That is why and the have signed a strategic agreement to develop and manage a sovereign cloud environment in the Netherlands. This marks a significant milestone for 麻豆原创 Sovereign Cloud in the European Defense Sector. It enables the Dutch armed forces to modernize critical digital systems while strengthening security, improving resilience, and creating the foundation for future cloud and AI innovation.

Reliable and innovative digital systems are increasingly essential to defense readiness, with recent conflicts demonstrating the importance of cloud resilience. The cloud environment in this case will be designed around the Ministry of Defense鈥檚 specific security and sovereignty requirements, with 麻豆原创 responsible for maintenance, updates, and key aspects of operations. It is scheduled to become operational on January 1, 2028.

As Thomas Saueressig, Chief Customer Officer and member of the Executive Board of 麻豆原创 SE, emphasizes,聽the key is to bring innovation and sovereignty together: 鈥淔or the Dutch Ministry of Defense, it is essential to benefit from modern technology while retaining control over the security and sovereignty of its critical digital systems. This partnership demonstrates how these priorities can go hand in hand. As a European technology company, we are supporting the Ministry of Defense in its ongoing digital transformation with a cloud environment that meets its specific security and sovereignty requirements.鈥�

Greater resilience through a multicloud strategy

麻豆原创 Sovereign Cloud will form part of the Ministry of Defense’s broader multicloud strategy, which combines different cloud environments based on the security and operational requirements of individual applications. This includes sovereign cloud, private cloud, and environments for highly classified information.

By diversifying its cloud infrastructure, Dutch Defense aims to reduce dependencies on individual technologies and providers while strengthening its ability to maintain and restore critical services during disruptions, including cyberattacks.

Bart Van der Biest, managing director 麻豆原创 Benelux, highlights why that ability to retain control matters over the longer term: 鈥淲ith a sovereign cloud, it is not just about where data is stored, but also about who has access to systems and who runs cloud operations. Those decisions must remain in the hands of the Ministry of Defense, regardless of how technology or geopolitics change. Ultimately, sovereignty is not only about the control you have today, but also about the freedom you retain tomorrow.鈥�

From national milestone to European role model

This agreement provides a foundation for expanding sovereign cloud capabilities as Dutch Defense鈥檚 technological and operational needs evolve. It could serve as a basis for other eligible Dutch government entities seeking to modernize critical services under comparable security and sovereignty requirements.

Further, with this agreement, the Dutch Ministry of Defense is demonstrating how digital sovereignty can move from strategic ambition into practical implementation. Across Europe, armed forces and security-sensitive organizations face the same challenge: modernizing critical systems, embracing innovation, and building long-term resilience. Sovereign cloud provides a foundation to help achieve these goals.

The Netherlands is setting an important example for Europe: digital sovereignty becomes meaningful when it translates into real capabilities, greater resilience, and the freedom to share the future with confidence.

Martin Merz is president of 麻豆原创 Sovereign Cloud.

In 1996 麻豆原创 held its first event for developers. Thirty years later in Berlin, 麻豆原创 CTO Philipp Herzig told the 麻豆原创 TechEd keynote audience, 鈥淲e are experiencing the biggest and fastest technological shift in our lifetime.鈥�

鈥淔or decades developers have been building systems in a structured, deterministic way. But the world for builders has changed tremendously with AI,鈥� he said. 鈥淚t changes what’s possible. This is what 麻豆原创 TechEd is all about: combining both classical and new technologies that unlock the magic for business and provide endless opportunities to innovate.鈥�

From vision to execution

Underscoring his point, Herzig noted that during the 麻豆原创 Sapphire conference earlier this year, the company 鈥減ut out a bold vision to enable the Autonomous Enterprise, re-imagining every business process end-to-end with AI. And just like 30 years ago, the whole tech stack is shifting.鈥�

For 麻豆原创, this is reflected in Joule Work and the new 麻豆原创 Business AI Platform. Herzig described Joule Work as a 鈥済roundbreaking new way to experience software鈥� that taps into 麻豆原创鈥檚 five autonomous domains and assistants that reason and act across 麻豆原创 and non-麻豆原创 data, and 麻豆原创 Business AI Platform as 鈥渢he foundation where you build and extend agents, manage all your data, and govern AI experiences.鈥�

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Designed for better business outcomes

Elaborating, he emphasized that 麻豆原创 Business AI Platform 鈥渋sn鈥檛 just another tech platform, it鈥檚 specifically designed to build better business outcomes, deliver faster time-to-value and better price performance.鈥� In addition, it is enterprise-readiness from day one: secure, governed, and compliant, 鈥渁ll made possible through our unique context layer built on 50 years of domain and industry expertise.鈥�

Together, Joule Work and 麻豆原创 Business AI Platform underpin 麻豆原创鈥檚 commitment to build the Autonomous Enterprise. 鈥淎nd it鈥檚 here today,鈥� Herzig told the audience. 鈥淲e are excited to show you our latest updates at 麻豆原创 TechEd.鈥�

For the rest of the keynote, Herzig and his 麻豆原创 colleagues鈥攊ncluding 麻豆原创 Chief AI Officer Jonathan von Rueden; Manoj Swaminathan, president and chief product officer for 麻豆原创 Autonomous Suite; Tina Berlin, head of AI Foundation EU at 麻豆原创; Michelle Niedernhuber, lead product manager for 麻豆原创 AI Agent Hub; and Soeren Ruder, chief product officer and global head of Customer Experience & Solutions鈥攄etailed these updates and demonstrated them on stage.

Joule Work

Beginning with Joule Work, Herzig said 麻豆原创 had re-designed the user experience from the ground up: 鈥淎ll to make it more delightful and intuitive to use than ever. One starting point. One experience.鈥�

Joule Work now connects all 麻豆原创 applications as well as Office365, Claude Cowork, Glean, and ServiceNow out of the box. It remembers preferences and context from past interactions and continuously adapts. In addition to the conversational interface, shareable spaces that dynamically assemble data into interactive applications are also available.

