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.

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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.
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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.

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

Global transformation, local anchoring as a guiding principle

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

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

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

How a federal agency demonstrates effective change management

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

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

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

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

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

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

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

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

From shadow AI to structured integration at KIT

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

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

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

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

What research confirms

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

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

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

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

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

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

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

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

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

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.

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

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

Partnership built for financial services innovation

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

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

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

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

AI is not the starting point, data integration is

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

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

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

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

Financial services can no longer afford 鈥減atchwork architecture鈥�

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

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

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

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

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

Real-time finance is becoming a strategic requirement

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

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

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

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

AI will reward those who simplify

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

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

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

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

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

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

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

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

.

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

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

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

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

From manual reporting to proactive decision intelligence

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

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

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

A collaborative architecture for scalable AI adoption

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

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

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

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

A blueprint for other businesses

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

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

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

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

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

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: .

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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.
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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.

Visit the聽. Get 麻豆原创 news via聽 and .

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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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.

鈥淓very motorcycle we sell comes with an experience,鈥� he explains. 鈥淐ustomers come back to us for service, for spare parts, and for workshop support, so we truly connect each customer to a different kind of user experience, starting from the moment of purchase.鈥�

Many people in Colombia get around every day on motorcycles鈥攐ften on 鈥楤oxer鈥� motorcycles made by UMA, which aims to ensure riders feel safe on the road, Urrea Ospina says. To help achieve that, UMA has implemented rigorous quality processes that run on . 鈥淓very motorcycle leaving our factories follows best quality practices,鈥� he says. 鈥淎nd we pass that value onto our customers.鈥�

In addition to Colombia, UMA operates in five countries in Central America: Guatemala, El Salvador, Costa Rica, Honduras, and Nicaragua. 鈥淲e鈥檙e also in Venezuela, as well as Spain and Portugal,鈥� Urrea Ospina says. 鈥淎cross all these regions, we aim to make a positive impact on our customers with each of our brands and motorcycles.鈥�

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鈥淔or us, our 鈥楢dventure鈥� project鈥攖he name we gave the 麻豆原创 project鈥攚as crucial to our expansion and growth,鈥� he says. 鈥淲e needed global models so we could expand faster and run more efficient, automated operations.鈥�

Working with 麻豆原创, UMA established global models and automated operations across finance, production, and logistics, says Victor Bedoya Aristizabal, corporate solutions manager at Grupo UMA. 鈥淲e defined global models, starting in finance with global charts of accounts that let us analyze information and make financial decisions much more efficiently. From there, we extended the approach through production and plant operations and into logistics.鈥�

鈥淭oday, 麻豆原创 is a fundamental pillar for our processes from the start of the value chain to the end,鈥� Bedoya Aristizabal says. 鈥淲e utilize the full suite for production, pricing, finance, and warehouse management to deliver a superior product.鈥�

鈥淭aken together, these cross-cutting global modules have helped us evolve, save time, shorten implementations, and expand across regions,鈥� Urrea Ospina says. 鈥淔or example, in Colombia we鈥檙e implementing our spare parts warehouse with 麻豆原创 Extended Warehouse Management, 麻豆原创鈥檚 supply chain management application. What we鈥檙e learning in Colombia can be replicated in Central America. Standardizing these logistics modules makes operations much more efficient while still accounting for each country鈥檚 specific needs.鈥�

He adds: 鈥淚 believe that if we hadn鈥檛 prepared with 麻豆原创 and the technologies we have today, the strong growth we鈥檝e had in Colombia would have been far more difficult. For example, we went from assembling 11,000 motorcycles in January of last year to around 17,000 motorcycles per month today鈥攏early doubling our output. Without 麻豆原创鈥檚 well-defined structures, process controls, and automations that replaced a lot of manual work, it would be much harder to reach the 20,000 motorcycles per month we expect to achieve soon.鈥�

鈥淭hat ability to scale鈥攂oth assembly volume and sales volume鈥攈as been driven by the Adventure project and everything we implemented with 麻豆原创,鈥� he says. 鈥淚 think it has significantly improved employees鈥� day-to-day work. And that鈥檚 where we need to take the organization: toward a data-driven company that makes better decisions. Before we implemented Adventure, a person鈥檚 day-to-day was mostly operational work.鈥�

Urrea Ospina emphasizes that one of the main motivations UMA had for adopting 麻豆原创 Cloud ERP Private was to have concrete, real-time information鈥攆or example, knowing exactly the assembly cost of a motorcycle. 鈥淭hat is key for an organization,鈥� he says. 鈥淏eyond that, another need was to remove the manual work that all the company鈥檚 areas had, working and operating completely by hand.鈥�

Luis Orrego, production supervisor at Grupo UMA, agrees: 鈥淚nstead of having many Excel files open with multiple sheets and trying to reconcile everything, we now consolidate the data in one place. With 麻豆原创, all the sheets and reports I used to present are now available in a single view. I don鈥檛 have to run multiple steps, and I can see all my indicators: capacity, materials, and line resources.鈥�

Another motivation was UMA鈥檚 expansion plans to enter other regions and countries efficiently. 鈥淲e wanted defined global models that we could replicate quickly with 麻豆原创 in the regions where we operate,鈥� Urrea Ospina explains. 鈥淭hose factors led us to undertake the 麻豆原创 project, which has been a highly strategic and very successful initiative.鈥�

Implementing 麻豆原创 Cloud ERP Private has also had a profound impact on Urrea Ospina鈥檚 technology team. 鈥淚t completely changed our roles and what we focused on,鈥� he says. “We used to be a technology team focused on operations, not one that added value. Today, the technology team plays a strategic role. We have time to analyze where we鈥檙e going and what we can implement next for the business. Culturally, 麻豆原创 gave us confidence through information traceability, and it also gave us time to think. It opened the door to innovation鈥攅specially around analytics.鈥�

David Aguirre is a media production specialist on the Multimedia Team for 麻豆原创 News.
Rana Hamzakadi is deputy head of the Multimedia Team for 麻豆原创 News.

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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Bel茅n Martinez Mill谩n, 麻豆原创 Spain, +34 91 4567220, belen.martinez@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.

In an industry rapidly shifting toward AI-powered operations, Martur Fompak International saw an opportunity to reimagine its material flow through the strategic implementation of technology. Building on the efficient, people-driven processes it already had in place, the company partnered with 麻豆原创 and Humanoid鈥攁 UK-based robotics and AI company鈥攖o explore how integrating embodied AI鈥損owered robotics could redefine material flow across its automotive manufacturing environment. Using Joule and embodied AI capabilities from 麻豆原创, Martur Fompak International now connects production signals and business context directly to autonomous execution, creating a context-aware automation system that prioritizes, picks and delivers materials while adapting in real time to changing business conditions.

Built on 麻豆原创 S/4HANA and enabled by the 麻豆原创 Extended Warehouse Management application, the solution enriches humanoid robots with real-time knowledge of tasks, attributes and exception handling. Guided by material data, storage locations, sequencing and production priorities provided via embodied AI, humanoid robots execute material flows across a live automotive manufacturing environment鈥攊dentifying, transporting and delivering materials to the line while continuously confirming back into 麻豆原创 solutions. Together with autonomous mobile robots (AMRs), the company has created a fully automated, scalable material flow that boosts throughput, improves accuracy and reduces reliance on manual coordination. By assigning repetitive, non-value-adding and physically demanding tasks to robots, Martur Fompak International is enabling its people to focus on safer, more meaningful and higher-value work that drives productivity and innovation.

鈥淥ur humanoid robot collaborates with digital production systems to ensure seamless coordination across order management, logistics and production, enabling scalable AI adoption and improving efficiency, consistency and operational resilience,鈥� said 脰zlem Alt谋n谋艧谋k, Group Intelligent Technologies Director at Martur Fompak International. 鈥淭he deployment of our humanoid solution, powered by an embodied AI layer and enabled through the Joule Studio solution, proves that combining cognitive autonomy with physical automation can transform execution, accelerate decisions and scale intelligent enterprise capabilities across the organization.鈥�

鈥淢artur Fompak International exemplifies what it means to turn AI ambition into real business value on the shop floor,鈥� said Emmanuel Raptopoulos, Chief Revenue Officer, EMEA, MEE and APAC, 麻豆原创 SE. 鈥淏y embedding 麻豆原创 Business AI directly into their physical operations, they are not only boosting throughput and operational resilience鈥攖hey are setting a new standard for what an intelligent, AI-first factory looks like. This is exactly the kind of end-to-end transformation that defines the future of manufacturing. We are proud to congratulate Martur Fompak International on being named the sole winner in the AI Excellence category at the 2026 麻豆原创 Innovation Awards鈥攁 testament to their boldness in turning intelligent enterprise vision into real-world impact.鈥�

Early results show increased throughput, fewer errors and a scalable, AI-driven intralogistics model. A future target of up to five times greater work efficiency has been set for mass production, with work orders expected to be completed faster, more consistently and with greater precision across production flows. With 400 daily production line feeds and 100% 麻豆原创 software鈥揹riven decision making already in place, Martur Fompak International is advancing beyond traditional automation, pioneering a scalable, intelligent factory that represents a new standard for the automotive industry.

Looking ahead, Martur Fompak International plans to further expand its autonomous operations across additional production lines, leveraging 麻豆原创 Business Technology Platform to scale AI-driven workflows and integrations鈥攕upporting both operational efficiency goals and broader sustainability commitments.

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Media Contact:
Ekin Tayali, +34 673019169, ekin.tayali@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.

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.

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�

The autonomous enterprise reflects a fundamental shift in how organizations operate using real鈥憈ime intelligence to guide decisions, orchestrate processes end to end, and continuously adapt as conditions change. AI becomes embedded into the fabric of the enterprise, helping every function operate with greater speed, resilience, and confidence.

The foundation of the autonomous enterprise is the 麻豆原创 Business AI Platform, which infuses AI with the process knowledge, data, and governance organizations depend on. 

Business Transformation Management solutions from 麻豆原创 help power the 麻豆原创 Business AI Platform by bringing together insights and enterprise knowledge that have long been fragmented and isolated in silos.

Business Transformation Management solutions from 麻豆原创 help deliver the promise of the autonomous suite. Here鈥檚 how.

麻豆原创 Agent Hub: Command center for agentic governance

Now available, the helps organizations discover, inventory, govern, and evaluate AI agents across the enterprise landscape. In fact, it鈥檚 already being used by 150 companies with over 100, 000 agents under management. 麻豆原创 AI Agent Hub acts a system of records for all AI agents, large language models (LLMs), and Model Context Protocols (MCP) servers.

In the context of 麻豆原创 Business AI platform, 麻豆原创 AI Agent Hub underpins the governance pillar, ensuring organizations can deploy and manage AI agents safely and at scale.

In addition to the enterprise architecture context that 麻豆原创 LeanIX provides, along with an giving agents access to architecture data, 麻豆原创 AI Agent Hub enables enterprise architects to apply proven governance practices, such as mapping to business capabilities, to the entire agentic landscape. The addition of agent mining capabilities supported by 麻豆原创 Signavio provides visibility into the behavior of AI agents, their conformance with policies, and their business impact.

From the standpoint of the Autonomous Enterprise, the insight the hub provides is not only necessary, it鈥檚 critical.

New AI capabilities

The new Enterprise Architecture Assistant from 麻豆原创 LeanIX is supported by several new agents, including two highlighted here. The Enterprise Content Research Agent draws on internal business content to enrich architecture data, while the Enterprise Architecture Web Research Agent scans the web for relevant vendor and application information.

These enhancements are part of a broader set of AI capabilities in 麻豆原创 LeanIX. The solution now makes it easier to create surveys, automate tasks, perform calculations, and plan target architectures. In addition, significantly improved semantic search enables Claude, AI co鈥憄ilots, and other agents to seamlessly access and work with enterprise architecture data.

In 麻豆原创 Signavio Process Transformation Suite, we redesigned 麻豆原创 Signavio Process Modeler with an AI-first architecture, modernized user experience and deeper integration with 麻豆原创 Autonomous Suite. 麻豆原创 Signavio also introduced the Process Transformation Assistant to enable business users to conduct sophisticated process analysis through natural language prompts. The assistant can identify high-impact opportunities for agent deployment, accelerating the time from question to decision and providing context-aware process insights to anyone.

