The data suggests that while organizations are becoming more thoughtful about trip economics, most are still prioritizing in-person meetings and employee travel.

Travel costs rose across most categories��

��According to �鶹ԭ�� Concur global data from January 1–May��31, 2026, overall business travel costs increased across��nearly every��major category year over year. Airfare rose��more than��8%,��hotel rates increased nearly 6%, and car rental costs climbed��roughly��5%.

At the same time,��the average cost of��fuel-related expenses surged.��The��average��transaction��in Concur Expense in the gas category��increased��approximately��22%��globally, rising from $50 in February to��$61��in��April��with similar spikes seen in countries around the world.��

Transportation patterns��shifted in select��segments

Those increases are already influencing some travel decisions. While overall air and hotel booking volumes remained relatively flat��year over year, there have been some shifts in how employees travel once they arrive at their destinations, as car rental bookings declined��roughly��4%��globally. Additionally, rail bookings increased approximately 4%, suggesting some organizations may be looking for more cost-effective or efficient transportation alternatives as fuel prices rise.

Premium travel demand remained strong

Even with costs rising, companies did not significantly reduce premium travel spending.��Premium cabin bookings—including business and first class—increased about 9% year over year. By comparison, economy bookings��remained flat, while premium economy bookings declined approximately 15%.

Companies��are��balancing��traveler��experience alongside budget pressures.��For longer flights and international trips in particular, some organizations��still��view premium travel as a worthwhile investment.

What to watch in��the second half of��2026

There��are early signs that rising costs and operational disruptions could begin affecting demand, with significantly higher average airfares and reduced airline capacity in some parts of the world. The coming months will help��determine��whether these disruptions create a short-term adjustment or shape a broader shift in business travel behavior. For now, the data suggests companies are willing to absorb higher travel costs rather than scale back travel plans.

Research shows that business travel��remains��closely tied to professional opportunity and relationship building.��In a����conducted in the U.S. on behalf of �鶹ԭ�� Concur,��90% of frequent business travelers say traveling for work has positively��impacted��their careers, underscoring the employee experience and retention benefits of continuing to prioritize business travel��even��as costs rise.

As��organizations navigate��higher travel costs in 2026,��the data suggests��many��still view��business travel as a worthwhile investment in relationships, employee development, and��long-term��growth.

Charlie Sultan is president of Concur Travel at �鶹ԭ��.

Methodology: �鶹ԭ�� Concur analyzed expense transactions��tagged��as “gas”��in��Concur Expense between January 1, 2026, through May 31, 2026, and equivalent time periods from 2025.���鶹ԭ�� Concur analyzed air, rail, hotel, and car bookings in Concur Travel for trips booked and undertaken between January 1,��2026��and May 31,��2026��and the equivalent��time period��in 2025.

As one of the��industry’s most trusted independent��peer review��platforms,��TrustRadius��is known for its rigorous verification process and commitment to unbiased, customer‑led insights. These��awards are based��on real��user experiences,��making��them��especially meaningful.

This recognition reinforces a clear message: organizations are turning to���鶹ԭ�� SuccessFactors solutions not just to manage HR but to modernize it. As companies move towards more autonomous, AI-driven ways of working, they need HCM solutions that bring together data, insights, and action.��That’s��exactly what the �鶹ԭ�� SuccessFactors portfolio can deliver.

Momentum across the portfolio

This year’s��results highlight strong and growing momentum.

�鶹ԭ�� SuccessFactors��increased from��12��Top Rated awards��in 2025��to 19��in 2026, reflecting��deeper customer satisfaction across��the HCM landscape.

Recognized categories include:

• HR Management
• Workforce Analytics
• Talent Management
• Compensation Management
• Workforce Management
• Applicant Tracking
• Talent Intelligence
• Corporate Learning Management
• Payroll
• International Payroll
• Pay Equity
• Recruiting Automation
• Employee Performance Management
• HR Compliance
• HR Service Delivery
• Employee Onboarding
• Succession Planning
• Diversity, Equity, and Inclusion (DEI)

This breadth reflects the strength of �鶹ԭ�� SuccessFactors solutions as a unified suite—connecting people, processes, and data across the workforce. �鶹ԭ����SuccessFactors��solutions can provide��the foundation to turn those connections into real-time insight and action.

What��our��customers are��saying

Across��thousands��of��verified��reviews,��customers��consistently��point to one thing: impact.��From operational efficiency to better decision-making and improved employee experiences, �鶹ԭ�� SuccessFactors solutions are helping organizations move faster and work smarter.

• “With �鶹ԭ�� SuccessFactors HCM AI, we get helpful, actionable insights to make the best decisions. For instance, the insights we gain help us streamline HR operations, especially when it comes to managing our payroll.” —��

• “�鶹ԭ�� SuccessFactors is our core platform and supports our finance and HR processes. We use every module for recruiting, compensation, and learning. It supports our HR transformation and lays the foundation for our data.” —��

• “�鶹ԭ�� SuccessFactors HCM stands out among other human capital management solutions due to its comprehensive suite of��cloud‑based��tools, strong global compliance capabilities, and seamless integration with other �鶹ԭ�� systems.” —��

• “For enhancing employee experience, AI offers personalized recommendations for their learning and development, which increases their productivity and engagement.” —��

• “�鶹ԭ�� SuccessFactors HCM is considered a ‘best of breed’ for a reason. The fact that it does allow for��in‑depth��customization, and its ability to be tailored not only to individual business needs, but also it allows for best practice��follow‑up��while ensuring organizations remain compliant with several legal requirements.” —��

• “We��leverage����(ECP)��for our payroll engine and for our payroll calculations.��Having ECP makes everyone’s life easier… the payroll control center will simulate the payroll before you run the actual payroll. That gives you a lot of analytics and KPIs to analyze results or potential issues well in advance.” —��

• “In our organization, we mainly use Joule in �鶹ԭ�� SuccessFactors to automate and complete tasks through natural conversation, which��eliminates��manual steps. [It] plays��a big role in eliminating the constant back and forth and guesswork involved in finding accurate information, as well as completing routine tasks, for example, workforce insights, budget and planning, document retrieval, etc. Additionally, it makes navigating [�鶹ԭ����SuccessFactors] seamless.” —��

Looking��ahead

We’re��incredibly grateful to the customers that shared their experiences on��TrustRadius��with insights that continue to guide our innovation.

Building on��the��introduction of��Autonomous HCM at �鶹ԭ�� Sapphire,��our focus is clear: helping HR��move beyond managing��processes to��orchestrating work. By��bringing��together AI, data, and workflows, �鶹ԭ�� SuccessFactors solutions enable organizations to��operate��with greater speed, clarity, and confidence, so they can not only adapt to change but actively shape what comes next.

Learn more about the impact customers are seeing with��.

Lara Albert is chief marketing officer for �鶹ԭ�� SuccessFactors.

This journey cannot be defined by technology alone. It requires dialogue, shared learning, and real-world insight. That is exactly why we are launching the podcast, a new series we will be hosting together.

Why this conversation matters now

Organizations today face unprecedented volatility, from geopolitical uncertainty and supply chain disruptions to energy challenges and rising resilience requirements. In this environment, the cost of inaction is increasing. Businesses must become faster, more adaptive, and structurally more resilient to stay competitive.

The Autonomous Enterprise offers a response. It combines three critical capabilities:

• Data-driven decision-making
• Automated execution
• Governance-by-design

Together, these capabilities enable organizations to move beyond isolated AI pilots toward measurable outcomes and enterprise-wide impact.

The shift is not just technical, though. It is organizational and strategic. The leaders we talk to are no longer asking whether they should adopt AI. The question now is how fast they can scale it and how they can generate tangible business value.

From concept to operating model

At its core, the Autonomous Enterprise reframes AI—not as a feature layered onto applications, but as an integral part of the operating model itself.

Three priorities define this model:

• Business value: focusing on measurable outcomes rather than experimental use cases
• Predictability: improving decision-making through trusted data and advanced forecasting
• Scalability: moving from proof-of-concept initiatives to enterprise-wide deployment

We are already seeing this shift change how organizations think about their systems and processes. Systems of record are evolving into systems of action. AI agents are moving from simple assistance toward execution. And AI is becoming embedded end-to-end, rather than confined to isolated scenarios.

At the same time, governance, auditability, and traceability are becoming non-negotiable. Enterprises must be able to stand behind every AI-driven decision with transparency and confidence.

What we are setting out to do

In this podcast series we want to create a space to explore these changes in depth and bring the voices shaping this transformation into the conversation. Each episode features discussions with �鶹ԭ�� leaders, customers, and industry experts who are actively building and operating autonomous capabilities today.

Some of the questions we will be digging into:

• What does the Autonomous Enterprise look like in practice?
• How are leading companies scaling AI across core business processes?
• What are the biggest barriers, and how can they be overcome?
• How do organizations balance automation with governance and trust?

—now live—features �鶹ԭ��’s Peter Maier, responsible for Strategic Customer Engagements in the Office of the CEO at �鶹ԭ��, who brings these ideas into focus through practical, real-world context. In our conversation, he outlines how organizations are moving beyond experimentation toward measurable outcomes, more trusted and predictive decision-making, and scaling AI across the enterprise.

What we found particularly compelling is how clearly this reinforces a broader shift already underway: AI is no longer something applied on top of the business. It is becoming part of how the business runs.

How companies can get started

While the vision is ambitious, the path to becoming an Autonomous Enterprise does not require a “big bang” transformation. The most effective approach is incremental and outcome driven.

Organizations can begin by focusing on a single high-value process, making it more intelligent, more automated, and more transparent. From there, they can expand step by step, scaling what works and continuously demonstrating measurable impact.

Success depends on more than technology, though. Trust plays a central role. Employees, executives, and stakeholders must understand and trust how AI decisions are made. This requires transparent and explainable systems, reliable high-quality data foundations, and strong governance frameworks embedded from the start.

Change management is equally critical. Becoming an Autonomous Enterprise is as much about people as it is about platforms. Organizations must align training, redesign roles, and empower employees to co-create how AI is integrated into their work.

A shared journey forward

The Autonomous Enterprise is not a branding concept. It is a new way of running a business—one that is more automated, more data-driven, and ultimately more resilient. And no organization will navigate this journey alone.

That is the spirit behind this podcast. We want it to be a platform for shared learning, bringing together perspectives from across industries, functions, and geographies. Whether you are just beginning your AI journey or scaling enterprise-wide transformation, we hope these conversations give you practical insights and inspiration.

We would love for you to join us. Listen in, engage with the discussion, give feedback, and help shape what comes next.

Benedikt Gieger is AI strategy lead for �鶹ԭ�� Supply Chain Management.
Julia Kloppenburg is a technology consultant for Customer Engagement & Adoption at �鶹ԭ��.

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

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

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

�鶹ԭ�� Cloud Infrastructure: A Fully Sovereign Cloud Infrastructure from Germany

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

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

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

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

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

Visit the��. Get �鶹ԭ�� news via�� and .

Media Contact:
Dana Roesiger, dana.roesiger@sap.com, +49 16090820259, CET
�鶹ԭ�� �鶹ԭ�� 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 �鶹ԭ��’s 2025 Annual Report on Form 20-F.
© 2026 �鶹ԭ�� SE. All rights reserved.
�鶹ԭ�� and other �鶹ԭ�� products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of �鶹ԭ�� SE in Germany and other countries. Please see https://www.sap.com/copyright for additional trademark information and notices.

“The global energy demand won’t stop growing,” he explained. “As renewables only provide a small share of the energy we currently use, we’ll still need oil and gas that are safely produced as we transition to a lower carbon world.”

Crude oil still remains indispensable where alternatives are limited, particularly in heavy transport and the chemicals industry, and natural gas plays a key role in the low-carbon transition, both as an energy source and in large-scale hydrogen production.

A unique model

What’s interesting about Harbour Energy, one of the world’s largest and most geographically diverse independent oil and gas companies, isn’t just that it’s big. What’s interesting is how it got big and how it operates differently from traditional energy companies. The company was founded in 2014 by private equity firm EIG Global Energy Partners with a goal to build a global, independent company by acquisition.

“We’re basically trying to solve a very hard problem. How do we scale like a major, but stay agile like a startup?” Young said during his presentation about Harbour’s rapid growth journey. He explained that in a company that grows through acquisitions and runs multiple ERP systems, the role of technology is less about “one system” and more about connecting everything, standardizing insight, and accelerating change.

Masters of integration

Most oil and gas giants grew over decades. Harbour did it in about 10 years by pursuing an aggressive strategy of mergers and acquisitions, buying assets such as oil fields from industry giants like Shell. The company also scaled rapidly across 11 countries giving it a broad geographical reach. Crucially, Harbour Energy was often able to integrate acquisitions within a year, demonstrating a rare combination of speed and integration.

“A lot of companies struggle after acquisitions,” Young said. “Systems break, processes clash, value gets lost. At Harbour, we focus on quickly stabilizing new assets, extracting synergies early, and reducing operating costs even while growing.”

Young’s team took a different approach to technology. While most companies push for one massive ERP system, Harbour doesn’t blindly take that path. It runs multiple ERP systems when it makes sense, focuses on fit-for-purpose architecture, and uses tools to connect processes rather than force everything into one box. Such flexibility is a big advantage for a company that keeps acquiring new businesses.

The digital backbone

Because Harbour Energy operates multiple ERP systems rather than a single monolithic platform, complexity is unavoidable. , particularly �鶹ԭ�� LeanIX solutions and the �鶹ԭ�� Signavio portfolio, connects this landscape by aligning processes, linking capabilities to systems, and providing a unified view of ‘what’s where,’ ultimately creating visibility across an otherwise fragmented environment.

“Before we implemented the �鶹ԭ�� tool chain, processes were hidden in Excel and PDFs. It was all part of the local knowledge we acquired,” Young said. “We had no clear view of duplication or inefficiencies. For example, we found that we had dozens of HR systems, which we were able to reduce by half.  We were able to consolidate 33 different ways to do travel expenses into just one.”