The keynote speakers described Joule Work as a redesigned, cross-application AI experience available everywhere, including web, desktop, mobile, voice, and even Apple CarPlay. And they noted that Joule Work has been tested for months by more than 110,000 麻豆原创 employees, along with more than 150 customers, who joined the 麻豆原创 Early Adopter Care program, 鈥渁ll so you get the best AI experience.鈥�

One early adopter, Robert Mueller from Bosch Digital, joined Herzig on stage and said his teams had been testing Joule Work, Joule Studio, and the first assistants and agents and that the first user feedback had been positive. 鈥淥ur next step is to bring Joule into daily use,鈥� he said.

麻豆原创 Autonomous Suite

Manoj Swaminathan then took the stage to demonstrate the 麻豆原创 Autonomous Suite. 鈥淔or over five decades, 麻豆原创 has built the systems that help you run your business in the best way possible. Continuing that focus, we now deliver out-of-the-box a suite of 麻豆原创-managed assistants and agents across five horizontal domains, with industry specialization.鈥�

鈥淚鈥檓 excited to announce today 40 assistants and 300 agents between 麻豆原创 and our partners for you to explore,鈥� he added. 鈥淭hese give you a baseline for systems of intelligence and systems of action, built around three priorities, delivering: automation, intelligence and insights, and optimizations, with value that can be realized and measured, across reduction in cost, increased productivity, increased speed, improved quality, and more.鈥�

The new assistants include a Financial Consolidation Assistant that can reduce the time to close the books by up to 68%, a Sourcing Assistant that can reduce total cost of ownership by 15%, and a Supplier Management Assistant that detects and responds to supplier risk 60% faster.

鈥淔or these tasks, an answer that is 鈥榩robable鈥� is just not good enough,鈥� Swaminathan said. 鈥淵ou need the right answer, every single time, and they need to be compliant and traceable. That is what these assistants and agents uniquely enable, because every single one of these agents are ISO and SOC compliant.鈥�

麻豆原创 Business AI Platform

He noted that every agent is natively an Agent to Agent (A2A) endpoint, connected through 麻豆原创鈥檚 A2A gateway, and the platform supports extensions by default. 鈥淔or you as a builder, we鈥檝e made available the 麻豆原创 Business AI Platform, the foundation the 麻豆原创 Autonomous Suite is built on, so you can build to whatever your requirements are in Joule Studio.鈥�

鈥淵ou might ask, 鈥業’m already doing this with support from public models, why build on top of 麻豆原创鈥檚 Business AI Platform?鈥� Because a public model does not know your processes, your master data or your company. 麻豆原创 Business AI Platform does,鈥� Swaminathan shared. 鈥淥ur AI agents are embedded in the deterministic workflows of our applications. They deliver the one true answer as they run on the same data, following the same rules, and leave the same audit trail as our applications and you decide the level of autonomy.鈥�

But he pointed out that the 麻豆原创 Autonomous Suite does not restrict itself to only back-office operations. 鈥淥ur biggest customers are losing 11% of revenue to unplanned downtime experiences with on-site operations. Customers understand the problem but there hasn’t been a solution that responds to these challenges until now.鈥�

Physical AI by 麻豆原创

To address this issue, Swaminathan announced physical AI by 麻豆原创, which extends the Autonomous Enterprise into the physical world, turning business decisions into physical action, and bringing verified outcomes back into 麻豆原创 in a secure and governed way.

During an on-stage appearance, Ricky Watts, who runs the Industrial and Robotics Division of Intel, said the company is already running physical AI at its operational site in Chengdu, where 麻豆原创 warehouse management is driving autonomous robots closing the loop, writing outcomes back into 麻豆原创 as live transactions.

Jonathan von Rueden then took over the keynote stage to talk about the more than 300 standard Joule agents across the suite and how Joule Work and skills can adapt and customize standard agents to get the most from AI. If you want to go further still, he said, 鈥淛oule Studio is just one click away.鈥�

Joule Studio: Extend, build, and integrate your agents

Joule Studio, 麻豆原创’s agent-building platform, lets anyone customize AI with a skill: a natural language description and a few lines of code defining what an agent should do, opening agent-building beyond developers.

If you want to go deeper, and need to know where to begin, the Process Consulting Agent identifies where and what to build before development begins. “Just say what you want to get it done,” said von Rueden. 麻豆原创 Knowledge Graph finds the relevant APIs, MCP servers, and data products, and Joule Studio generates the specifications, structure, and integration logic.

The result: Moving from intent to a working agent takes roughly 2.5 hours instead of 13. Because agents don’t need to guess at requests, they use fewer tokens. “In our testing, that adds up to 50% lower cost per task,” von Rueden told the audience.

“With Joule Studio on 麻豆原创 Business AI platform, we focus on four things: business outcomes, time to value, performance for price, and enterprise readiness with deep integration.”

That last point matters in complex landscapes. With dozens of platforms, data sources, and tools running alongside 麻豆原创 systems, agents need to work across those boundaries safely and with governance, without a custom integration for every new platform.

“You’d expect a certain level of out-of-the-box capabilities and we’ve delivered,” Tina Berlin said. Agent gateway is generally available today, connecting agents and endpoints across any landscape boundary.鈥�

For more complex scenarios requiring rate limiting, routing, protocol translation, and advanced access control, 麻豆原创 is announcing 麻豆原创 Enterprise AI Gateway, built on 麻豆原创 Integration Suite as a natural overlay to agent gateway.