Looking ahead to a new paradigm

Despite the rapid pace of change brought about by agentic AI, we are still in the early days of this technological revolution. To succeed and continue to ride the wave of innovation, companies need to aggregate and organize their procedural knowledge about how they operate.  This knowledge is often fragmented across many structured and unstructured sources鈥攕uch as process models, application logic, documents, and chats鈥攖o create a coherent view of how the business s runs.

This foundation enables agents to understand and act within the business context. In turn, agents will continuously contribute back, enriching and evolving this knowledge repository over time.

At 麻豆原创 Signavio we call this storehouse 鈥渃ompany memory.鈥� Company memory, comprised in part of process atoms, captures all the knowledge of operational practices, business rules, preferences, and more so that it can be accessed by agents as needed to check conformance and change behavior.

To enable the Autonomous Enterprise, you need to capture the tribal wisdom and unstructured knowledge your company depends on to operate today. That is what process atoms and a centralized company memory, accessed and updated by agents, do for you. In the future, it鈥檚 hard to imagine how any enterprise will succeed without the context, learning, and guidance that company memory delivers.

Business transformation never stops

As our research has shown, . That鈥檚 why you need a capability in place that allows for planning, managing, and realizing value from every transformation you undertake.

This year at 麻豆原创 Sapphire , we talked about all the ways our solutions support this capability as well as all the ways our solutions continue to evolve in the era of the autonomous enterprise, allowing you to adapt, innovate, and thrive into the future.

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Andre Wenz is chief product officer of 麻豆原创 Signavio.
Dominik Rose is chief product officer of 麻豆原创 LeanIX.

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.

Employees at , a global fashion retailer with tens of thousands of employees, had to navigate multiple systems to piece together data across sales and procurement. Answering a single question could take up to 10 minutes.

Today, they just ask Joule. What used to take 10 minutes now takes about three seconds, driving a 70% increase in operational efficiency and a 50% reduction in manual errors.

Using capabilities in , LC Waikiki partnered with 麻豆原创 and to build a custom AI-driven experience that dynamically interprets user requests, applies role-based context, performs the necessary queries, and connects data across systems to present a complete view in one place. It then links people directly to the relevant transaction.

At 麻豆原创, stories like these inspire our vision for the enterprise in which AI transforms how people and processes work鈥攐ne where people set the direction and AI executes. We call it the the Autonomous Enterprise.

In the Autonomous Enterprise, decisions are grounded in real-time intelligence, workflows are automated end-to-end, and AI proactively improves every function while empowering people to do their best work.

The Autonomous Enterprise also provides fully governed AI you can trust, so you can achieve more. Making this a reality for companies is critical because AI is now essential to how all work gets done. It is increasingly involved in decisions that carry financial, operational, and regulatory consequences.

Joule: One place to direct the entire business

In the Autonomous Enterprise, Joule Work, announced at 麻豆原创 Sapphire, is the next step in the evolution of how people engage with and execute end-to-end business processes. Joule Work is a dynamic workspace that adapts to intent, keeps people focused on outcomes, and delegates execution to AI.

Through Joule Work, you can say goodbye to manually coordinating work across multiple applications and interfaces. Instead, tell Joule what you want to accomplish. Joule Assistants with role and process context will coordinate teams of Joule Agents to surface the right insights and automate routine work across departments and systems. Rather than static, disjointed systems, you get workspaces that pull together information and menus from various systems that fit your specific needs, in real time.

Joule Work is available now to customers in the 麻豆原创 Early Adopter Care program. 麻豆原创 Early Adopter Care program for the Joule Work desktop app is planned for Q2 2026; general availability for both is planned for H2 2026. The Joule Work mobile app is generally available now.

We also announced that Joule鈥檚 bi-directional Agent-to-Agent (A2A) capabilities will be generally available in Q4, enabling third-party agents to securely call on Joule Agents and act within enterprise processes, extending interoperability in both directions across 麻豆原创 and non-麻豆原创 environments. Agents built in Joule Studio will natively support A2A protocols, enabling interoperability and scalability for multi-agent execution.