One major impact is speed. Whereas traditional transformation planning took up to 24 months, now, with the tool chain and process modeling, key design cycles can sometimes be achieved in four to six weeks. This is enabled by standard process templates and automated modelling for faster validation cycles leading to faster execution of integration and transformation programs.

In addition, tools like the �鶹ԭ�� Test Automation solution by Tricentis and �鶹ԭ�� Cloud ALM for application lifecycle management help ensure that releases are safer and fewer operational surprises occur during go-lives, which is critical in an industry where downtime is expensive.

By connecting systems and processes, the tool chain enables cost transparency across business units and investment prioritization based on real data. This directly supports financial discipline and shareholder value creation

For a company built on acquisitions, probably the biggest value driver is that the tool chain helps rapidly map the systems of acquired companies and compare them against Harbour’s core model identifying what to keep, retire, or migrate. This is why Harbour can integrate acquisitions quickly instead of getting stuck in years of IT consolidation.

Structure before automation

Only when processes are structured and visible can they be used for automation, which is why these tools all play a crucial role in enabling AI adoption. Standardized workflows and process maps are input for AI tools, and digital adoption platforms guide users through systems.

The three key engines provided by the �鶹ԭ�� tool chain include:

• Transparency engine makes the business visible end-to-end
• Standardization engine aligns processes, systems, and capabilities globally
• Acceleration engine speeds up M&A integration and transformation delivery

Together with �鶹ԭ�� Analytics Cloud for global forecasting and planning, these tools are at the heart of the company’s successful business transformation.

Young listed the three strategic levers keeping the company strong, resilient, and ambitious. The first is maintaining strict financial discipline, followed by using data driven insights that ensure the company remains competitive, and, last but not least, equipping the business teams with advanced capabilities ensures resilience.

“The �鶹ԭ�� tool chain allows us to grow aggressively through acquisitions without collapsing under complexity,” Young concluded. “It’s essentially the difference between chaotic expansion and controlled, scalable growth.”

Check out the �鶹ԭ�� integrated tool chain and its core capabilities .

“Last year, we were in the process of planning the optimization of our talent discovery tech stack and then something happened,” Eric Goldstein, global head of Talent Discovery for �鶹ԭ��, said. “We acquired SmartRecruiters in September, so we had to pivot in an agile way.”

SmartRecruiters for �鶹ԭ�� SuccessFactors enables enterprises to manage the entire hiring lifecycle, from sourcing to onboarding, with AI-enabled recruiting capabilities that can result in faster time-to-hire, improved candidate experiences, and deeper analytics for workforce planning.

For �鶹ԭ��, this means adding much-needed rigor and precision to its global talent acquisition operations. This will not only elevate the quality of hires but also the candidate experience, which Goldstein identified as the “biggest game changer.”

Building a more intelligent hiring process

SmartRecruiters for �鶹ԭ�� SuccessFactors helps optimize processes, increasing transparency and personalization. This means improved experiences and processes for candidates, hiring managers, and recruiters. “We have been through a time where we focused solely on the recruiter experience. Then it was fashionable to focus only on the candidates. Now we really see that with SmartRecruiters, it really is an enhanced experience for all stakeholders that are involved in the recruiting process,” Ilka Sagner-David, global head of Talent Discovery Solutions and Innovations at �鶹ԭ��, said.

Candidate perspective

Seventy percent of candidates that apply for jobs are mindful to take their valuable time to do so, Goldstein shared, reiterating that it is important for companies to match that commitment when shaping and delivering the candidate experience. With SmartRecruiters for �鶹ԭ�� SuccessFactors as the foundation, it becomes possible for every pre-qualified applicant to interview, receive personalized and constructive feedback post-interview, and maintain 24×7 interaction with agentic AI built into SmartRecruiters.

“In our opinion, only responding with polite, automated rejection notes is not enough. [Candidates] need to be provided with some constructive, actionable feedback—and that’s what we [at �鶹ԭ��] are going to be able to do,” Goldstein said.

Hiring manager perspective

SmartRecruiters for �鶹ԭ�� SuccessFactors can give hiring managers a more precise and consistent way to identify strong candidates, helping to reduce time-to-hire while improving hiring quality. AI-prompted interview questions focused on skills can support more relevant and structured conversations while greater transparency across interview panelists can create better alignment throughout the evaluation process. In addition, AI-supported feedback collection can make it easier for interviewers at �鶹ԭ�� to capture timely, consistent insights, enabling its hiring teams to make more informed decisions with greater confidence. 

Recruiter perspective

Recruiters are often bogged down by manual tasks, such as outreach, prospect identification, and candidate screening, making it nearly impossible for them to step into the role of a trusted advisor. With SmartRecruiters for �鶹ԭ�� SuccessFactors, recruiters can experience automated internal and external prospect identification, personalized outreach and prioritization of candidates, and, therefore, the ability to focus on higher value-add advisory and relationship management.

“It’s going to allow the recruiters to focus on relationship management with candidates and hiring managers, really challenging the feedback of how well the interview panel measures skills proficiency,” Goldstein said.

Bringing AI into the candidate journey

A key to the successful delivery of these benefits is SmartRecruiters Winston for �鶹ԭ�� SuccessFactors, an AI-driven, candidate-facing agentic experience. At �鶹ԭ�� Sapphire Orlando, Karl Baert, global head of People Solutions for �鶹ԭ��, demonstrated how Winston can facilitate the application experience for candidates.

In the demo, he acted as a candidate applying for an open position at �鶹ԭ��, showing how through a natural language conversation with Winston, he completed his application by uploading his CV and verifying some personal details with Winston. “All that information is very, very quickly brought together so with just a few questions my application is done,” Baert said, adding that “there’s also a few checks happening along the way because we want to make sure the data we are collecting is the right quality.”

Winston also collects feedback from the applicant. “Measuring the quality of your agent and what’s happening with it is important. It’s something that really needs to be actively monitored just to ensure that the information provided by the agent is accurate,” Baert said.

“The implementation of SmartRecruiters is the foundation for infusing AI into our processes,” Sagner-David said. But, she added, “we shouldn’t just plan to transfer everything tomorrow, but ensure we’re liberating AI when it makes sense.”

The next step

Currently, SmartRecruiters for �鶹ԭ�� SuccessFactors is being implemented into �鶹ԭ��’s HR systems for two phases of user acceptance testing, with the global go-live expected in September.

�鶹ԭ�� bringing SmartRecruiters for �鶹ԭ�� SuccessFactors to life across its own organization is more than a technology rollout, it’s a glimpse into the future of hiring at scale: more intelligent, more human, and more connected. By combining AI, better experiences, and real-word enterprise rigor, �鶹ԭ�� is not only transforming how it hires but also helping to define what modern hiring can look like for companies everywhere.

What it cannot say is why this supplier keeps slipping, what resolved a similar dispute last time, or that the same supplier has a delayed shipment in logistics and a renegotiated contract in procurement at the same moment.

The reasoning behind enterprise decisions has stayed locked in human judgment, scattered across systems.

For 50 years, enterprise software has been an excellent system of record. Closing the reasoning gap on top of it is what enterprise AI was always meant to do.

From AI-first to AI-native

The first wave, the AI-first approach, added intelligence inside existing applications. A feature can summarize an invoice or suggest a journal entry, but it lives within one application and cannot see across the landscape. Three barriers keep it confined: It lacks business and process context, it sits on disconnected systems without a shared data model, and it lacks the governance to be accountable at scale.

Meanwhile, the pace of change is unforgiving. Agentic systems, new interaction models, and new ways of grounding AI in business data are arriving faster than most architectures can absorb. As �鶹ԭ�� CEO Christian Klein noted this year at �鶹ԭ�� Sapphire, 80% accuracy may suffice for consumer AI; it is nowhere near enough for the world’s most business-critical processes. Bolting more intelligence onto isolated applications will not close that gap. It only multiplies the silos.

So what does it actually take to move beyond isolated AI features and build an enterprise that reasons, learns, and acts as one, without sacrificing the trust, governance, and reliability the business depends on? It is the question CIOs, CTOs, and enterprise architects are working through right now.

The foundation behind the Autonomous Enterprise

It takes a new foundation, and that is exactly what �鶹ԭ��’s provides.

This is not a white paper that sits on a shelf; it is the technology foundation �鶹ԭ�� is actively building to bring the Autonomous Enterprise to life: a business where agents, orchestration, and data work in one continuous loop to turn intent into trusted outcomes.

The shift it enables is from AI-first to AI-native, where software operates across the landscape as a system of context: an intelligence layer connecting data, process knowledge, decision history, and semantics. Agents reason over the whole picture, not fragments. Every interaction feeds intelligence. Every correction becomes a learning signal. Value shifts from software as a service to outcome as a service.

AI-native paves the way for the Autonomous Enterprise: one system of context that understands disputes in service, delays in logistics, and contract changes in procurement all at once, and can act on them with full governance and accountability.

Philipp Herzig, CTO and Member of the Extended Board, �鶹ԭ�� SE

Crucially, AI-native does not replace what already works. It pairs two complementary paths. The deterministic path keeps the predictable, rule-based execution that compliance depends on. The probabilistic, AI-native path adds reasoning that learns from data and experience. One is reliable but rigid. The other is powerful, but without context and control, often confidently wrong. Context engineering, guardrails, and observability bind the two, turning raw capability into reasoning the enterprise can trust.

The architecture delivers this through four reimagined layers that together form a cognitive core:

• The user experience layer shifts interaction from navigating apps to stating intent, with Joule as the central engagement point.
• The process layer turns applications into capability providers that expose stable APIs, events, and data for agents to orchestrate.
• The foundation layer is where data and AI come together as the intelligent core: orchestration, reasoning, and model services on one side; �鶹ԭ�� Business Data Cloud and the �鶹ԭ�� Knowledge Graph on the other, with �鶹ԭ��-trained models, including �鶹ԭ��-RPT-1 for structured business data, sitting alongside leading third-party models in one governed generative AI hub.
• The platform layer provides the runtime, governance, and harness that turn stateless models into reliable enterprise agents.

It defines the cornerstone architectural building blocks for agentic systems across experience, process, data, and platform, turning �鶹ԭ��’s unique business context into a living system of intelligence

What does this look like in practice? A finance analyst asks Joule to resolve high-value disputes likely to delay payment. Joule does not act alone. It coordinates AI assistants, which in turn direct specialist AI agents through agentic orchestration: the assistant decomposes the goal, delegates to a finance agent and a service agent, and reconciles their results. People set direction; assistants coordinate; agents execute. Those agents draw on the right information through context engineering, find the correct data through semantic grounding in �鶹ԭ�� Knowledge Graph, and act within governed boundaries, routing only exceptions to a human. Each resolution becomes a decision trace that makes the next one smarter.

This is not theoretical. During the 2026 keynote at �鶹ԭ�� Sapphire, �鶹ԭ�� COO Sebastian Steinhaeuser pointed to life sciences customer Takeda, which 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. That is what AI-native looks like at work.

Frontier models are available to everyone. Business context is not. Each resolved dispute, each corrected decision, each completed process adds to it, compounding with every interaction.

Trust is engineered in, not bolted on. A set of cross-cutting, �鶹ԭ��-managed qualities holds the layers together: integration, identity, security, observability, and extensibility, with resilience, compliance, and sustainability handled by the platform.

Autonomy only creates value when it is governed, so agents become first-class principals with their own agent identity, scoped to a bounded subset of permissions and audited like any enterprise actor. Harness engineering wraps each model with the sandboxing, memory, and guardrails that make it dependable.

As the paper puts it, the model reasons but the harness governs, and it is the harness, not the model, that determines the ceiling. Open standards such as the Model Context Protocol and Agent2Agent protocol let agents interoperate across the enterprise, while sovereign cloud options keep data residency and compliance built in.

This direction is being shaped with the customer community, not handed down to it: the architecture carries forewords from the leaders of the German-Speaking �鶹ԭ�� User Group (DSAG) and Americas’ �鶹ԭ�� Users’ Group (ASUG) alongside �鶹ԭ��’s own.

The North Star is a living document. Published openly on , it will keep evolving as the technology and the agentic ecosystem advance, and as customer feedback shapes the design. If you build with �鶹ԭ�� or build on �鶹ԭ��, this is your invitation: Read the architecture, push back where it should be sharper, and contribute. The same invitation extends to the wider �鶹ԭ�� Architecture Center site, where �鶹ԭ��’s reference architectures are being built openly with the community. 

Read the AI-Native North Star Architecture and ��or .

Beyond the architecture itself is a single commitment: building systems that learn rather than dictate. For �鶹ԭ�� customers, 50 years of process knowledge, governed data, and trusted decision frameworks compound into a new kind of enterprise intelligence that is reliable, transparent, and deeply human.

The Autonomous Enterprise will not arrive as a single product launch. It will be built layer by layer, decision by decision, on the foundation described here, one grounded interaction at a time.

is head of the Office of the CTO at �鶹ԭ��.
is vice president of the Office of the CTO at �鶹ԭ��.