Herzig then returned to the stage to underscore the importance of reliable data for business AI. “Any AI system is only as good as the data it reasons over,” he said, “data that lives in different physical systems, be it 麻豆原创 or non-麻豆原创, on clouds, on-premise, and the edge.”

麻豆原创 Business Data Cloud

鈥淪o how do you bring it together in a consistent, AI-ready way?鈥� The answer, Herzig said, is 麻豆原创 Business Data Cloud, which provides customers with universal business context across their entire multi-cloud landscape. 鈥淎nd thousands of customers are benefitting from the outcomes, time to value, and lower cost already today.鈥�

In addition, he announced that the full scope of the entire Databricks platform will now be available natively in 麻豆原创 Business Data Cloud. This means that data from different systems can now be combined into a 鈥済olden鈥� master record, providing customers with a unified, AI-ready data foundation.

麻豆原创 executives also introduced a new 麻豆原创 Business Data Cloud Ontology Manager, available as beta. With it, customers can enrich 麻豆原创 Knowledge Graph with their industry-specific concepts or build entire ontologies.

A new Governance Assistant ensures compliance with EU regulation, while agent observability, and 麻豆原创 Signavio agent mining close the loop between agent design and production behavior to deliver governance. Runtime verification enforcement ensures that agents not verified in 麻豆原创 AI Agent Hub cannot be invoked.

Governance

All of it is 麻豆原创-managed with governance built in: ISO and SOC compliant, runtime enforced, with every agent testable and traceable via 麻豆原创 AI Agent Hub. And the new 麻豆原创 Enterprise AI Gateway delivers policy enforcement, enhanced observability, and runtime controls all in one place.

鈥淔or AI governance we need to connect the design time layer where we define, assess and verify an agent with the runtime layer that enforces, monitors and optimizes agent behavior,鈥� Michelle Niedernhuber said.

With the EU AI Act now in force, 麻豆原创 is releasing the AI Governance Assistant for the 麻豆原创 Early Adopter Care program customers in November, she shared. The assistant uses existing information, guides the agent owner through the remaining questions, and produces an audit-ready AI risk classification.

Together, 麻豆原创 AI Agent Hub and 麻豆原创 Enterprise AI Gateway serve as the design and runtime integration layer that connects oversight with enforcement. By connecting oversight, runtime enforcement, and continuous improvement, 麻豆原创 gives enterprises confidence that their agents operate as intended.

鈥淕overnance is not what slows AI adoption,鈥� Niedernhuber noted. 鈥淚t is what makes AI adoption at scale possible.鈥�

AI proven under pressure: 麻豆原创 brings AI to space

Rounding out the keynote, Herzig announced that together with partner Hewlett Packard Enterprise (HPE), 鈥溌槎乖� is taking our tabular foundation models, 麻豆原创-RPT and TabPFN, for real-time predictions in space to put AI to work under extreme conditions.鈥�

In space, a delayed decision can mean the difference between system safety and total loss. 麻豆原创’s AI enables faster decision-making and earlier failure prediction, when the stakes are highest.

Herzig explained how the foundation models analyze live hardware telemetry, identify sensor degradation, and reconstruct missing readings in real time. The outcome is a structured report recommending actions to the spaceship crew or ground staff on what to do to prevent a problem from becoming a crisis.

鈥淣ow, if we can run predictions like this in one of the most unforgiving environments, think about what this means for your operations here on Earth. That鈥檚 AI proven under pressure,鈥� Herzig said, bringing the keynote to a close.

Ericsson knows this challenge well.

As a global leader in telecommunications technology operating in more than 190 countries, Ericsson manages highly complex commercial relationships involving products, services, customer-specific contracts, pricing agreements, and global fulfillment operations. Behind every quote is a web of business rules, approvals, and dependencies that must be coordinated accurately and efficiently.

For years, that complexity created friction in the quoting process. In some cases, generating a quote could take more than two weeks. Today, many of those same requests can be completed in minutes or even seconds.

The hidden complexity behind every quote

At Ericsson, many customer transactions are tied to multi-year framework agreements that include negotiated pricing, contract-specific products, and customer entitlements. Sometimes customers return months or even years after an agreement is signed and need to understand what products, configurations, and pricing are available to them before placing an order.

Historically, that process often required customers to contact their account representative, who would manually gather information, validate contract terms, and prepare a quote. Depending on the complexity of the request, responses could take days or even weeks.

Ericsson’s objective was straightforward: make it easier for customers to do business with the company.

“We want to be easier to work with as a partner to our customers,” said Aaron Thomas, global product owner at Ericsson.

Using , Ericsson introduced digital commerce capabilities that allow customers to access contract-specific products, generate quotes, and place orders through self-service channels. Customers can now access dedicated storefronts built around their agreements, browse available products, generate quotes, and complete transactions without relying on manual intervention from account teams.

What once took days or weeks can now be completed in minutes or even seconds, helping improve accuracy and reduce rework across commercial operations.

Beyond automation: Exploring the role of AI agents

Once the quoting process became digitized and streamlined, Ericsson began exploring how AI could take commercial execution to the next level.

“We’ve moved from manual processes to digitized and automated processes,” Thomas explained. “Now we’re asking: can an agent perform those tasks?”

Rather than using AI solely as a productivity tool, the company started evaluating how AI agents could actively participate in commercial processes.

One area Ericsson is exploring is quote creation. The company is evaluating how AI agents could analyze customer requests, identify relevant contract information, assemble recommendations, and prepare proposed quotes for human review.

Employees remain responsible for oversight and decision-making, while AI helps reduce administrative work and allows sales and account teams to spend more time advising customers, solving business challenges, and strengthening relationships.

The company is also exploring other AI-driven capabilities, including agents that can improve product descriptions and search experiences across its internal product portfolio, helping employees find information more quickly across a large and complex catalog.