麻豆原创 Autonomous Suite: The operational core of the modern enterprise

While Joule Work empowers every individual to do their best work and expand their impact, the 麻豆原创 Autonomous Suite transforms how entire business functions, or 鈥渁utonomous domains,鈥� work.

麻豆原创 Autonomous Suite spans five domains: finance, spend, supply chain, human capital management, and customer experience. These domains will operate as a single system, so workflows and agents run across functions without fragmenting into separate tools, separate data, or separate decisions. This approach allows AI recommendations to reflect your full operating reality.

With 麻豆原创鈥檚 integrated suite of business applications and industry-leading business data, AI in the Autonomous Enterprise is grounded in the specifics of how key business functions actually work. This foundational context for transformative AI outcomes is where 麻豆原创鈥檚 unique experience comes in. For decades, we have been trusted to run our customers鈥� most important functions. 麻豆原创 Autonomous Suite infuses our deep knowledge of business processes into your AI, along with the data context and operational guardrails it needs to be truly effective and reliable at enterprise scale.

Each organization is also unique. Over time, your business has defined how your work gets done. These are the rules, workflows, and how systems respond when something unexpected happens, like a failed transaction, so processes don鈥檛 break. In the Autonomous Enterprise, AI delivers its greatest value by respecting these boundaries, turning your unique ways of working into a true advantage.

At 麻豆原创 Sapphire, we announced new Joule Assistants and Joule Agents, spanning the domains of the Autonomous Enterprise, to help organizations move from managing work to directing outcomes. These new assistants and agents will roll out through the end of this year.

麻豆原创 Business AI Platform: The foundation of the Autonomous Enterprise

The 麻豆原创 Business AI Platform turns the vision of human-led, AI-driven business operations into something enterprises can build and run. It enables them to move from AI experimentation to execution by grounding agents and applications in real business context that governs it all at enterprise scale.

At the center is , a fully managed environment that empowers enterprises to build and manage the full lifecycle of AI agents, applications, extensions, and workflows. Intent-based development capabilities allow people to describe what they need in natural language. A Joule Agent then generates structured requirements, specifications, code, and test artifacts grounded in 麻豆原创 process and data context.

Developers can work within the tools they already use, including VS Code and MCP-enabled toolchains, and choose their preferred agent frameworks, such as , , and .

Through deep integration with the , 鈥攁nd the new 麻豆原创 Domain Models trained on 麻豆原创 code, customer data, metadata, and business processes鈥擩oule Agents reason over real, semantically rich enterprise data rather than generic knowledge. 麻豆原创 Domain Models are available through the 麻豆原创 Early Adopter Care program, with general availability planned for Q3 2026.

Speed and governance, no longer a tradeoff, are built into the 麻豆原创 Business AI Platform. At 麻豆原创, we believe that corporate governance鈥攊ncluding approval flows, compliance processes, identity management, and the ability to audit decision-making鈥攎ust carry into how AI is deployed, updated, and scaled. Joule Studio runtime provides a secure, production-ready, fully managed environment for deploying agents, helping organizations meet compliance standards while reducing infrastructure complexity.

An enhanced 麻豆原创 AI Agent Hub also provides a vendor-agnostic command center to discover, inventory, and govern 麻豆原创 and non-麻豆原创 AI agents and MCP servers across the enterprise. Integration with and further embeds governance and architecture transparency into the development process.

The 麻豆原创 AI Agent Hub leverages enterprise-wide process intelligence to continuously track where AI agents are creating value and can proactively surface where they can deliver even more, because we believe AI needs to remain accountable for outcomes in addition to uptime. 麻豆原创 AI Agent Hub is generally available, with additional capabilities rolling out through 2026. See release timelines in the .

Empowering everyone to solve business challenges with AI

We are making the Autonomous Enterprise a reality because at 麻豆原创, we believe that companies of all sizes need far more than marginally better AI models or the latest bolt-on solutions. They deserve AI-driven outcomes that increase innovation, revenue, and margins.

The Autonomous Enterprise is what brings our vision to life: AI grounded in your data, connected across your most important processes, and governed to fit how your business runs.

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