AI works when the foundation is right 

The energy and utilities sector is investing heavily in AI. Business��leaders worldwide are embracing artificial intelligence to increase efficiency, unlock new business models, and prepare for the energy transition. A successful proof of concept is often the first milestone—but it marks only the beginning. The��real challenge��lies in scaling pilot projects across the entire��organization.��

In this context, the time and effort��required��for a full implementation��is��frequently��underestimated. Around six months are needed to build a robust data foundation. A further��12��months pass before initial results manifest in the form of a measurable return on investment. Large-scale rollout can take another three years. The reasons for this are manifold:��

• Unrealistic expectations: Many people use AI in their daily lives for simple tasks and expect similarly seamless effects in complex enterprise environments. 
• Legacy infrastructure: Historically grown system landscapes cannot be transformed overnight. 
• Regulatory complexity: In regulated industries such as electricity, gas, and water supply, compliance requirements are particularly high. They must be factored into every architectural decision from the very beginning. 
• Lack of AI-specific talent: What is needed are people who genuinely understand both the business and AI. This bridge between IT and the business side will become increasingly important in the future. 
• Organizational��change management:��Technology alone is not enough. Organizational transformation is and��remains��the decisive success factor.��

From AI hype to real value 

Building a new application is��only the first��step.��On the path to scaling, lifecycle management, identity and access management, security, compliance, and governance must all be consistently taken into account.��Release management, testing, and continuous improvement processes add further complexity.��“The��companies��that��invest in the right foundation today will benefit from AI to its full extent tomorrow,” says Andre Bechtold,��president and��head of �鶹ԭ�� Industries & Experiences.��

For companies, this means overcoming fragmented data silos and developing an integrated data strategy. Legacy systems must be integrated into a modern data and AI platform on which AI models can genuinely create value. Torsten Welte,��head of Energy & Natural Resources Industries��at �鶹ԭ��,��summarizes��it as follows:��“AI is fundamentally transforming the energy industry. The business must understand what is technologically possible. And IT must understand what the business needs.”��

��can��provide��the��essential foundation for this. AI is already natively embedded in the suite in the form of Joule. This��can open up��concrete use cases for the energy industry:��in the area of asset management and predictive maintenance, utilities��can��proactively manage assets and grids before disruptions occur. The Utilities Customer Self-Service Agent, in turn, enables 24/7 self-service for customers and can reduce service costs by up to 90%.��

Distributed energy requires intelligent networking 

The topic of��distributed��energy��resources (DER) remains of��central importance. In the past, energy flowed in only one direction: from the power plant to consumers. In the future, it will be bidirectional. Consumers��that��generate their own energy will actively feed it back into the grid.��

DER��describes precisely��this principle: the generation of electricity through millions of decentralized resources such as solar panels, EV chargers, heat pumps, and battery storage systems��by��consumers and so-called��prosumers. These assets generate vast amounts of data. Their orchestration��represents��one of the key challenges of the energy transition.��

The ��solution��provides a platform��for��a��single source��of truth: technical assets, commercial contracts, and customer data are brought together in a coherent data model. This helps create the foundation for new business models such as smart tariffs, dynamic pricing, energy sharing, and demand response.

�鶹ԭ�� consistently relies on a growing partner network built around its own data platform. Markus Bechmann,��global VP and��co-head��of��Industry Business Unit Utilities��at �鶹ԭ��, describes it this��way:��“Dynamic pricing and smart tariffs are no longer distant concepts.��They��are the business models��of��tomorrow. With �鶹ԭ��, energy providers already have the technological foundation today to seize these opportunities.”��

�鶹ԭ�� Experience Centers: experiencing AI, not just discussing it 

To make AI tangible, �鶹ԭ�� Experience Centers offer visitors the opportunity to experience AI in real-world scenarios beyond classic demo environments. One central example is the �鶹ԭ�� Energy Park in Walldorf. Using real infrastructure on the campus, �鶹ԭ�� demonstrates how the company itself is implementing the energy transition. This includes e-mobility, intelligent asset management, and energy communities. 

A new chapter for the energy industry 

The �鶹ԭ�� for Energy & Utilities Conference in Toulouse has once again demonstrated that AI in the energy industry is no longer a topic for the future. However, the path from pilot project to company-wide transformation requires more than technological enthusiasm. To meet the challenges of the energy transition, what is needed—alongside technological innovation—is a solid foundation of data, processes, and organization.

As discussed in a new whitepaper, , this perspective is grounded in interviews with supply chain leaders across six industries: automotive electronics and software, agricultural equipment, chemicals, global technology, automotive supply, and home appliances.

Their experiences reveal where companies are investing, where adoption challenges remain, and where the next wave of value is likely to emerge.

Supply chains are entering an era of permanent disruption

Four structural forces are reshaping global supply chains simultaneously: geopolitical instability, economic pressure, demographic shifts, and accelerated digital transformation.

Since 2017, relative to trade among closer partners, signaling growing fragmentation in global commerce. , while labor shortages and digital skill gaps continue to constrain operations.

Europe alone could face by 2028, and 63% of companies cite .

Together, these pressures are pushing supply chains beyond the limits of the traditional “plan-source-make-deliver” model.

Companies are shifting from optimization to AI-enabled orchestration

Supply chains are increasingly viewed as strategic levers for resilience, service differentiation, and competitive advantage.

Across all six companies interviewed, each is investing in at least three forward-looking AI use cases in planning alone.

• A leading agricultural equipment company has deployed more than 1,000 AI agents to support orchestration, scenario planning, and value chain visibility. A global chemicals company is embedding AI across planning and scenario management while emphasizing explainability and trust.
• A home appliance company is applying AI selectively to improve forecasting, transport optimization, warehouse safety, and logistics costs.

The common theme: organizations are redesigning how the enterprise senses, decides, and acts.

Resilience is now defined by decision velocity

In today’s fragmented environment, resilience is no longer about static buffers. It is about how quickly companies can convert disruption signals into coordinated action across sourcing, production, planning, and logistics.

• An automotive electronics and software company centralized electronics ordering across roughly 30 plants and redesigned crisis-management processes, reducing disruption response times by approximately 95%.
• A global technology company adopted a regional “two-leg” supply chain model, using inventory strategically to respond faster to disruptions.

The emerging differentiator is not forecast accuracy alone, but the speed from disruption detection to execution. Visibility remains important, but visibility without coordinated action is no longer enough.

Trust and governance are the biggest barriers to scaling AI

Despite rapid interest, . The challenge is not model accuracy alone; it is trust, explainability, fragmented systems, and manual overrides.

• One global chemicals company found that scaling AI depended less on technical performance and more on whether users could understand and trust the outputs. This led to stronger human-in-the-loop governance and progressive autonomy thresholds.
• A major automotive electronics company requires transparent, traceable AI reasoning before planners rely on AI-generated recommendations.

The path to autonomy will be incremental: companies will first augment human decision-making, then automate routine and semi-structured decisions as governance, trust, and data maturity improve.

The next frontier is the Autonomous Enterprise

The Autonomous Enterprise is an operating model where AI workflows, contextual business data, and embedded governance work together to anticipate disruption, coordinate action, and continuously improve performance.

The shift is moving from isolated copilots to coordinated agent-to-agent workflows spanning the supply chain.

In autonomous production environments, supplier reliability agents can monitor vendor risk while workforce orchestration agents align labor capacity with demand. Procurement agents execute sourcing decisions, and production planning agents dynamically rebalance schedules in response to changing conditions.

A similar pattern is emerging in asset management, where alert-processing, maintenance, warehouse replenishment, and goods-movement agents collaborate to resolve operational issues with minimal human intervention.

The business impact is significant. Agentic AI has by 20 to 30%, , and helped .

Collectively, these improvements mark the transition from reactive supply chains to systems that can increasingly anticipate, decide, and execute autonomously.

Building the autonomous supply chain

Capturing this opportunity requires three capabilities that remain fragmented in many organizations today:

• Organizational intelligence: The ability to detect patterns, anticipate risks, and reason across constraints
• Contextual data: Trusted operational data, business rules, workflows, and policies that ground AI decisions in enterprise reality
• Embedded execution: Integrating intelligence directly into workflows so actions can move from recommendation to execution without manual intervention

This creates a virtuous cycle: better data improves decisions, better decisions improve processes, and improved processes generate richer operational data over time.

Importantly, companies do not need to rebuild the enterprise from scratch. Deterministic systems of record remain essential for control, compliance, and auditability. The real transformation lies in rewiring how decisions are made and governed.

Organizations moving fastest are focusing first on high-value, high-frequency decisions such as forecasting, inventory optimization, disruption sensing, transport planning, procurement workflows, maintenance, and customer-service resolution.

The bottom line

The future of supply chain management will not be defined by more digital tools alone. It will be defined by the ability to operate the supply chain as a connected, adaptive, and increasingly autonomous system.

For leaders who move first, supply chain will evolve from a cost-management function into a competitive differentiator, enabling faster time to market, stronger service levels, and greater resilience. The organizations that lead will not be those running the most AI pilots. They will be the ones using AI to redesign how the enterprise senses, decides, and acts across the end-to-end supply chain.

For more information about Autonomous Supply Chain Management, download the white paper, .

Hagen Heubach is chief marketing officer for Supply Chain Management at �鶹ԭ��.

The journey began in early 2019 with the launch of Agile Finance, an end-to-end transformation initiative designed to support the company’s aggressive growth trajectory, which included a goal to double in size. The initiative was built around three strategic pillars: enhancing the efficiency and effectiveness of the finance organization, promoting career fulfillment, and establishing a robust digital operating model. The impact was significant, with the finance function achieving approximately 35% productivity gains in its labor force.

The third pillar—the move to a digital operating model—is where the partnership with �鶹ԭ�� Taulia began.

“The �鶹ԭ�� Taulia Dynamic Discounting solution was introduced not merely as a cost-cutting measure, but as a strategic tool to transform and digitize the interaction with Applied’s extensive, global supplier base,” Junaid Ahmed, corporate VP, Finance at Applied Materials, says. “We understood that to reap the benefits of digitization, we had to ensure the suppliers were on board. It needed to be a win-win outcome.”

Unprecedented flexibility for suppliers

The program empowers suppliers—thousands of them worldwide—to self-select which approved invoices they wish to discount for early payment. This is not a continuous, all-or-nothing commitment but rather a decision made on an invoice-by-invoice basis. This flexibility allows suppliers to manage their working capital needs with greater precision, taking advantage of early payment during their own critical periods, such as quarter-end or year-end, to help meet their own financial targets.

The system also drastically improves transactional efficiency. Suppliers no longer have to call Applied to track invoice status, approval, or payment date. All this information is available 24/7 in the �鶹ԭ�� Taulia solution, reducing resource allocation on both sides and ensuring both reap the benefits of moving to an integrated, digital system.

Strategic benefits for Applied Materials

For Applied, the program is a testament to its focus on balancing efficiency with strong supplier relationships. The philosophy is a “win-win” built on a crucial spread: Applied Materials, as a Fortune 500 company with strong cash flow, has a significantly lower cost of capital than many of its suppliers. By funding the discounts, Applied captures a return—the discount income—while offering its suppliers funding at a rate close to their cost of capital, but with greater convenience.

This relationship-focused approach is critical. Applied’s supplier account managers actively support the program because they recognize its mutual benefit, not viewing it as a finance mandate to push costs onto the supply base.

Furthermore, the “dynamic” nature of the discount rates is a powerful risk mitigation tool. Unlike fixed contractual discounts, the rates can be adjusted in response to global economic changes, such as shifts in interest rates. When interest rates rose after the pandemic, Applied was able to adjust the discount rates accordingly with minimal pushback, as the core proposition remains the valuable spread between the parties’ cost of capital.

The �鶹ԭ�� Taulia Dynamic Discounting solution has been rolled out globally, giving all suppliers the opportunity to use it. This has been critical over the last 12 months as many businesses around the globe have been subject to new and often unexpected tariff costs impacting their margin and their liquidity.

“The flexibility of the solution means suppliers can access funds when they need them, which helps them navigate some of the economic uncertainty that many businesses are facing,” Dirk Holoubek, managing director, Finance Shared Services, explains. “2025 saw a 23% increase in usage of the discounts, reflecting the pressures that suppliers are feeling right now on their cash flow.”��

The solution’s capability to drive sophisticated analytics is also a major strategic asset. It helps provide insights into the different costs of capital between Applied and its supplier base. This data allows for targeted outreach and communication, ensuring that the offer of capital support is proactively extended to the suppliers that need it most.

The strategic value of the solution is further cemented by its ownership. The acquisition of Taulia by �鶹ԭ�� brings several advantages.

“Trust is really important to both us and our suppliers,” Ahmed says. “For our suppliers to adopt a new solution, they need to know its technology they can rely on in the long term. Being part of �鶹ԭ�� creates that assurance in the long-term future of the program.”

Looking forward, Applied Materials is already focused on the next stage of the transformation project: Agile Finance 3.0, which is focused on enabling the organization to become AI-first. The company is deploying a global, organization-wide AI assistant to drive personal productivity, but the strategic application of AI in the supplier management space is even more profound.

AI is expected to transform decision-making enablement by analyzing critical information and communicating effective options. In the future, AI will be able to proactively assess the specific needs and attributes of the supplier base, enabling Applied to address issues more quickly and resolve them earlier. The benefits are already tangible in e-invoicing: AI has made the solution more flexible and “human-like,” capable of reading minor changes in invoice format that would have previously caused electronic errors. This reduced rigidity and increased flexibility are directly contributing to the overall efficiency of the digital operating model.

By leveraging the �鶹ԭ�� Taulia Dynamic Discounting solution, Applied Materials has not only digitized a process but also strategically transformed its financial operations, creating a system that is agile, resilient, and focused on maintaining mutually beneficial relationships with its global supplier ecosystem.

Cedric Bru is CEO of �鶹ԭ�� Taulia.

This momentum is aligned with consumer audio brand Victrola’s 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 �鶹ԭ�� ERP Central Component (�鶹ԭ�� 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. “We needed something that fueled our creativity at Victrola,” he said. And �鶹ԭ�� Cloud ERP was the right fit.

Victrola’s “let’s go” 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.

“We have a ‘let’s go’ mentality in every single meeting we do. You’ll see a lot of fist bumps at Victrola and we love it. It really keeps that change front and center,” Schneider said.

The project team prioritized change management, openly communicating the reasons for moving to the cloud and the expected benefits. Giving the “why” made a big difference, Schneider said, and everyone was “excited for something new.”

It was also important for the team to engage with and get buy-in from company leaders, who were concerned about disruption during Victrola’s peak sales quarter. To address these concerns, the team ensured the transformation was a business-led project, involving about 75% of Victrola’s 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 �鶹ԭ�� Cloud ERP. The right partner would also fit seamlessly into Victrola’s unique, candid, music-oriented culture, Schneider said, which it found in .

“We wanted someone that could really provide that business knowledge on the public cloud,” he said. “From a timing perspective, we couldn’t afford to have people figuring out the project as they’re on the project, so it really was the expertise of the public cloud that was a huge part in this.”

With a winning team assembled, Victrola’s move to the public cloud was well-supported and bolstered by stakeholders across the company, �鶹ԭ��, 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.

“We 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,” 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’s improved agility, more informed decision-making, and stronger business performance.