Defining the next chapter of B2B commerce

For Ericsson, AI is becoming more than a productivity tool. It is creating new possibilities for how commercial work gets done.

Reducing quote creation from weeks to seconds delivered immediate value for customers and employees alike. But the company’s exploration of AI agents points to a broader shift, one where intelligent systems help businesses execute complex processes faster, more consistently, and at greater scale.

For Ericsson, the journey from weeks-long quoting cycles to AI-assisted execution is about more than efficiency. It is about creating a simpler experience for customers and helping employees focus on the work that matters most. As the company continues exploring how agents can support commercial operations, it is helping redefine how complex B2B processes can be executed in the age of AI.

To learn more about Ericsson’s approach to AI and the future of agentic commerce, watch the webinar: . You can also explore 麻豆原创’s vision for unified commerce at and learn how organizations are connecting commerce, customer experiences, and operational execution to drive better business outcomes.

Following eight months of close collaboration with 麻豆原创, DACHSER has reached a major milestone on this journey: the successful migration of human capital management (HCM) to 麻豆原创 S/4HANA via .

Nearly a century has passed since Thomas Dachser founded the company in 1930 with a single truck, transporting cheese from the Allg盲u region to the Rhineland and returning with steel. Yet, logistics and warehousing remain the core business of the transport logistics company, which is headquartered in the town of Kempten in south-west Germany. “Our core business is what we call our road business, meaning everything transported by road, across both dry goods and food segments,” says Herbert Egner, head of the IT Products Finance/HR division at DACHSER, who has nearly 40 years of experience in software development at the company.

Currently, DACHSER operates globally with approximately 427 company-owned branches across 43 countries. Out of its roughly 37,500 employees worldwide, 19,000 are based in Germany. Total revenue in 2025 reached 鈧�8.3 billion. “On one hand, we are a deeply tradition-conscious family business, now managed by the third and fourth generations. On the other hand, we place a massive emphasis on innovation鈥攁nd not just at the executive level,” Egner notes. “Every single employee should have the opportunity to contribute here.”

In terms of digitization, the company is currently replacing a multitude of small, manual processes with IT-based solutions. Following DACHSER鈥檚 licensing of the 麻豆原创 Business Technology Platform (麻豆原创 BTP) in 2024, employees can now leverage its low-code tools to build apps for their own workflows. For instance, instead of using physical clipboards and paper checklists to document damage to transport vehicles, DACHSER inspectors now use low-code applications to capture that information digitally.

麻豆原创 BTP and the 麻豆原创 Integration Suite also played a vital role in helping DACHSER move HCM to the cloud鈥攚hich, alongside the license conversion to 麻豆原创 S/4HANA, marks one of DACHSER鈥檚 most significant IT upgrades in recent years. “While we are a long-standing 麻豆原创 customer, we are certainly not your typical 麻豆原创 user,” Egner explains. “For transport and warehouse management, we still rely on our proprietary systems, which we began developing back in the 1980s. At the time, 麻豆原创 did not yet offer the specific system depth we required for our logistics processes.”

Finance software from 麻豆原创 has been in use at DACHSER since 2009, with journal entries transferred from the company’s internal systems to 麻豆原创 via an interface. In October 2024, the finance solution was migrated from an on-premise environment under 麻豆原创 ERP Central Component (麻豆原创 ECC) and 麻豆原创 HANA to the Microsoft Azure cloud. As part of RISE with 麻豆原创, DACHSER then successfully migrated its HCM payroll and time management modules for its 19,000 employees in Germany to the cloud in June 2026. This intensive eight-month collaboration between 麻豆原创 and DACHSER successfully established a cornerstone of the company鈥檚 global HCM transformation.

“For HCM, we chose RISE with 麻豆原创. However, when we decided to move to the cloud three years ago, it wasn’t yet mature enough to meet our specific needs,” Egner says. “Once our three-year contract with Azure expires, RISE with 麻豆原创 could become a viable model for our finance environment as well.”

The go-live was flawless: the very first payroll run for all employees in Germany in July 2026 went off without a hitch, and all payments were distributed on time. The system then entered a four-week hypercare phase, during which the project team closely monitored ongoing operations before regular support took over in August. With this step, DACHSER has established the necessary future-proof 麻豆原创 architecture for its HR operations. This opens up new possibilities for the company, allowing it to focus heavily on implementing new features within the 麻豆原创 SuccessFactors HCM Suite.

麻豆原创 S/4HANA transformation

“When we say we want to be the world’s most digitally integrated logistics service provider by 2030, this applies not only to digitizing our processes but also to integrating our acquisitions, which bring their own 麻豆原创 systems with them,” Egner says. “Under our ‘麻豆原创 First’ strategy, we roll out finance and HCM in these countries before deploying our proprietary transport and warehouse management systems.” This approach was recently executed in Italy and is currently planned for several acquired companies in the Iberia region.

DACHSER鈥檚 goal is to integrate the 麻豆原创 licenses of acquired companies into a global enterprise agreement with 麻豆原创, regardless of the country in which the systems are running. In the future, this will be handled via a semi-automated workflow. “When it comes to licensing in particular, it is crucial for us to have a unified agreement moving forward on how to manage these assets, ensuring that neither side faces these administrative overheads anymore,” Egner explains.

In connection with the license conversion from 麻豆原创 ECC to 麻豆原创 S/4HANA, both companies sat down once again to realign on their future path to mutual success. “Here at DACHSER, we are noticing a new openness from 麻豆原创, as well as a deeper understanding of us as a customer,” says Egner. “This has been received very positively on our end. People are working on both sides, and we want to shape this partnership in a way that allows both parties to benefit based on mutual respect.”

