secure environment within AWS to easily build reports, dashboards, and AI agents.

“麻豆原创 and AWS share a commitment to helping customers put their most valuable data to work,” said Ruba Borno, vice president of Global Specialists and Partners at AWS. “By combining 麻豆原创 Business Data Cloud with the AWSsecure, global infrastructure and advanced AI services, organizations can unlock mission-critical 麻豆原创 data and act on it at the speed and scale their business demands.” 

With 麻豆原创 BDC Connect for Amazon Athena customers can: 

• Create a single, trusted foundation for all their business data, 麻豆原创 and beyond 
• Get AI up and running faster with business data that is ready to use 
• Build intelligent applications and agents powered by the business data that matters most 
• Fast-track insights and innovation with self-service analytics that reduce time-to-insight from weeks to hours 

General availability

麻豆原创 Business Data Cloud is already available on AWS in the U.S. East (N. Virginia), Europe (Frankfurt), Asia Pacific (Tokyo), Canada (Central), Asia Pacific (Sydney), South America (S茫o Paulo), Asia Pacific (Seoul), Asia Pacific (Singapore) Regions, and the AWS European Sovereign Cloud.