“We’re more trusting in our data and that’s because we went through that exercise of really retooling what our landscape looked like and what our foundation looked like,” Schneider said. It’s 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. “When we think about our AI strategy, I’m no longer scared of our system,” Schneider said. “We’re now applying an AI strategy to a state-of-the-art system that �鶹ԭ�� very much supports.”

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

“I don’t think there is ever a perfect time to do these transformations,” he added. “I do think once you get into them, you start to really embrace them and feel good about it and say ‘It’s happening, let’s go.’”

Interested in learning more about how �鶹ԭ�� supports the transition from �鶹ԭ�� ECC to �鶹ԭ�� Cloud ERP? .

Photo courtesy of Victrola.

Demand for professionals who can build, govern, and orchestrate these agents is rising faster than supply, making graduates with hands-on agent-building experience among the most sought-after profiles in today’s job market.

This year at �鶹ԭ�� Sapphire, �鶹ԭ�� laid out its vision for the Autonomous Enterprise, where AI agents manage and execute business processes end to end. For universities, this raises an immediate question: How do graduates get ready for a world where AI agents are part of daily operations?

�鶹ԭ�� is now providing new no-cost offerings and resources for universities that give lecturers and students hands-on access to AI agent building, process management, and enterprise architecture tools. The goal is to help higher education keep pace with the rapid adoption of agentic AI in industry and prepare graduates for a changing job market.

Preparing the next generation of AI agent builders

�鶹ԭ�� has put together a new set of offerings and resources that help universities embed agentic AI-related concepts and technology into their teaching hands-on. Three offerings, each covering a different angle of agentic AI, are now accessible at no cost for academic lecturers and their students:

• : Before building an agent, the process it will operate in must be understood. �鶹ԭ�� Signavio Process Transformation Suite gives lecturers and their students access to process mining, modeling, and process transformation capabilities. They can model and analyze existing processes, spot inefficiencies, and design improved workflows that include AI agents. Additionally, students and lecturers can now experience process modeling with �鶹ԭ�� Signavio Process Modeler as part of �鶹ԭ�� Learning Hub, student edition.
• : For students to understand where agents sit within an organization’s IT landscape, this is the tool. Newly available at no cost for academic lecturers via �鶹ԭ�� Learning Hub, student edition, �鶹ԭ�� LeanIX lets students model enterprise architectures and reason about what changes when introducing AI agents into an existing system landscape.
• : Lecturers and their students can access an agent-building environment from �鶹ԭ�� and leverage various enablement resources. These allow students to explore configuring and building an AI agent, either in a guided demo experience or in a live system hands-on.

What makes this especially valuable is how the pieces connect. Students can explore different components of agentic AI hands-on using �鶹ԭ�� solutions. They learn that building an agent is only part of the job. Understanding process context, architectural and governance implications is equally important.

Collaboration with educational institutions globally

�鶹ԭ�� will also collaborate intensively on embedding agentic AI into teaching with lecturers from more than 10 universities globally, including:

• Budapest University of Technology and Economics, Hungary
• Eötvös Loránd University, Hungary
• Hasso Plattner Institute, Germany
• HEC Montréal, Canada
• Karlsruhe Institute of Technology, Germany
• National University of Singapore Business Analytics Centre, Singapore
• TEC de Monterrey, Mexico
• Technical University of Munich, Germany
• Tongji University, China
• Technical University of Dresden, Germany
• University of California, Irvine, U.S.

The institutions will get exclusive early access to �鶹ԭ��’s latest agent building platform capabilities, benefit from agent building deep dives for students with �鶹ԭ�� experts, and from the opportunity to articulate academic needs with regards to teaching agentic AI related concepts hands-on to �鶹ԭ��.

“We want students to work with the same tools and scenarios that companies are using right now,” Dr. Katharina Schaefer, head of Academic Partnerships at �鶹ԭ��, said. “By giving lecturers free access to our agent-building resources, we make it easy for them to bring that reality into their courses. Students who build AI agents on real enterprise processes during their studies will have a head start when they enter the job market.”

For faculty, the practical element is what counts. Students do not just read about AI agents in a textbook. They build them on real systems with real constraints.

“What excited me is that students get to work with enterprise-grade tools, thanks to this new platform,” said Prof. Jesús Aguilar-Gonzalez, TEC de Monterrey. “Students from our School of Engineering & Sciences build agents connected to real business processes and have to think about architecture and governance. That is much closer to what they will face in their first job than any textbook exercise.”

What sets this apart is its enterprise context: Agentic AI is taught in connection with business processes and the system landscape that supports them, so students learn how AI fits into real operations rather than experimenting in isolation.

Building the workforce of the future

As part of the , �鶹ԭ�� has been partnering with more than 2,800 educational institutions for decades to enable students to learn, research, and innovate with business applications and technology. With these offerings, �鶹ԭ�� supports students in developing sought-after �鶹ԭ�� skills, preparing them for job opportunities worldwide.

Ready to bring agentic AI into your classroom? Visit the or reach out via universityalliances@sap.com to get started.

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—it creates a responsibility to adopt innovation in a controlled, resilient, and purpose‑driven 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’t revolutionize the consumption of IT when people started expecting the same ease of use in the workplace?

“AI 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.”

One of E.ON’s biggest challenges is closing the widening gap between the rapid pace of technological innovation in the outside world and the organization’s 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’s green energy transformation.

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

“Bringing the system up to speed requires internal readiness. It means we must think deeply about investments, prioritization, and most importantly, people and culture,” said Weber. “One thing is sure: we won’t be going back to what was normal speed before.”

Becoming strategic

E.ON operates across three domains: energy grid, customer solutions, and energy infrastructure solutions. ��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’s 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 �鶹ԭ�� 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’s journey is the importance of embedding digital capabilities into the heart of operations. “We’ve moved away from isolated innovation hubs such as digital labs or experimental ‘garages’ in favor of integrating digital tools directly into business processes,” 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’s 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.

“In essence, our experience highlights a broader truth about digital transformation,” said the IT expert. “Success really depends on balance. We absolutely must push innovation forward, but not at the expense of stability, cyber security or governance.”

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’s 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’s a fundamental shift in how technology drives business success, turning technology into a cornerstone of making new energy work—reliably, affordably, and at scale.

What made the event distinctive was its candor. Customers did not come to present polished success stories. They came to compare realities. And those realities were refreshingly honest.

Why direct procurement is hitting a breaking point

The pressure on direct procurement is not coming from one direction. Geopolitical instability and accelerating technological change are forcing sourcing decisions earlier in the product lifecycle, at precisely the moment when many organizations are least equipped to act. Meanwhile, institutional knowledge is leaving faster than systems are modernizing. The experienced individuals who once held fragile processes together are retiring or moving on, and the systems meant to replace that knowledge are not yet ready.

The result is an operating model that prevents procurement leaders from influencing value at the moments that matter most. Several customers described a tension they are actively dealing with. Sourcing is being pulled upstream into design and development, while the tools and processes that support it are still anchored downstream.

What customers shared about their reality

While all participants operate with a strong �鶹ԭ�� footprint, spanning and , many acknowledged that direct materials sourcing remains fragmented and disconnected from the digital core. The picture they described was familiar but worth stating plainly: engineers, buyers, and suppliers still collaborating through e-mail, local tools, and disconnected applications; there’s an overreliance on a small number of experienced individuals to make things work; and multiple ERP landscapes run in parallel, with direct sourcing living largely outside all of them.

One observation stood out clearly. The real friction is not the sourcing events themselves. It is the handoffs, the gaps between systems and teams where decisions get made too late, data is reconciled manually, and no single digital thread connects product intent to sourcing execution.

In other words, the process functions, but it functions in silos.

Participants also noted that traditional indirect source-to-pay approaches simply do not support direct materials adequately. They lack native support for procurement embedded in new product development, sourcing scenarios that evolve with engineering change, demand aggregation across programs, and contracts treated as executable objects rather than static documents. That last point came up repeatedly, particularly the need to treat contracts as executable objects. This is also where the add-on in �鶹ԭ�� S/4HANA is starting to resonate more strongly in ongoing customer discussions.

Where customers are focusing next

What emerged from the discussions wasn’t a long list of priorities, but a firm shift in where companies are focusing their efforts.

Moving sourcing upstream into product development—rather than reacting after design decisions are already locked—was a consistent theme. So was reducing dependency on hero buyers: individuals whose personal expertise and relationships are currently holding critical processes together.

Commodity volatility and renegotiations also came up as structural challenges, not one-time events. Organizations want to handle these systematically rather than heroically. And several participants raised the reality of managing multi-year �鶹ԭ�� S/4HANA journeys without stalling progress in the meantime—a genuine tension that demands honest road map planning.

While the direction is widely understood, most organizations do not yet have the setup to execute against it at scale.

These priorities help explain why customers are increasingly adopting the �鶹ԭ�� Ariba direct materials sourcing add-on alongside , , and —capabilities that together can support the connected execution model direct procurement actually requires.

How AI fits into direct procurement

AI generated significant interest, but expectations were measured and, I would say, appropriately so.

The consistent message was this: AI only matters once the fundamentals are addressed. Agent-based capabilities depend on clean processes and consistent data. Without a unified digital thread across product design, sourcing, contracting, and execution, AI does not generate insight—it amplifies noise.

Leaders also expressed clear skepticism toward black-box automation. They want AI that is explainable and embedded directly into sourcing, negotiation, and execution workflows, not layered on top of broken processes and presented as a fix.

This thinking aligns closely with �鶹ԭ��’s vision for the Autonomous Enterprise, introduced at �鶹ԭ�� Sapphire just weeks after our Walldorf discussions. The vision anchors AI agents directly in transactional business processes, data, and governance—exactly what customers said they needed before they could trust AI in direct procurement environments. Hearing them articulate that requirement so clearly, before the announcement, felt like meaningful validation.

Where this is all heading

The Walldorf roundtable confirmed a clear trajectory. Direct procurement organizations are moving away from heroics and spreadsheets and toward system-led execution. They are aligning sourcing transformation with their �鶹ԭ�� S/4HANA road maps and preparing their organizations—not just their systems—for a future where AI supports decision-making across the full procurement lifecycle.

Direct procurement, seen through this lens, is not a standalone transformation. It is a foundational building block. Connecting product, sourcing, contracts, and execution through a single digital thread is what enables AI to operate accurately, compliantly, and at scale. That connection has to exist before any of the more ambitious automation goals become realistic.

For �鶹ԭ��, conversations like the one in Walldorf directly inform our product direction and investment priorities. There is no substitute for sitting in a room with people navigating these challenges every day, without a script.

What was clear in Walldorf is that the direction is no longer in question for participating organizations. The challenge now lies in execution and in how quickly organizations can move from fragmented, person-dependent processes to cohesive models that reflect how direct procurement operates today.

Karolina Bombardelli is global go-to-market lead for Direct Procurement at �鶹ԭ��.

Now, the countdown is on. By June 7, all 27 EU member states are expected to adopt the directive into national law, marking what many HR leaders are calling “Day One” of a new era in workplace transparency.  

But while the deadline is fast-approaching, many organizations are still far from operationally ready. Even though employers will be required to share pay information with both employees and candidates during the recruiting process, current practices suggest a significant gap. For example, across Europe, salary disclosure in job postings remains inconsistent and often limited, according to .

For HR leaders, the challenge is no longer understanding the directive—it’s executing on it with confidence.

The barrier to execution 

For many organizations, the challenge often starts with the state of their HR and compensation data. In multinational organizations, compensation data often spans multiple systems, payroll providers, spreadsheets, and local processes. Job classifications vary across countries, salary bands are not consistently defined, and workforce data remains siloed across regions. 

As a result, many organizations struggle to produce consistent pay comparisons, define standardized salary ranges, explain compensation decisions clearly, and generate accurate reporting across multiple countries.��

Without a connected and reliable workforce data foundation, pay transparency becomes difficult to operationalize at scale. 

Building a foundation for continuous transparency����

The organizations making the most progress are focusing first on data consistency, workforce visibility, and connected HR processes. 

This is where technology is becoming critical. AI can help organizations move beyond manual reporting by identifying pay anomalies, surfacing unexplained pay variance, and accelerating workforce equity analysis across large, complex data sets. 

With pay transparency insights (generally available on June 5),  a capability within the  package in , organizations can unify compensation and workforce data across systems while applying AI-assisted analysis to help identify inconsistencies, support explainable pay decisions, and improve reporting readiness. 

Instead of relying on fragmented systems and disconnected reporting processes, organizations can move toward a more consistent and scalable approach to transparency. 

Three areas HR teams need to execute now 

With the right data foundation in place, organizations are better positioned to address the directive’s three major operational requirements. 

1. Enabling employee pay transparency 

Under the directive, employees have the right to request information about average pay levels by gender for comparable work. For many organizations, this��immediately exposes data consistency issues. Comparable roles may be classified differently across countries or��business units, while compensation data��often lives in disconnected systems that were never designed to work together.��

��helps organizations provide employees with pay transparency statements through����while supporting more consistent comparisons across worker groups.��These statements can give clear insight into the employee’s annual pay and the average pay of the same worker��category��broken down by gender.����

2. Preparing for candidate pay transparency 

The directive also requires employers to disclose salary ranges in job postings or before interviews and prohibits asking candidates about salary history. While this may sound straightforward, many organizations are discovering they lack standardized pay ranges, consistent job architecture, or alignment between recruiting and compensation systems. 

 allows organizations to display salary ranges directly within job postings and support more transparent hiring experiences. AI-driven recommendations can also help organizations establish more consistent pay ranges aligned to internal equity, external benchmarks, and evolving workforce needs. 

3. Meeting gender pay gap reporting obligations 

Mandatory gender pay gap reporting represents one of the directive’s most operationally demanding requirements. Annual reporting obligations begin in 2027 based on 2026 workforce data, meaning organizations need to prepare now.  