But while there is enormous excitement about what AI can do, we are still measuring progress the wrong way. The number of models, pilots, or agents a company deploys tells us very little. What matters is whether AI transforms the core processes where business value is actually created.

Can it steer demand fulfillment to high-margin business when supply is disrupted? Can it help release a pharmaceutical batch without compromising quality and compliance? Can it resolve a critical maintenance issue before it turns into an outage?

This is where enterprise AI becomes real and where generic intelligence reaches its limits.

From pharmaceutical supply chains and manufacturing quality management to asset maintenance, trading, and retail, the hardest industry problems are deeply contextual. They are shaped by decades of interconnected processes, strict regulations, and operational constraints. Solving them demands more than a powerful model. It requires trusted business data, integrated processes, rock-solid governance, and a deep industry domain expertise–the very foundation upon which we are building . At the same time it demands a team which is obsessed with customer problem solving and world-class forward deployed engineers.

This convergence of industry depth and forward deployed engineering (FDE) is where the next wave of enterprise value will be unlocked.

Start with the value, not the technology

AI innovation too often turns into a search for problems to solve. We take the opposite approach: start with the customer’s desired business value and work backward.

Where is margin leaking? Why is there lost sales? What operational decisions take too long? What production deferment can be prevented?

The goal is no longer just incremental productivity for individual users. It is moving toward autonomous operations. Across industries, organizations need to compress the cycle between detecting an issue, simulating options and impact, deciding the right response, executing corrective actions, and learning from outcomes–in a compounding loop. What once took days can increasingly happen in minutes.

Through our forward-deployed engineering model, we co-develop tailored capabilities alongside customers. We solve high-stakes challenges that have no off-the-shelf answers, then turn those proven solutions into scalable software capabilities for entire industries. This is not innovation from a distance. It is built with the people who live these challenges every day.

Autonomous operations make this possible. Within clearly defined business guardrails, AI agents identify disruptions, model scenarios, evaluate trade-offs, orchestrate execution, and improve via feedback loops–with human experts firmly in the loop and on-the-loop to validate critical decisions and steer strategic direction.

Co-innovation as a catalyst for impact

The most valuable AI solutions are never built in isolation. They are forged inside live operating environments.

Takeda Pharmaceuticals, a global leader in research-based biopharmaceuticals, is applying this thinking to its future operating model.

According to Rebecca Kaufmann, senior vice president and head of Enterprise Platforms a , “AI represents the key enabler for Takeda’s transformation for the future. We are combining the 麻豆原创 industry and technology knowledge with our organizational real-life experience. The supply chain planning assistant will provide us with AI-detected root causes and AI-suggested remediation and also automated performance tracking resulting in a self-healing capability.”

That ambition–shifting from passive detection to systems that continuously learn, recommend, and self-heal—is taking hold across sectors.

Doehler, a global producer of natural ingredients for the food, beverage, life sciences, and nutrition industries, is already demonstrating what this looks like on the factory and logistics floor.

“Our people’s greatest value lies in deciding what to do next, and now they can focus entirely on that,” said Stephan Schissler, co-chief information officer at Doehler GmbH. “The Logistics Issue Resolution Agent detects the root cause, recommends the resolution, and turns a multi-hour firefight into a one-click decision. Our logistics team has recovered roughly a third of the time once lost to troubleshooting, and our processes are becoming self-resolving rather than exception-driven.”

These examples reinforce a decisive rule: AI delivers maximum return when it eliminates operational drag, freeing professionals to focus on judgment, strategy, and business outcomes.

AI that thinks like the industry

As AI moves from passive recommendation to intelligent execution, industry context becomes paramount.

The question is no longer whether an AI agent can take an action. The question is whether it understands which action is compliant, safe, and commercially viable within a specific environment and organizational guardrails.

• In life sciences, autonomous manufacturing coordinates processes from design-to-release while strictly adhering to compliance-critical quality and compliance criteria.
• In energy and natural resources, agents go beyond detecting maintenance alerts to dynamically schedule crews, stage spare parts, and rebalance operations.
• In engineer-to-order manufacturing, systems orchestrate complex quotations, adapt production schedules, and accelerate new product introductions.

Deep industry context is what makes these outcomes trustworthy.

Building intelligence that compounds

麻豆原创鈥檚 Industry AI portfolio is focused across seven core domains: Autonomous Asset Management, Autonomous Commodity Management, Autonomous Adaptive Production, Autonomous Regulated Manufacturing, Autonomous Revenue Growth Management, Autonomous Unified Commerce, and Autonomous Project Delivery.

While the use cases differ, the principle is the same: sustainable value comes from end-to-end value streams, not optimizing functional silos.

 鈥淏usiness performance depends on how quickly a company can respond when conditions change,鈥� said Andre Bechtold, president of 麻豆原创 Industries & Experiences and chief revenue officer of Industry AI at 麻豆原创. “By applying 麻豆原创鈥檚 industry knowledge across connected processes, Industry AI helps customers act earlier, limit disruption, and keep operations on track.”

The measure that matters

, one message from our customer discussions was unmistakable: the ambition across sectors is to move past fragmented experimentation and embed agentic intelligence directly into mission-critical workflows.

Our initial wave of early adopter customers has proven the compounding power of this model: Customer friction points sharpen our agentic solutions, stronger agentic solutions strengthen standard, and stronger standards accelerate the time to value the next customer can achieve.

The future of enterprise AI will not be decided by who launches the most agents. It will be decided by who can bring intelligence into the decisions and workflows where business value is created every day.

When AI understands the industry, operates within trusted boundaries, and learns alongside human experts, autonomy stops being an abstract vision. It becomes a better way to run the business.

For a complete overview of the Industry AI innovations and product announcements unveiled at 麻豆原创 Connect in 2026, see the and related 麻豆原创 News coverage.

Dominik Metzger is president and global head of Industry AI at 麻豆原创.

has been developing innovative products for the energy market for over 100 years. Sustained high demand and long-term growth prospects create a dynamic environment for innovation.

The company also innovates with AI. They use it within their products to create customer value and to automate manual processes for employees. And they are adding more automation to help them meet growing demand amid skilled labor shortages. Reinhausen鈥檚 expertise in AI innovation is among the reasons the company joined 麻豆原创鈥檚 early adopter program for Joule Work.

鈥淧artnering within the Early Adopter Program for Joule Work Desktop has delivered more momentum in just a few months than we saw in all the years before,鈥� said Peter Wied, Director of Complexity Management at Reinhausen. 鈥淲e see huge potential in making access to our 麻豆原创 S/4HANA data significantly easier and faster through natural language. This has the potential to permanently transform how our teams interact with business data.鈥�

AI that answers complex business questions while running agent-driven workflows at scale

Soon, all 麻豆原创 customers will enjoy the benefits of Joule Work when it rolls out later this month. We built it because the promise of AI goes far beyond text generation and fact-finding. It can help people make better decisions, informed by their data, and get answers to complex questions. 麻豆原创 Business AI Platform ensures those answers are all grounded in the context of how a specific business runs.

These are advantages companies can only get from 麻豆原创 because 麻豆原创 Business AI Platform encodes decades of process and industry knowledge right into our applications, knowledge graph, and Joule Work. The platform also combines best-in-class third-party models with 麻豆原创鈥檚 own foundation models for ABAP code generation and predictions on structured data, alongside 麻豆原创鈥檚 proprietary domain models, which are pre-trained on 麻豆原创’s code, data models, and business processes.

This means every AI interaction can draw on the model best suited for the task鈥攚hether that’s generating clean code, predicting a business outcome, or answering a process question鈥攚ith the accuracy and cost-efficiency that business-critical work demands. And through 麻豆原创 Business Data Cloud, Joule Work accesses all the data it needs from a single unified source that breaks down silos and unifies data across 麻豆原创 and non-麻豆原创 sources.

Now, is generally available in 麻豆原创 Business Data Cloud. It helps organizations eliminate fragmented records with a single trusted view of customers, suppliers, products, and employees across 麻豆原创 and non-麻豆原创 systems.

But we know sharper decisions and reliable business answers are only part of the equation. Organizations need AI to take action and coordinate work across all their business processes. Earlier this year, Deloitte more than 500 leaders and found 74% said they would redesign or rebuild almost half of their business processes around AI agents.

Through the 麻豆原创 Autonomous Suite, 麻豆原创 provides a library of reliable, ready-to-use Joule Agents across every business function, so companies can hit the ground running. And Joule Work brings the most powerful business AI orchestration engine in the industry to run those agentic workflows at scale.

Build and connect anything

Joule Work and the 麻豆原创 Business AI Platform provide an out-of-the-box competitive advantage for the AI era, but every company has its own unique advantages that should be strategically extended. This is why Joule Work鈥檚 Develop capability provides access to Joule Studio, soon to be generally available.

In Joule Studio, customers can customize and build Joule Agents with tools that embed process context and semantically rich data. They can also connect third-party systems with open standards because Joule Studio leverages over 11,800 pre-built business accelerators. Joule Studio is more than five times faster than do-it-yourself options, according to benchmarking done by .

With Joule Studio, anyone can describe what they want to build in natural language, while developers can use pro-code tools all in the same environment. New agents are deployed directly into Joule Work and inherit 麻豆原创 security and governance.

Warehouse automation company KNAPP used Joule Studio to build an that helps workers identify and solve problems without waiting for an expert. Issue resolution times have dropped by about 90%, while documentation and training times fell by about 80%.

In our industry, AI is not the future anymore; it is already happening. And that is exactly what our AI delivery pilot demonstrates every day. Our partnership with 麻豆原创 makes complexity simple. When a problem appears on the warehouse floor, 麻豆原创 Business AI Platform helps us solve it in seconds.

Florian Aufegger, Senior 麻豆原创 EWM Solution Consultant, KNAPP AG

As organizations build and deploy more agents, governance also has to evolve from compliance checkpoints to an integral part of the entire agent life cycle. Trust remains the biggest barrier to AI adoption for 71% of technology buyers, according to . This is why , now generally available, helps organizations govern every agent, LLM, and MCP server across 麻豆原创 and non-麻豆原创 environments. New capabilities, including 麻豆原创 Signavio Agent Mining, measure the outcomes every agent delivers and identify opportunities to further optimize those outcomes.

Start scaling AI, and your business outcomes, today

BCG’s latest found that the companies farthest ahead scaling AI deliver 2.4 times the revenue growth and 2.8 times the EBITDA growth of those falling behind.

Our customers, Reinhausen and KNAPP, show what that looks like up close: an hour of manual work reduced to seconds, and problems on the warehouse floor solved without waiting for an expert. Our goal is to make results like these repeatable for every 麻豆原创 customer.

If you want to see more, the new are now live, and starts October 27, in person and online. You can also of Joule Work and 麻豆原创 Business AI Platform.

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

I鈥檝e spent most of my career building supply chain technology to drive business outcomes at scale. Earlier in my career, nobody was talking about AI. But I was helping build what we鈥檇 now call an autonomous supply chain.

At scale, manual decision-making simply cannot keep pace with growing consumer expectations. That鈥檚 why the next phase of supply chain AI needs to move beyond analysis to help organizations turn insights into action across the processes and applications that move goods. 