For many HR teams, the challenge is turning complex, multi-country workforce data into accurate and defensible reporting. With��, organizations can use AI-assisted analysis to��identify��potential drivers behind pay gaps, surface workforce equity insights more quickly, and support more proactive decision-making��before reporting deadlines arrive.����

What HR should do now 

The EU Pay Transparency Directive is not just introducing a new compliance requirement. It’s accelerating a broader shift toward continuous transparency in how organizations manage compensation, hiring, and workforce equity. 

The organizations best prepared for this shift are taking action now to: 

• Unify workforce and compensation data��
• Standardize job and pay structures��
• Improve reporting readiness��
• Build more consistent, explainable compensation processes across the business

As transparency expectations continue to grow among employees, candidates, regulators, and business leaders, pay equity can no longer operate as a periodic reporting exercise. It is becoming an ongoing operational capability. 

Watch the  to learn how to move from policy to execution and prepare your organization for EU Pay Transparency requirements at scale.  

Maryann Abbajay is chief revenue officer for �鶹ԭ�� SuccessFactors.

WALLDORF —��, rated A1 (stable) by Moody’s and A+ (stable) by S&P Global, successfully completed a Eurobond transaction with a total volume of €3.5 billion across four tranches with tenors of two, three, five and seven years. The net proceeds from this transaction are used for general corporate purposes, including (re)financing of recently announced acquisitions.

This announcement does not contain or constitute an offer of, or the solicitation of an offer to buy or subscribe for, securities to any person in Australia, Canada, Japan, Singapore or the United States of America (the “United States”) or in any jurisdiction to whom or in which such offer or solicitation is unlawful. The securities referred to herein may not be offered or sold in the United States or to, or for the account or benefit of, U.S. persons, absent registration under the U.S. Securities Act of 1933, as amended (the “Securities Act”) except pursuant to an exemption from, or in a transaction not subject to, the registration requirements of the Securities Act. Subject to certain exceptions, the securities referred to herein may not be offered or sold in Australia, Canada, Japan or Singapore or to, or for the account or benefit of, any national, resident or citizen of Australia, Canada, Japan or Singapore. The offer and sale of the securities referred to herein has not been and will not be registered under the Securities Act or under the applicable securities laws of Australia, Canada, Japan or Singapore. There will be no public offer of the securities in the United States.

This announcement is a general information and not a prospectus for the purposes of Regulation (EU) 2017/1129. Investors should not purchase or subscribe for any securities referred to in this announcement except on the basis of information in the prospectus relating to such securities, which, when published, will be available on the website of the Luxembourg Stock Exchange ().

This announcement is directed at and/or for distribution in the United Kingdom only to (i) persons who have professional experience in matters relating to investments falling within article 19(5) of the Financial Services and Markets Act 2000 (Financial Promotion) Order 2005 (the “Order”) or (ii) high net worth entities falling within article 49(2) of the Order or (iii) persons to whom it would otherwise be lawful to distribute it (all such persons are referred to herein as “relevant persons”). This announcement is directed only at relevant persons. Any person who is not a relevant person should not act or rely on this announcement or any of its contents. Any investment or investment activity to which this announcement relates is available only to relevant persons and will be engaged in only with relevant persons.

Visit the . Get �鶹ԭ�� news via  and .

Media Contacts:
Aimée Leabon, +1 (646) 799-3277, aimee.leabon@sap.com, EST 
Daniel Reinhardt, +49 151 168 10 157, daniel.reinhardt@sap.com, CEST
�鶹ԭ�� �鶹ԭ�� 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 �鶹ԭ��’s 2025 Annual Report on Form 20-F. 
© 2026 �鶹ԭ�� SE. All rights reserved.  
�鶹ԭ�� and other �鶹ԭ�� products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of �鶹ԭ�� SE in Germany and other countries. Please see  for additional trademark information and notices.  

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. “City employees expected the same simplicity and modern design from their workplace software, but the old systems are causing friction and frustration.”

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 �鶹ԭ�� Fiori interface delivered the modern, intuitive experience everyone had been waiting for. “That’s what we’re probably most excited about,” Hawkes says. “With potentially powerful new AI capabilities at their fingertips, the city’s team can now exceed expectations, instead of struggling to meet them.”

This new technology also became a powerful tool in the battle for talent. Hawkes sees the integrated AI tools as a “great leveler,” 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 �鶹ԭ�� 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’t be on guard 24/7. “We’re not, [but] they are,” he says of �鶹ԭ��’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’d 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’s story is a testament to what’s possible when vision, strategy, and technology align. It’s a narrative of turning daunting challenges into defining opportunities and building a government that’s 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 �鶹ԭ�� America.

The version for , part of the �鶹ԭ�� Services and Support portfolio, is the helping organizations adopt, activate, and scale the �鶹ԭ�� Customer Experience and AI innovations announced at �鶹ԭ�� Sapphire.

The proactive, expert-led engagement model is built to de-risk transformation, accelerate time to value, and sustain measurable outcomes across customer experience initiatives. It combines guided adoption, prescriptive functional and technical assistance, AI-powered best practices, and continuous value realization aligned to the realities of modern customer experience (CX): AI at the core, unified data, omnichannel at scale, retention over acquisition, service-led growth, and persistent skills gaps in a rapidly evolving digital landscape.

At its heart, the Advanced Success Plan for �鶹ԭ�� Customer Experience brings together the right expertise at the right time, program governance, solution experts, value advisors, and adoption specialists. This helps teams execute faster and smarter with �鶹ԭ�� Customer Experience.

What sets the Advanced Success Plan apart

• Outcome-based: Business outcomes and key value indicators are co-defined with teams, with milestones and workstreams aligned to deliver measurable Autonomous CX results.
• Proactive by design: AI Assistants, adoption checks, and innovation accelerators are embedded throughout, reducing risk and compressing time to value as agentic capabilities evolve.
• Continuous enablement: Role-based best practices and coaching are tied directly to the Autonomous CX road map, closing skills gaps at pace as new AI and platform capabilities become available.
• Cross-solution orchestration: Unified processes and shared business context across marketing, commerce, sales, and service break silos and enable enterprise-scale execution.

This is the first of a planned series to deep dive on the topics below. Here, we start with introducing how the Advanced Success Plan for �鶹ԭ�� Customer Experience helps operationalize seven macro trends shaping modern customer experience.

1. AI‑powered customer experiences

AI now underpins everything from next best engagement to intelligent service resolution. The Advanced Success Plan embeds AI adoption patterns directly into the delivery approach, identifying high value use cases, calibrating data prerequisites, and guiding model governance.

The results are prioritization of high‑impact starting points, a plan to scale with guardrails, accelerating time from pilot to production and grounding every decision in �鶹ԭ��’s CX AI capabilities and product road map.

2. Hyper‑personalization at scale

Personalization demands more than algorithms; it requires clean, consent‑aware data, robust decisioning, and experimentation discipline. The Advanced Success Plan delivers:

• Data readiness assessments and integration patterns to enrich customer profiles and segments
• Governance and testing playbooks to validate personalization hypotheses at scale
• Prescriptive journeys to operationalize next best action across every customer channel

The result: hyper personalization moves from proof of concept to standard operating model.

3. Unified customer data and breaking down silos

Siloed data undermines CX. We help establish a unified data foundation and harmonized identities, aligning business, data, and integration teams. With technical guidance and adoption accelerators, users can move faster toward a single view of the customer to fuel analytics, personalization, and service excellence.

The results are unified profile use cases, data quality baselines, and source‑of‑truth decisions to reduce duplication and latency.

4. Omnichannel commerce and B2B digital transformation

Modern buyers expect seamless journeys across web, mobile, marketplace, and partner portals, especially in B2B. The plan accelerates omnichannel capability build‑out by uniting commerce, order sourcing, pricing, and fulfilment patterns, supported by outcome‑based governance.

The result: Channel consistency, catalogue and contract complexity, and the alignment of service and sales motions are all addressed, driving measurable improvement in conversion rates and repeat purchase.

5. Customer retention over acquisition

Acquisition costs are rising and retention is the new growth engine. The Advanced Success Plan helps operationalize retention strategies, churn prediction, intelligent engagement, loyalty, and proactive service across the CX stack.

The result: We align metrics such as retention rate, customer lifetime value, and service‑to‑revenue contribution, and ensure the data foundation supports them.

6. Service as a revenue driver

Service is no longer a cost center; it’s a growth channel. We guide users to productize services, monetize value‑added offerings, and embed outcome‑based contracts. The plan includes:

• Playbooks for cross‑sell/upsell from service interactions
• Knowledge and field service patterns to improve first‑time fix and attach rates, KPI frameworks for service‑led growth

The result: With prescriptive governance and AI‑driven intelligence, service organizations move from reactive cost management to consistent, measurable contribution to top‑line revenue and customer retention.

7. Navigating digital transformation complexity and skills gaps

Large transformation programs falter on orchestration and capability enablement. The Advanced Success Plan addresses both by:

• Establishing a cadence of value sprints and decision forums
• Providing role‑based enablement covering functional and technical assistance, data, product ownership, end-user adoption, and change management
• AI-guided best practices embedded throughout delivery to eliminate rework and accelerate quality outcomes across Industry AI scenarios

Organizations execute with confidence, even amid shifting requirements, resource constraints, and rapidly evolving agentic AI capabilities.

Measurable outcomes

• Accelerated time to first value through prioritized, AI-ready use cases aligned to capabilities
• Higher adoption and sustained performance via continuous enablement
• Reduced program risk through proactive governance, telemetry, and structured decision forums
• Measurable gains in conversion rates, customer retention, and service-led revenue contribution across the full CX stack

Getting started

• Define Autonomous CX priorities: Identify two to three priority outcomes for the next two quarters facilitated by the �鶹ԭ�� Value Management service.
• Assess readiness: Evaluate data, integration, governance, and enablement gaps to define a 12 to 18 month engagement plan.
• Engage the Advanced Success Plan: Align workstreams, milestones, and metrics with our expert team.��
• Industrialize and scale: Convert proven delivery patterns into reusable accelerators, deployable across regions and lines of business.

This series will examine each of the seven trends in depth, demonstrating how the Advanced Success Plan for �鶹ԭ�� Customer Experience translates CX strategy into repeatable execution and measurable business outcomes.

Tara Tracey is a global product owner at �鶹ԭ��.

Demand shifts quickly, supply networks are more exposed to disruption, cost and margin pressure remain constant, and the decisions that determine whether a company can respond with confidence rarely sit inside one function.

The enterprise is left with a critical question: How do you move fast enough to capture opportunity without putting fulfillment, margin, or customer trust at risk?

Many of the world’s largest organizations navigate this challenge on a regular basis. It is exactly the kind of moment that exposes the limits of how enterprises currently operate. Connecting the dots across functions, systems, and decisions still takes too much time, too much manual effort, and too much stitching across fragmented landscapes. By the time teams have gathered the data, aligned the functions, modeled the trade-offs, and agreed on a response, the environment has already shifted.

This is why we introduced the Autonomous Enterprise at �鶹ԭ�� Sapphire. The goal is to sense change earlier, understand its impact across the enterprise, coordinate the right response, and keep people in control of important decisions. This is a fundamental shift in how businesses can operate: intelligence that is continuous, decisions grounded in real-time context, and an enterprise that moves as a connected system rather than a collection of disconnected parts.

Autonomy at scale

An Autonomous Enterprise is an organization that can continuously sense what is happening across its operations, reason over those signals using business context and established rules, and act across end-to-end processes without depending on manual coordination at every step. AI assistants and agents advance work across the enterprise in alignment with the goals, policies, and constraints defined by humans.

Every AI-driven action is auditable and traceable. Human judgment is deliberately embedded in decisions that require accountability and exceptions that fall outside defined parameters.

Three principles underscore the Autonomous Enterprise:

1. Process knowledge: Deep, industry-specific understanding of how a business truly runs
2. Business data: Enriched, connected, contextual data that gives AI something real to work with
3. Governance: The backbone that keeps everything upright, traceable, and within policy

Beneath it all is the �鶹ԭ�� platform, ensuring every layer works in concert, every agent operates within guardrails, and every outcome can be traced back to a decision made by a human.

Intelligence that works across the business

The average business landscape probably doesn’t look like one system, one vendor, or one clean stack. Your processes still have to run end to end across all of it: record to report, plan to make, source to pay, hire to retire, order to cash. If AI is going to work in the enterprise, it has to work across this landscape, not inside one application or vendor boundary.

IDC shows that more than 50% of business decisions still take between one and seven days. That is the gap we are closing—from days to moments.*

At the core of the Autonomous Enterprise is the �鶹ԭ�� Autonomous Suite. Joule becomes the way you interact, as a single entry point into your business. In the middle, the �鶹ԭ�� Autonomous Suite connects your core domains: finance, supply chain, spend, HCM, and customer experience. And underneath, everything is grounded in your business context, your data, your processes, your rules, your governance.

With �鶹ԭ��’s unified foundation of applications, data, and business context, AI is embedded directly into how work gets done, enabling autonomous, end-to-end execution rather than isolated use cases.

The operating model behind this is built on a clear division of responsibility: people set priorities, policies, and guardrails. Assistants understand role and process context and coordinate activity across domains. Agents carry out the defined work, detecting signals, triggering actions, and resolving routine tasks continuously in the background.

And while automation is a part of this, the bigger shift is intelligence and optimization. The system is no longer following predefined workflows. It is using business context to understand what is happening, and what should happen next. This is the shift from systems of record to systems that help run the business.

Autonomous Finance shows what changes

Finance offers a clear example of how this model changes the work itself. Many finance organizations still contend with manual steps, fragmented data, and slow cycles. In a volatile environment, that lag translates directly into slower responses to risk, missed opportunities, and diminished confidence in the decisions that shape performance.

With Autonomous Finance, more of that work can be handled by the system, allowing finance teams to spend less time chasing numbers and more time shaping decisions. The function begins to move from reconciling the past to shaping the future.

Autonomous Finance is not one capability, one agent, or one use case. It is built across the entire finance process, from planning to revenue management, treasury, closing, compliance, and tax. Within each area, assistants are supported by specialized agents working continuously in the background. Some focus on forecasting, some on billing, some on cash, and some on closing. The important point is that these capabilities are connected, so decisions in one area can flow into the others. Connected assistants, specialized agents, continuous optimization. That is the model.