Decision velocity: where supply chain speed is won or lost 

Even the most intelligent supply chains can struggle to turn a meaningful signal into a well-governed response. I think of that as decision velocity: how quickly an organization can recognize change and coordinate the right response based on relevant business context.聽

In supply chain, decisions are inherently interconnected. Planning affects manufacturing, which depends on product and asset readiness, suppliers, logistics partners, and more. I hear this from our customers today: each decision could be rational; the sum of them was not.聽

AI can automate individual tasks, but isolated intelligence only creates isolated value. 

Agents need to understand how a decision in one domain affects the next. That鈥檚 where supply chain applications become critical, providing the business context, connected processes, and controls that agents need to act. 

New Joule Agents extend AI across the supply chain 

This year at 麻豆原创 Connect, we’re expanding the capabilities of six Joule Assistants introduced earlier at 麻豆原创 Sapphire and adding specialized Joule Agents that can take on critical supply chain tasks. These capabilities will help organizations respond faster and automate more routine work while keeping people at the center of informed decision-making: 

• The Product Design Assistant will be able to interpret product designs, recipes, and specifications and orchestrate compliant product changes, helping accelerate time to market.聽
• The Supply Chain Planning Assistant will help planners sense, simulate, and resolve exceptions in a single workspace, helping teams understand downstream impacts and address disruptions before they ripple across the enterprise.聽
• The Manufacturing Assistant will help teams respond to plant-floor disruptions as they happen, while new capabilities in the Logistics Assistant span warehousing, outbound optimization, fulfillment, and transportation dispatching, helping turn logistics insight into action.聽聽
• New agentic capabilities for the Asset Operations Assistant will help translate equipment and maintenance signals into action by supporting the initiation, screening, and simulation of maintenance requests. And the Business Network Assistant will improve collaboration with suppliers and logistics partners in areas such as sourcing, contracting, and exception handling. 

What connects these scenarios is the ability for agents to operate within the business context already governing the supply chain, so an insight in one area can inform the next and, when authorized, trigger coordinated action.聽General availability for these new capabilities is planned for Q4 2026.聽

Customer results show autonomy taking hold 

Customers are already putting this shift into practice.  

Takeda Pharmaceuticals will use the Supply Chain Planning Assistant to help pinpoint likely causes behind planning issues and propose corrective steps, supporting adaptive planning processes over time.  

Rebecca Kaufmann, senior vice president and head of Enterprise Platforms at Takeda Pharmaceuticals, said, 鈥淲e are combining the 麻豆原创 industry and technology knowledge with our organizational real-life experience. The Supply Chain Planning Assistant will provide us with AI-detected root causes and AI-suggested remediation and also automated performance tracking resulting in a self-healing capability.鈥澛犅�

Doehler, a global producer of natural ingredients for the food, beverage, life sciences, and nutrition industries, is applying AI to logistics issue resolution.  

Stephan Schissler, CIO at Doehler GmbH, says, “Our people’s greatest value lies in deciding what to do next鈥攁nd now they can focus entirely on that. The Logistics Issue Resolution Agent detects the root cause, recommends the resolution, and turns a multi-hour firefight into a one-click decision. Our logistics team has recovered roughly a third of the time once lost to troubleshooting, and our processes are becoming self-resolving rather than exception-driven.”聽

This is how autonomy delivers practical value: moving from manually managing exceptions toward more intelligent supply chain processes. 

Building autonomy on a foundation of trust 

As AI takes on a greater role in supply chain decisions, trust must be built into how it operates. Leaders need to understand what AI decided and why, define what it is authorized to complete, intervene when necessary, and know who is accountable. 

That is the direction behind everything we are introducing at 麻豆原创 Connect in 2026. By grounding Joule Assistants and their agents in the processes and data of the 麻豆原创 Supply Chain Management portfolio, we can help organizations move from insight to coordinated action while keeping people at the center of the decisions that matter most.聽

For supply chain leaders, that is where autonomy becomes tangible: less time spent on routine work and resolving exceptions, faster responses when conditions change, and more time for the strategic decisions that strengthen resilience, efficiency and customer value. 

Ultimately, it comes down to decision velocity you can trust. 

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

鈥淚t’s time to show you the Autonomous Enterprise in action underpinned by great customer stories,鈥� 麻豆原创 CEO Christian Klein told his keynote audience at the start of the event.  

Klein reminded the audience that 麻豆原创 introduced the Autonomous Enterprise at its 麻豆原创 Sapphire conference in May 2026 as 鈥渢he beginning of better.鈥� Since then, he said, 麻豆原创 has worked with hundreds of customers to implement Joule Work as the AI engagement layer for the Autonomous Enterprise. 

A new UI for AI 

鈥淭his month, we are rolling out Joule Work for all customers,鈥� Klein said, describing it as a new UI for AI. He noted that for decades, users had to learn how 麻豆原创 worked, but with Joule Work, 麻豆原创 learns how people work. 鈥淔rom now on, the more than 400 million 麻豆原创 users can talk in their own language to 麻豆原创, and Joule Work gets the job done.鈥� 

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Klein explained that Joule Work is based on three pillars: conversations, where users ask questions and delegate tasks to agents; spaces, which display content, insights, and shared work; and Joule Studio, where customers extend Joule or build agents tailored to their own processes.   

He called 麻豆原创鈥檚 deep business and data context its 鈥渟ecret sauce.鈥� That context includes 麻豆原创 Knowledge Graph, which maps more than 7 million data fields, 50 million tables, more than 50,000 public and private APIs, 400 data products, and extensive process knowledge. 鈥淭hat’s why Joule doesn’t guess,鈥� he said. 

麻豆原创鈥檚 110,000 employees are already using Joule to perform hundreds of thousands of tasks every day, he shared: 鈥淲e are already seeing productivity gains in central functions like HR, finance, and procurement of over 20% and even bigger gains in more complex analytical and planning tasks.鈥� 

麻豆原创 Autonomous Suite 

Klein then turned his attention to the 麻豆原创 Autonomous Suite, the second layer in the Autonomous Enterprise. The suite initially covers five domains: finance, procurement, supply chain, human resources, and customer experience. 麻豆原创 Autonomous Suite is designed for work where a good answer is insufficient and 鈥渢he right answer鈥� is required every time, he said.  