The impact across these areas compounds. Finance teams reclaim meaningful capacity as manual reporting, reconciliation, and transaction processing give way to continuous intelligence. Cash cycles compress. Close timelines shorten. Forecasting becomes more accurate and more responsive to changing conditions.

Because these capabilities are connected, improvements in one area reinforce the others: faster billing flows into better cash visibility, which flows into stronger planning confidence, which flows into more decisive action at the executive level. Compliance strengthens as well, not through added controls, but through better intelligence embedded in the process itself, supporting requirements across ISO, SOC, and SOX with greater accuracy and less manual effort.

The result is not incremental improvement in isolated tasks. It is a fundamentally different operating posture for the finance function, one where the system handles orchestration and people direct outcomes.

Industry AI adds depth

Autonomous domains give breadth across business functions, while Industry AI provides the depth of knowledge. The same supply chain problem looks very different in life sciences, in industrial manufacturing, in agribusiness, in retail, or in energy. The rules, regulations, data models, and value chains are different.

�鶹ԭ�� is not starting from generic AI and trying to teach it how an enterprise works. We start with decades of industry and process knowledge, already embedded in the systems that run the world’s most complex businesses. Our AI is grounded in sector-specific processes, end-to-end value chains, operational realities, and compliance requirements. And our ecosystem extends this with specialized expertise, so organizations can adapt the intelligence to their markets and their industries.

This is not AI for the sake of AI. This is AI applied to the real operating model of each industry.

The path forward

That is the real shift: not AI operating in isolated tasks, but AI helping the enterprise continuously sense, reason, act, and learn. People remain in control throughout, while the system handles the orchestration required to bring together the right data, context, and decision at the right moment.

The Autonomous Enterprise marks a shift from managing processes to directing outcomes. It moves organizations from reacting to events to anticipating them, and from stitching together decisions after the fact toward helping the business move as one connected system.

This does not require waiting for a perfect, fully transformed landscape. Organizations can begin by applying AI on top of existing landscapes and evolving their business as they go. That work is already underway with many of our customers. What they have in common is that they are starting now, moving faster, making better decisions, and building the foundation for a more autonomous enterprise, step by step.

This is a journey. And it begins with the recognition that the enterprise of the future will not be defined by how efficiently it executes predefined processes, but by how intelligently it can sense change, weigh trade-offs, and move with confidence when it matters most.

For more on �鶹ԭ��’s broader Autonomous Enterprise announcement, read The Future of the Enterprise Is Autonomous. For more details on 2026 �鶹ԭ�� Sapphire announcements, see the .

Manoj Swaminathan is general manager and chief product officer of �鶹ԭ�� Autonomous Suite, Finance & Spend, and member of the Extended Board of �鶹ԭ�� SE.
Eric van Rossum is chief marketing officer of �鶹ԭ�� Global Product Marketing and chief product officer of �鶹ԭ�� Industries and Globalization.

*IDC Resource Map for �鶹ԭ��, �鶹ԭ�� Custom Survey 2026: Enterprise Process Automation Survey– April 2026, sponsored by �鶹ԭ��, doc #US54531626 _RMD , May 2026

The difference is not the model. It is context: the ability for AI to understand how a business actually runs. 

Much of today’s AI discussion centers on agents, along with models and benchmarks. Which model performs best? Which system completes the most tasks? Which interface feels most natural? These factors matter, but they do not solve the central enterprise challenge.

Companies run workflows that cut across teams, policies, approvals, authorizations, and data. They plan, source, produce, hire, pay, and serve through systems that carry real business consequences. AI only creates durable value at scale when it operates inside this reality.

Models generate answers. An agent can complete a task. But running a business requires something more. It requires an understanding of how work gets done, who is authorized to act, which rules apply, and how decisions connect across functions. Without that context, AI simply can’t deliver on its promise.

That is one reason I believe AI raises the premium on software with deep business context. It allows companies to fundamentally reinvent how work gets done. When AI agents understand end‑to‑end processes, they can operate across functions, execute workflows autonomously, and coordinate actions in real time. Instead of automating individual steps, AI can run processes end to end, freeing employees from repetitive coordination and enabling them to focus on higher‑value judgment, oversight, and strategy.

This is what we describe as the Autonomous Enterprise, a fundamental shift from systems of execution to systems that can reason, decide, and act. A vision where �鶹ԭ�� is poised to lead. 

For more than five decades, we have powered the core processes that run the world’s leading organizations. Our systems don’t just store data; they encode how businesses actually operate: their processes, rules, and decisions. Our ERP is the institutional memory and the brain of many companies across industries and around the globe. Our new �鶹ԭ�� Business AI Platform brings together enterprise data, processes, and governance into a unified context for AI.

Building on this foundation, Joule is the interaction layer that connects people with AI and redefines how they interact with software. Joule Assistants collaborate with users, while Joule Agents execute business workflows end to end. This is how intelligence becomes embedded directly into operations, not added on top. We call this the .

“Show me how my financial forecast for the year could change based on the latest pipeline and supply chain data.” On the surface, this looks like a simple prompt directed to a large language model.��But disconnected from enterprise systems, the answer is��mere��speculation.

Grounded in the full context of the business,��the system first identifies the correct business process from��hundreds��of��mission‑critical processes and understands the specific configuration that governs how this process runs in your organization. It then selects exactly the right data from��millions��of��data fields stored across the ERP landscape. Finally, every step is checked against identity, authorization, and access controls, ensuring the result is accurate, compliant, and trustworthy. This is how enterprises move beyond generic, probabilistic answers toward decisions they can rely on.

Reaching this state requires more than adding a chatbot or layering AI on top of existing systems. Many enterprises still operate with fragmented landscapes, data spread across systems, and processes shaped by years of incremental change. In this environment, AI cannot simply be “bolted on” or layered onto fragmented, outdated systems. It does not accelerate progress. It amplifies inefficiency and risk. Companies must rethink how their processes, data, and infrastructure work together and how humans and AI share responsibility. This is not only a technical shift. It is a change‑management challenge. 

New technology only creates value when it is accompanied by real change. AI does not replace transformation. It raises the return on transformation done well. And it comes to life only when every element of the system—the agent, the process, and the human—works together by design. People need to understand how to work with AI agents, and processes must be intentionally shaped to embed intelligence where decisions and execution happen.

This is why change management is foundational. It means reskilling employees, re‑engineering processes to connect them directly with data and AI, and modernizing the underlying landscape. 

That is why we are introducing new��AI-led RISE with �鶹ԭ�� and �鶹ԭ�� GROW��offerings��and fundamentally resetting our services model: to help companies modernize, navigate change, and turn AI from potential into sustained business value at their own pace.��

This marks the beginning of a new era of enterprise software:��where intelligence is not separate from��operations but embedded within them.��The companies that lead will not be those with the most advanced models in isolation, but those that connect AI to the way their business actually runs—with context, governance, and trust.��

This is the dawn of the Autonomous Enterprise, and �鶹ԭ�� is uniquely positioned to help the world’s leading organizations realize its full potential. 

Christian Klein is CEO of �鶹ԭ�� SE.

“Every motorcycle we sell comes with an experience,” he explains. “Customers 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.”

Many people in Colombia get around every day on motorcycles—often on ‘Boxer’ 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 . “Every motorcycle leaving our factories follows best quality practices,” he says. “And we pass that value onto our customers.”

In addition to Colombia, UMA operates in five countries in Central America: Guatemala, El Salvador, Costa Rica, Honduras, and Nicaragua. “We’re also in Venezuela, as well as Spain and Portugal,” Urrea Ospina says. “Across all these regions, we aim to make a positive impact on our customers with each of our brands and motorcycles.”

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“For us, our ‘Adventure’ project—the name we gave the �鶹ԭ�� project—was crucial to our expansion and growth,” he says. “We needed global models so we could expand faster and run more efficient, automated operations.”

Working with �鶹ԭ��, UMA established global models and automated operations across finance, production, and logistics, says Victor Bedoya Aristizabal, corporate solutions manager at Grupo UMA. “We 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.”

“Today, �鶹ԭ�� is a fundamental pillar for our processes from the start of the value chain to the end,” Bedoya Aristizabal says. “We utilize the full suite for production, pricing, finance, and warehouse management to deliver a superior product.”

“Taken together, these cross-cutting global modules have helped us evolve, save time, shorten implementations, and expand across regions,” Urrea Ospina says. “For example, in Colombia we’re implementing our spare parts warehouse with �鶹ԭ�� Extended Warehouse Management, �鶹ԭ��’s supply chain management application. What we’re learning in Colombia can be replicated in Central America. Standardizing these logistics modules makes operations much more efficient while still accounting for each country’s specific needs.”

He adds: “I believe that if we hadn’t prepared with �鶹ԭ�� and the technologies we have today, the strong growth we’ve 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—nearly doubling our output. Without �鶹ԭ��’s 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.”

“That ability to scale—both assembly volume and sales volume—has been driven by the Adventure project and everything we implemented with �鶹ԭ��,” he says. “I think it has significantly improved employees’ day-to-day work. And that’s where we need to take the organization: toward a data-driven company that makes better decisions. Before we implemented Adventure, a person’s day-to-day was mostly operational work.”

Urrea Ospina emphasizes that one of the main motivations UMA had for adopting �鶹ԭ�� Cloud ERP Private was to have concrete, real-time information—for example, knowing exactly the assembly cost of a motorcycle. “That is key for an organization,” he says. “Beyond that, another need was to remove the manual work that all the company’s areas had, working and operating completely by hand.”

Luis Orrego, production supervisor at Grupo UMA, agrees: “Instead of having many Excel files open with multiple sheets and trying to reconcile everything, we now consolidate the data in one place. With �鶹ԭ��, all the sheets and reports I used to present are now available in a single view. I don’t have to run multiple steps, and I can see all my indicators: capacity, materials, and line resources.”

Another motivation was UMA’s expansion plans to enter other regions and countries efficiently. “We wanted defined global models that we could replicate quickly with �鶹ԭ�� in the regions where we operate,” Urrea Ospina explains. “Those factors led us to undertake the �鶹ԭ�� project, which has been a highly strategic and very successful initiative.”

Implementing �鶹ԭ�� Cloud ERP Private has also had a profound impact on Urrea Ospina’s technology team. “It completely changed our roles and what we focused on,” 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’re going and what we can implement next for the business. Culturally, �鶹ԭ�� gave us confidence through information traceability, and it also gave us time to think. It opened the door to innovation—especially around analytics.”

David Aguirre is a media production specialist on the Multimedia Team for �鶹ԭ�� News.
Rana Hamzakadi is deputy head of the Multimedia Team for �鶹ԭ�� News.

In today’s dynamic business landscape, characterized by the rise of AI, process intelligence is vital for gaining data-driven insights that boost efficiency, support smarter decision-making, and drive business growth—and �鶹ԭ�� Signavio maintains an ongoing dedication to innovation and customer satisfaction in this arena.

We believe this year’s evaluation reflects the expanded category definition from Gartner, assessing platforms that unify process mining, task mining, modeling, analysis, optimization, monitoring, automation discovery, and governed repositories within a single, integrated environment.��

The Gartner® Magic Quadrant™ research methodology highlights that “Leaders execute well against their current vision and are well positioned for tomorrow.” �鶹ԭ�� Signavio has been recognized as a Leader among 16 vendors, evaluated based on its Ability to Execute and Completeness of Vision.

“Our aim is to help our customers create a repeatable, sustainable transformation capability, rather than treating it as a series of independent projects,” Dee Houchen, chief marketing officer at �鶹ԭ�� Signavio, said. ”We believe process intelligence is fundamental to what this approach can deliver for organizations today, driving greater enterprise observability, smarter decision-making, accelerated transformation, and measurable business outcomes. With �鶹ԭ����Signavio, this is made possible through one unified��suite,��empowered��by deep AI capabilities,��as well as intelligent��agents��and assistants.��We are proud of being recognized as a Leader by Gartner, and feel this reaffirms our ongoing commitment to innovation and customer satisfaction, with value realization as our guiding principle.”

As ever, the Gartner report provides useful context regarding the state of the process intelligence market. See why we feel we were recognized as a Leader in the report:

• Our innovative process atoms concept serves as the AI-ready foundation for business transformation, gathering and curating an AI-consumable layer of “company memory” that can provide the precise, contextual process knowledge AI agents need in order to take informed action.
• Our technology, approach, and value accelerators help achieve full enterprise observability and surface actionable insights with an end-to-end integrated suite, supporting organizations to identify, prioritize, and prove the value of your transformation initiatives, track and showcase ROI, and maximize business impact.��

The Gartner Magic Quadrant equips businesses with valuable insights to make informed decisions. For a complimentary copy of the latest resource featuring �鶹ԭ�� Signavio solutions (Gartner, Magic Quadrant for Process Intelligence Platforms, by��,��,��, 5 May 2026), visit .

Lucas de Boer is global marketing program lead for �鶹ԭ�� Signavio.

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner and Magic Quadrant are trademarks of Gartner, Inc and/or its affiliates.

Workforce scarcity impacting global logistics operations, logistics corridors becoming tools in geopolitical conflicts, and continuously rising transportation costs all point to the same reality:��Future-proofing logistics is no longer a strategic choice, but the critical tipping point that separates those who will rise from those who will fall in an increasingly unforgiving global economy.

Modernizing logistics is no longer about “if” but how fast and how far

Traditional logistics systems were designed primarily for efficiency. Today’s supply chains, however, are more complex and exposed to even more risk, requiring resilience, agility, and smarter decision‑making across warehousing and transportation operations.

�鶹ԭ��ure is constant and from multiple directions, including geopolitical tensions, economic volatility, stringent government regulations, and rising customer expectations. The core challenge is no longer reacting to individual disruptions but overcoming constant firefighting. Leaders are seeking a clear path toward a more agile and future‑ready logistics strategy. Two questions sit at the center of this challenge:��How fast do logistics operations have to change? And how complex are the operations that must be supported?

Answering these questions starts with examining the technological foundations that power your current logistics operations. Long-standing solutions—such as �鶹ԭ�� ERP Central Component modules for logistics execution – warehouse management (LE-WM) and logistics execution – transportation (LE-TRA) as well as �鶹ԭ�� Extended Warehouse Management and �鶹ԭ�� Transportation Management—have served reliably for decades, yet �鶹ԭ�� continues to evolve to serve today’s demands for real-time adaptability and scale.

�鶹ԭ��’s logistics portfolio has supported customers across three decades and continues to evolve with adaptive, cloud-native, AI-driven platforms capable of learning, predicting, and orchestrating logistics processes autonomously, because that is what companies need to compete now and in the future.

The right journey depends on innovation speed, risk tolerance, operational complexity

Future‑proofing logistics ultimately comes down to choosing the right modernization path. Every organization has a unique starting point, depending on how existing solutions have been implemented across the business. To choose the right next step, leaders need to understand the available options, determine what fits with the organization’s appetite for innovation, and assess the transformational impact on the team.

Gartner’s logistics complexity model, breaking down process complexity across five levels, can help when setting a transformation strategy.

Companies operating at levels one to three of Gartner’s model are typically characterized by manual or semi-automated processes, regional distribution, and lower complexity. These companies are best-suited for SaaS-native warehouse and/or transportation solutions that offer the implementation speed of standardized workflows, cloud qualities such as flexible, mobile interface, and advantages from improved scalability and lower TCO.

Organizations at levels four and five require warehouse automation for high volume distribution, multi-modal transportation, and orchestration across multiple ERP systems. These companies seek the benefit of dedicated cloud environments that offer a higher level of adaptation for more control over their operations and their solution landscape.

�鶹ԭ�� offers different pathways to help businesses run and modernize across the span of operational complexity. Below are three journeys to guide strategic planning for modernization. Each journey can be aligned to high-level business ambitions, accounting for process complexity, innovation speed, and risk tolerance.

Journey 1: Sustain temporary stability while preparing for modernization

Moving legacy �鶹ԭ�� ERP Central Component modules LE-WM and LE-TRA to �鶹ԭ�� S/4HANA for stockroom management keeps operations supported until 2040, offering more time to manage your transformation. This journey is about sustaining what works until you need to evolve. It is designed for organizations that need more time to move their decades-old warehousing solution into the future. This approach, rooted in stockroom management, extends the life of a proven system without prematurely forcing change. It is a temporary but intentional holding pattern for conservative logistics strategies based on a firm foundation. Throughout this journey, �鶹ԭ�� Logistics Management can be the go-to solution when you are ready to move from stockroom management.

Journey 2a: Modernize logistics in controlled steps

For organizations that know modernization is essential but need to be mindful of organizational change management, this journey offers a stable path. It begins with moving on-premise �鶹ԭ�� ERP Central Component modules LE-WM and LE-TRA to �鶹ԭ�� S/4HANA Cloud, private edition, with basic warehouse management and transportation management capabilities, creating a modern foundation for future readiness. From there, companies can evolve their processes toward more advanced options as their needs grow (see journey 2b). This journey is best suited to businesses that want flexibility and control in their operations as well as in their change management. This journey also supports deeply integrated logistics that align with the RISE with �鶹ԭ�� journey.

Along this journey, if you find that your business complexity is manageable, �鶹ԭ�� Logistics Management can even be incorporated as the most modern, AI-native �鶹ԭ�� solution for logistics to complement your ongoing operations.

Journey 2b: Move large-scale logistics to the cloud

This journey supports organizations that need to manage highly specific processes, high-automation, and mission-critical logistics execution with one or multiple ERP systems. You can migrate from �鶹ԭ�� Extended Warehouse Management and �鶹ԭ�� Transportation Management to the cloud with �鶹ԭ�� S/4HANA Cloud for advanced extended warehouse and transportation management without losing depth or control. This path can also serve as a second step for advancing operations from basic to advanced �鶹ԭ�� S/4HANA Cloud in order to support growing business needs.

Journey 3: The modernization gamechanger for logistics

For innovation leaders ready to sprint ahead of the competition, this journey represents the direct path to the most modern logistics operating model. Companies move from on-premise �鶹ԭ�� ERP Central Component modules LE-WM and LE-TRA to AI-native��, bringing together cloud delivery, embedded intelligence, and network connectivity in a single solution for warehousing and transportation.

This is the right path for businesses that want to standardize faster, simplify their landscape, and take advantage of continuous innovation. You gain a more connected and adaptive logistics model with built-in AI and a carrier network. Along this journey, you are setting the pace for modern supply chains.

Future of logistics: Cloud-first, AI at the core, modular by design

Logistics is accelerating into a new era, one defined by both efficiency and intelligent adaptability and autonomous orchestration. Embodied in its evolving logistics portfolio, �鶹ԭ��’s strategy offers a clear, flexible, and future-ready path for companies at every level of complexity.

Whether maintaining existing operations, maturing gradually as complexity increases, or leapfrogging to leading-edge capabilities, you can now compose and run your entire logistics operations on �鶹ԭ��. Basic to moderate complexity processes are supported with �鶹ԭ�� Logistics Management, and advanced and highly automated business with S/4HANA Cloud EWM and TM. By combining the two, you can design and achieve logistics networks to improve speed, agility, and resilience.

To explore which��logistics��journey fits your business best, don’t��miss our “Future of Supply Chain” conversation with �鶹ԭ�� executive��Till��Dengel:��.

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 �鶹ԭ�� 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 �鶹ԭ�� journey and �鶹ԭ�� Business Technology Platform (�鶹ԭ�� BTP). The former is a comprehensive journey that combines the elements needed to migrate to the private cloud under a single contract: �鶹ԭ�� 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 �鶹ԭ�� into the city’s 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’s 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 “Golden Records” 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), “�鶹ԭ�� 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.”

Carlos Lacerda, Senior Vice President and Managing Director of �鶹ԭ�� Southern Europe, stated: “�鶹ԭ�� 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.”

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 “paperless” administration: The “paperless” 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 �鶹ԭ�� 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 �鶹ԭ�� 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: �鶹ԭ�� 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.

Visit the . Get �鶹ԭ�� news via  and .

Media Contact:
Belén Martinez Millán, �鶹ԭ�� Spain, +34 91 4567220, belen.martinez@sap.com, CET
�鶹ԭ�� �鶹ԭ�� 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 �鶹ԭ��’s 2025 Annual Report on Form 20-F.
© 2026 �鶹ԭ�� SE. All rights reserved.
�鶹ԭ�� and other �鶹ԭ�� products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of �鶹ԭ�� SE in Germany and other countries. Please see for additional trademark information and notices.

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

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

“Once you scale AI, it stops being an AI problem—and becomes a data problem,” said Esra Kocatürk Norell, Vice President, Customer Experience, Enterprise IT at Ericsson. “That’s why we invested early in a business data fabric. With �鶹ԭ�� Business Data Cloud, we can define what data means once—from revenue to market structures and access rules—and apply it consistently across the enterprise. That’s what allows us to scale AI in a way that is trusted, repeatable and delivers real business value.”

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

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

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

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

“Ericsson’s approach shows how leading companies are moving from AI experimentation to execution by focusing on data, governance and business context,” said Manos Raptopoulos, Global President Customer Success Europe, APAC, Middle East and Africa at �鶹ԭ�� SE. “Together, we are helping organizations unlock the full potential of AI at scale.”

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

Visit the . Get �鶹ԭ�� news via  and .

Media Contact:
Ulrika Wass, +46 73 827 1074, ulrika.wass@sap.com, CET
�鶹ԭ�� �鶹ԭ�� 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 �鶹ԭ��’s 2025 Annual Report on Form 20-F. 
© 2026 �鶹ԭ�� SE. All rights reserved.  
�鶹ԭ�� and other �鶹ԭ�� products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of �鶹ԭ�� 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 �鶹ԭ�� and Humanoid—a UK-based robotics and AI company—to explore how integrating embodied AI–powered robotics could redefine material flow across its automotive manufacturing environment. Using Joule and embodied AI capabilities from �鶹ԭ��, 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 �鶹ԭ�� S/4HANA and enabled by the �鶹ԭ�� 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—identifying, transporting and delivering materials to the line while continuously confirming back into �鶹ԭ�� 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.

“Our 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,” said Özlem Altınışık, Group Intelligent Technologies Director at Martur Fompak International. “The 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.”

“Martur Fompak International exemplifies what it means to turn AI ambition into real business value on the shop floor,” said Emmanuel Raptopoulos, Chief Revenue Officer, EMEA, MEE and APAC, �鶹ԭ�� SE. “By embedding �鶹ԭ�� Business AI directly into their physical operations, they are not only boosting throughput and operational resilience—they 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 �鶹ԭ�� Innovation Awards—a testament to their boldness in turning intelligent enterprise vision into real-world impact.”

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% �鶹ԭ�� software–driven 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 �鶹ԭ�� Business Technology Platform to scale AI-driven workflows and integrations—supporting both operational efficiency goals and broader sustainability commitments.

Visit the��. Get �鶹ԭ�� news via�� and .

Media Contact:
Ekin Tayali, +34 673019169, ekin.tayali@sap.com, CET
�鶹ԭ�� �鶹ԭ�� 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 �鶹ԭ��’s 2025 Annual Report on Form 20-F.
© 2026 �鶹ԭ�� SE. All rights reserved.
�鶹ԭ�� and other �鶹ԭ�� products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of �鶹ԭ�� SE in Germany and other countries. Please see for additional trademark information and notices.

Every week brings another announcement about smarter copilots, more capable agents, or new orchestration layers designed to automate work across the enterprise. The progress is undeniable. But much of the market is not optimizing for how businesses operate.

That distinction is more important than many realize. Because enterprises do not run on prompts. They run on execution.

A global manufacturer deciding how to reroute inventory during a supply chain disruption needs more than simply an answer. It must evaluate supplier alternatives, inventory availability, customer commitments, and financial tradeoffs simultaneously. A CFO forecasting liquidity exposure during market volatility needs context that a simple chatbot interaction can’t provide. These are interconnected operational decisions shaped by dependencies, preferences, approvals, financial consequences, and tradeoffs that ripple across the business in real time.

In countless conversations I’ve had with executives over the past year, the discussion inevitably shifts from AI capability to operational reality. The models are improving quickly. The harder question is whether AI understands the business environments it is operating within.

Today, too much of the AI conversation still assumes that better models alone will produce better business outcomes. They will not. Enterprises are discovering that intelligence disconnected from operational context – the processes, the data, the rules and policies that govern and protect your organization – can generate activity without creating much progress. In some cases, it can create more fragmentation and risk.

A generated recommendation may sound convincing while missing critical dependencies elsewhere in the system. An AI agent may automate one workflow efficiently while disrupting planning assumptions in another. Enterprises do not suffer from a shortage of AI outputs. They suffer from a shortage of AI systems capable of understanding operational consequences.

That is the real challenge now emerging in enterprise AI and solving it requires something deeper than orchestration. It requires context.

For decades, enterprise software has quietly served as the operational backbone of the global economy. Finance systems, supply chains, procurement networks, workforce planning platforms, manufacturing operations, and customer fulfillment processes all run through interconnected systems that capture not just information, but the logic of how businesses function. They contain years of accumulated process knowledge and data, governance structures, authorizations, policies, and economic relationships that shape every decision a company makes. They are the institutional memory of the enterprise.

In the AI era, that business context becomes enormously valuable. Without it, AI’s outputs remain educated guesses rather than grounded judgments.

When AI is grounded directly inside operational processes, it can begin to reason across the full reality of the enterprise. That changes the role software plays inside organizations. Enterprise systems are beginning to participate directly in execution itself.

AI can identify risks earlier, coordinate responses across functions, recommend actions in real time, and automate routine execution within defined boundaries. Not as isolated agents operating independently, but as intelligence connected to the economic and operational fabric of the enterprise itself. 

Importantly, autonomy in enterprise does not mean removing humans from decision-making. It means reducing the friction, fragmentation, and administrative drag that prevents organizations from operating with speed and coherence at scale. ��People still define priorities, make judgment calls, and hold accountability. But AI can help coordinate and execute the operational work surrounding those decisions.

Consider a supplier disruption affecting a critical manufacturing component. Most AI systems today can summarize the issue or predict likely delays based on learned patterns. But operationally grounded AI can move beyond insight into coordinated execution. It can identify affected production schedules, evaluate inventory positions globally, assess alternative sourcing options, estimate financial exposure, flag customer delivery risks, and recommend actions across procurement, logistics, finance, and customer operations simultaneously.

That is not simply workflow automation. It’s an entirely new way for humans and systems to interact.

This is also why I believe the AI era will increase the strategic importance of enterprise systems, not diminish it.

As AI moves closer to execution, the systems that matter most will be the ones capable of grounding intelligence in operational and transactional reality. The value shifts toward systems that understand permissions, policies, dependencies, processes, financial consequences, and organizational accountability at enterprise scale.

This shift also changes how leaders should think about transformation.

The first phase of enterprise AI adoption focused heavily on experimentation. Companies tested copilots, deployed pilots, and automated isolated tasks. Few delivered productivity gains and fewer fundamentally changed how organizations operate.

The companies that lead in the next phase will approach AI differently. They will connect intelligence directly to the operational systems where decisions carry real economic consequences. They will recognize that trustworthy AI depends not only on governance, but on context, data quality, process integrity, and transactional understanding.

Most importantly, they will understand that successful AI adoption in enterprises is not only a technical shift. It is a change management challenge. Real value comes to life only if AI agents, processes, and humans work in concert.

The future belongs to enterprises that strike this balance: humans defining priorities and holding accountability, while intelligent systems coordinate and execute with precision – enabling businesses to navigate an increasingly complex world with greater resilience, productivity, and intelligence.

Christian Klein is CEO of �鶹ԭ�� SE.

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That idea framed a , where Thomas Saueressig, chief customer officer and member of the Executive Board of �鶹ԭ�� SE, and Jan Gilg, global president of Customer Success & Americas and member of the Extended Board of �鶹ԭ�� SE, set out the company’s 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.

“Every day, billions of people wake up trusting that the world simply runs,” Saueressig said.

But making that happen is anything but simple. Saueressig pointed to the hidden complexity behind everyday routines — 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.

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

That challenge led directly to �鶹ԭ��’s vision for the — 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

“It’s this need for trusted, seamless integration that led us to our vision for the Autonomous Enterprise,” 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 �鶹ԭ��’s role as helping customers get there: “Our goal is to help you become an Autonomous Enterprise step-by-step. … We are making AI value real today.”

He linked that approach to RISE with �鶹ԭ��, �鶹ԭ��’s AI offerings, and the �鶹ԭ�� 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

“When you are fully committed to RISE with �鶹ԭ��, we are committed to support you at every step,” 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 —  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’s most demanding environments.

“Transformation is not the goal. Readiness is for us,” said Maria Demaree, SVP and CIO of Lockheed Martin Corporation, stressing that the stakes are “human” when systems support national defense and allied missions. Readiness, she explained, means the ability to move “with speed, clarity, and confidence across the enterprise.”

Through its largest transformation investment in the company’s history, Lockheed Martin is redesigning processes end-to-end, connecting fragmented systems, and embedding AI into a model-based enterprise built on �鶹ԭ��.

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 “transformation doesn’t start with technology. You must rethink your processes.” �鶹ԭ��’s role, she said, has evolved from vendor to trusted partner understanding Lockheed Martin’s 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 �鶹ԭ�� 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’s commercial flights, they need to keep airport operations running during severe winter weather. This has historically relied on manual, fragmented processes — 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.

“We passed from a reactive to a proactive model,” said Gustavo Sabato, Chief Information Officer of Aeropuertos Argentina, highlighting expected benefits, including a 16% cost reduction and lower CO₂ 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 �鶹ԭ�� R/3 to �鶹ԭ�� S/4HANA in 2023 and building the solution on �鶹ԭ�� Business Technology Platform.  While integrating multiple non-standardized data sources was challenging, the result is now that the company operates with “only one version of the truth,” 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. “Our goal is not short-term optimization but long-term agility: standardizing on industry best practices, establishing a clean core, and becoming upgrade stable,” he said.

He emphasized that both the transformation and the company’s AI ambitions depend on a strong foundation. “If you can’t get this foundation right, you will continue to pay the price for it,” 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 �鶹ԭ��, standardizing processes and establishing a clean core.

That unified backbone now supports Levi’s 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% — often submitted by smaller customers through handwritten notes, emails, or unstructured documents — previously took two to five days to process manually.

“Now, with the agents that we’ve built on top of �鶹ԭ��, that process takes 20 to 30 minutes,” 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. 

�鶹ԭ�� 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 — from landscape analysis to custom-code assessment — can now be completed in a single weekend.

The world doesn’t 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’t 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’s 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.

“The world doesn’t break because of change,” he said. “It breaks when change moves faster than resilience. And that’s where �鶹ԭ�� comes in.” 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. “The future isn’t written by AI.  It is written by us,” he said.

Currently in beta, the agents help organizations deliver measurable results: a greater than 50% reduction in packaging compliance review hours, scenario simulation time cut from a day to 20 minutes, up to 80% reduction in manual GHS classification effort, and over 20% fewer packaging compliance errors.

The agents handle multi-step workflows that previously required hand-offs between teams and systems, including sustainability reporting preparation, packaging and product compliance assessments, carbon footprint simulation, and workplace safety documentation. They address mounting pressure across the enterprise: giving finance teams visibility into how carbon exposure affects forecasts; helping procurement teams manage regulatory risk without slowing down innovation; enabling supply chain teams to spot emission hotspots while maintaining service levels; and supporting operations in connecting safety observations to proactive, audit-ready actions.

New AI sustainability agents

The Sustainability Regulatory Readiness Agent helps organizations prepare for upcoming sustainability regulations such as the by translating materiality assessments into a defensible reporting scope and mapping the right data and metrics to each disclosure requirement. This enables sustainability teams to capture, validate, track, and ultimately disclose ESG information with far less manual effort.

For finance teams that need to manage carbon costs and disclosure risk while balancing the financial implications of sustainability performance, the agent automates financial-grade data mapping between material topics, regulatory requirements, and �鶹ԭ�� finance data, improving audit readiness and turning an existing materiality assessment into a clear, defensible reporting scope. Unlike a standalone sustainability point solution that only surfaces issues or a generic AI model that drafts narrative text, this agent works inside and the broader �鶹ԭ�� landscape to keep reporting scopes aligned to policy and keep underlying data structured and traceable.

The Footprint Optimization Agent brings together carbon, energy, and waste data from across Scope 1, 2, and 3 sources and pinpoints where emissions and other environmental impacts are highest across products, plants, and supply chains. It then runs side‑by‑side simulations of different reduction levers and turns the results into reports, supplier requests, and targeted initiatives that support decarbonization projects and ESG goal tracking. For operations, the agent makes it easy to test “what‑if” operational changes and see their projected impact on carbon and other environmental footprints. It reduces scenario simulation time from approximately one day to about 20 minutes, making operational decisions based on real impact projections available at workers’ fingertips. This directly addresses the financial implications of carbon exposure: with ESG data often derived from industry averages that can vary by 30 to 40% or more from actual values, the ability to simulate and act on granular, accurate data carries significant margin protection value.

The Packaging Compliance Agent reads and interprets evolving packaging regulations starting with the , maps supplier and product documentation to a structured data model, infers and flags missing information, and checks product designs for conformity at scale. It turns scattered, often unstructured packaging data into an auditable compliance record for each SKU, shipment, and product run, reducing manual review effort and error rates in the process.

Procurement and sourcing teams facing growing pressure to ensure supplier eligibility, material compliance, and traceability while managing cost and availability now have an agent that helps protect revenue by catching packaging issues before they block orders or trigger fines. This equates to a greater than 50% reduction in manual compliance review hours and over 20% reduction of packaging compliance assessment errors. As sustainability moves to the transaction level—compliance per SKU, per shipment, per product run—this kind of automated, embedded compliance capability becomes an operational necessity.

The GHS Classification and Labeling Agent collects the required input data, applies the relevant Globally Harmonized System (GHS) rules, and proposes classifications and label elements that can be used directly in downstream product compliance processes.

By automating these steps, it delivers up to an 80% reduction in manual efforts and a 60% reduction in GHS labeling and classification errors. For product and compliance teams that must keep launches on schedule and avoid shipment holds or market access denials, the agent embeds GHS product compliance into everyday workflows, turning a historically expert‑driven, error‑prone process into a consistent, auditable control point across the portfolio.

The Workplace Safety Agent supports workplace safety by analyzing reported observations and proposing follow-up tasks, risk assessments, and controls. It generates updated, approved safety instructions based on those observations to help organizations strengthen safety governance. With operations under increased pressure to ensure safe work environments without compromising service and speed of production, the agent delivers proactive, standardized safety management at scale, reducing the risk of incidents and unplanned downtime. At the same time, HR and EHS leaders can point to a clear trail of actions and updated instructions to demonstrate continuous improvement in safety culture to employees, regulators, and boards.

Only AI can deliver sustainability at scale

To ensure compliance and enhance strategic decision-making, sustainability data needs to become granular. It should move beyond a record of what happened and become a driver of future outcomes. To reach this level of insight, sustainability data needs to be analyzed at transaction level. Getting transaction-level data at scale is not something that can be done manually.

Granular sustainability data allows businesses to ensure compliance, control carbon and cost exposure, safeguard product marketability, and strengthen supply chain transparency and resilience. Perhaps most important is the ability to embed sustainability into business performance and across all business functions. This final point is the key to unlocking sustainable business autonomy.

In the sustainability context, becoming an Autonomous Enterprise means that sustainability policies are executed automatically inside enterprise workflows. This includes connecting financial and sustainability data for trusted steering, automating disclosure and performance insights, and blocking non-compliant shipments. Ultimately, sustainability becomes a governing factor in enterprise decisions, as opposed to a reporting or compliance activity.

Enterprise autonomy entails gradual AI maturation:

• Intelligence: Faster visibility into reporting and materials compliance risks across the enterprise
• Optimization: Data-driven decisions that balance cost, risk, and sustainability impact
• Autonomy: Actions executed directly within operational workflows, eliminating manual coordination

The choices enterprises make now—how data is structured, how decisions are supported, and how sustainability is integrated—will determine whether they can safely scale automation later or whether complexity and risk increase as systems evolve.

With the Autonomous Enterprise, leaders can deliver sustainable outcomes at scale.

Why �鶹ԭ��?

AI needs three things to successfully run autonomously: business and process context, data connection and integration, and a reliable governance structure.

Generic models can read data, but without business context they cannot reason how a business actually runs. They see tables, not operations, and provide recommendations that may be commercially or operationally unviable. Without data that is integrated and connected across all business departments, AI has to perform in siloes, unaware of how sustainability decisions might impact financial targets, or how procurement decisions affect supply chain risk. �鶹ԭ��’s rich ERP data foundation ensures that enterprise AI has the full business picture, not just fragments of it.

Finally, AI that lacks governance and cannot be audited or controlled can be more harmful than helpful to a business. �鶹ԭ��’s more than five decades of business process expertise anchored in governance, risk, and compliance, mean that AI for enterprise deployment can be managed safely and reliably. Sustainability agents operate within defined parameters, ensuring that automation scales without sacrificing control or compliance.

This is the foundation that makes everything possible. Without it, an enterprise has AI experiments. With it, it has an operating model.

Sophia Mendelsohn is chief sustainability and commercial officer at �鶹ԭ��.
Gunther Rothermel is chief product officer of �鶹ԭ�� Sustainability.

At �鶹ԭ�� Sapphire, that mission is being redefined for a fundamentally different environment, one in which every industry faces the same core challenge: success depends not just on what professionals know, but on how effectively they can apply that knowledge alongside AI.

Technology has already changed. What now differentiates organizations is not access to innovation, but the ability to translate it into outcomes. According to the , skills gaps are the primary barrier to transformation, ranking ahead of investment constraints and regulatory complexity. Closing that gap requires more than expanding training catalogs. It requires rethinking how skills are built, validated, and continuously developed.

Certification reimagined

to reflect how work actually gets done. Across more than 100 certifications, traditional multiple-choice exams have been replaced with scenario-based and system-based assessments. Candidates work through case-based challenges, role simulations, and practical tasks in �鶹ԭ�� environments that mirror real-world complexity. They can also use AI tools during exams—by design, not exception.

This marks a fundamental shift. Certification is no longer a test of knowledge recall; it is a demonstration of applied capability: the ability to navigate ambiguity, make decisions, and use AI as a tool without relying on it. More than 100,000 exams have already been completed under this model, establishing a new benchmark for certification at scale and reinforcing the relevance of certification in an AI-driven workplace.

Learning is evolving in parallel

In , AI is transforming how professionals engage with content. These capabilities are enabled by the integration of selected functionalities from Google NotebookLM into the customer and partner editions of the platform.

This shifts learning from passive consumption to active interaction. Learners can engage with �鶹ԭ�� content in more than 80 languages, ask questions, and receive precise, source-based answers with direct references to official materials. AI also generates complementary formats. Podcasts are available for moments when a screen is neither available nor practical, whether commuting, traveling, or simply stepping away from the desk, available both for passive listening and as interactive conversations with AI hosts. FAQs, study guides, mind maps, timelines, briefing documents, and video overviews allow learning to adapt to individual needs and time constraints.

Early adoption underscores the impact. More than 7,500 users are already leveraging these capabilities. reports onboarding that is 50 percent faster, while has made �鶹ԭ�� Learning Hub its primary environment for developing talent prepared for the agentic AI era. The shift is clear: Learning is becoming embedded in daily work, not separated from it.

Building the data foundation

At the same time, �鶹ԭ�� is addressing a prerequisite for effective AI: data. Many organizations continue to operate with fragmented and inconsistent data landscapes, limiting the impact of AI initiatives. The learning journey focuses on building the capability to connect, govern, and structure enterprise data, ensuring that AI systems operate on a reliable and consistent foundation.

This capability is increasingly strategic. Organizations that establish a strong data foundation can move faster from insight to action, scale AI more effectively, and create more consistent business outcomes. In this sense, data architecture is no longer a back-end concern; it is a core enabler of enterprise transformation.

Skills at scale

�鶹ԭ�� has committed to equipping 12 million people with AI-ready skills by 2030. Delivering on this ambition requires expanding access while maintaining depth and relevance. Select AI such as , are now available without login or cost, giving professionals at all career stages direct access to �鶹ԭ��’s business AI strategy.

Role-based learning journeys provide targeted development for key profiles such as enterprise architects, while a dedicated “Clean Core” course supports organizations in maintaining �鶹ԭ�� S/4HANA landscapes in ways that enable faster innovation cycles and more efficient adoption of new capabilities.

Scaling skills also requires ecosystem reach. �鶹ԭ��’s partnership with Accenture LearnVantage expands , combining �鶹ԭ��-authored content and training systems with Accenture LearnVantage’s proven experience in technology skills development for enterprise clients. This creates a continuous path from foundational knowledge to hands-on experience to certification, reflecting how professionals actually develop skills: progressively, in context, and in alignment with real-world application.

A broader shift

These developments point to a broader shift. Learning is no longer episodic; it is continuous, adaptive, and embedded in how work gets done. Participation in �鶹ԭ�� learning has increased by 33% year over year, reinforcing that organizations increasingly view skills as strategic assets in an AI-powered economy. The Autonomous Enterprise takes shape differently across industries, and so does the capability required to make it work.

At �鶹ԭ�� Sapphire, �鶹ԭ�� marks 30 years of certification not by looking back, but by redefining its role. Certification is becoming a measure of capability in action. Learning is becoming an ongoing process that evolves alongside technology and business needs.

In an AI-driven world, advantage will not come from access to technology alone, but from the ability to apply it with purpose. Across industries, the pattern is consistent: how quickly organizations capture value from AI depends on the people deploying it.

To explore these innovations in more detail and understand how �鶹ԭ�� is enabling organizations to build AI-ready skills at scale, read the .

Andre Bechtold is president of �鶹ԭ�� Industries and Experiences.