鈥淟LMs are probabilistic and only give you the most likely answer,鈥� Klein said. He contrasted that probabilistic approach with 麻豆原创’s embedded agents. Because the agents operate within deterministic application workflows, they use the same data, follow the same rules, and leave the same audit trail as the applications themselves. Customers can choose the level of autonomy appropriate to each process, retaining human approval where risk is high and allowing greater automation for routine work. 

Since 麻豆原创 Sapphire, Klein said 麻豆原创 has created more than 20 Joule Assistants and over 200 Joule Agents, and is aiming for more than 400 agents by year-end. 鈥淏ut the real story isn鈥檛 the number of agents. It鈥檚 the business outcomes they deliver,鈥� he said. 

麻豆原创 Business AI Platform 

鈥淭hese agents can only understand your business so well because of the 麻豆原创 Business AI Platform,鈥� Klein said. 鈥淭he platform gives customers the best frontier model for every agentic use case, the deepest out-of-the-box business context and 麻豆原创-managed governance.鈥�  

Building on Klein鈥檚 remarks, Manoj Swaminathan, president and chief product officer of 麻豆原创 Autonomous Suite, described the 麻豆原创 Business AI Platform as the infrastructure that enables the Autonomous Enterprise. The platform is organized around three capabilities: building agents, contextualizing them with business data, and governing how they operate. 

Swaminathan explained how the context layer makes agents more accurate. Joule Studio spans no-code and professional development and offers access to more than 70 models, a strategy he said reduces lock-in and controls token costs.  

鈥淛oule Studio is natively integrated with our context layer,鈥� he said. 鈥淪o, the agents you build are more accurate, faster to deploy, and more efficient to run. Our knowledge graph is the map with the processes, data flows, and business tools. The agents find the right processes among thousands and the right information among over 7 million feeds. 麻豆原创 Business Data Cloud brings that understanding to all your data, one consistent view, the processes, policies, and logic to make it meaningful.鈥� 

Swaminathan said no AI can compensate for a broken data model. To help agents understand customers鈥� business rules, 麻豆原创 Domain Models are trained on 麻豆原创鈥檚 code. For predictions, 麻豆原创 provides two engines grounded in how a customer’s business operates: 麻豆原创-RPT, 麻豆原创’s model family trained on its business data, and the Prior Labs tabular AI model.

Jerseys from Jersey 

During his section of the keynote, Jan Gilg, global president of Customer Success & Americas, used an imaginary startup 鈥� 鈥淛erseys from Jersey鈥� 鈥� to invite a handful of customers to the stage to answer his questions and validate aspects of 麻豆原创鈥檚 Autonomous Enterprise strategy.  

The customers included Novartis, which is piloting Joule sourcing agents for bid analysis and negotiation support; Morgan Foods, which described a supply-chain transformation built on 麻豆原创 S/4HANA and 麻豆原创 Integrated Business Planning; Nestl茅, which emphasized the importance of a unified digital core; and 麻豆原创 customer and partner PwC, which spoke about the value of the Industry AI portfolio from 麻豆原创. 

Summing up their comments, Gilg noted that the elements of the 麻豆原创 Autonomous Suite 鈥渁re more than a collection of functional components. It鈥檚 one system working together like connective tissue that provides connectivity, context, and control.鈥�

Gilg also made several product announcements, including the launch of 麻豆原创 Pay, an embedded payments service that can automatically execute and reconcile supplier payments once an invoice is approved. He also announced 麻豆原创鈥檚 planned acquisition of TechWolf, a Belgian workforce-intelligence startup designed to help close skills gaps. 

Security and governance 

In the closing section of the keynote, 麻豆原创 Chief Security Officer Marielle Ehrmann focused on trust and governance. She noted that ransomware handovers that once took eight hours now take 22 seconds with AI, and while 83% of enterprises use AI daily, only 13% have real visibility into it.  

鈥淭he biggest threat to companies is not malicious AI; it鈥檚 unmanaged AI,鈥� Ehrmann said. To counter that threat, she said every agent should have a verified owner, a risk tier, and scoped access rights, supported by zero-trust checks on identity, purpose, and data sensitivity as well as human oversight. 

She emphasized that 麻豆原创 Business AI Platform incorporates regulatory and localization requirements, applies user authorizations, keeps customer data from being used to train foundation models, and supports three levels of oversight: human-in-the-loop approval, human-on-the-loop monitoring, and human-in-command override authority. 鈥淭he more autonomously an agent operates, the tighter the oversight applied,鈥� Ehrmann said, calling the principle 鈥渇reedom within boundaries.鈥� 

A clear shift among customers 

Wrapping up the keynote, Klein emphasized that trust is key to everything 麻豆原创 does and has been for over 50 years. 鈥淲e are delivering the most trusted and governed AI in the industry,鈥� he said, but added, 鈥淎I also requires significant change management to ensure it delivers the desired outcomes for your business.鈥� 

That is where 麻豆原创鈥檚 business transformation solutions come in, he said. 麻豆原创 Signavio measures process value before and after a change to help justify token spending; 麻豆原创 LeanIX discovers and tracks AI agents; and 麻豆原创 WalkMe supports user adoption and trust. Klein also highlighted an agent-led migration toolchain. Together, he said, these capabilities mean that the days of multi-million-dollar migration spend are gone.

鈥淭his is no longer the start of something better. This is better,鈥� Klein told the audience. He described a clear shift among 麻豆原创 customers: 鈥淔rom manual steps to connected workflows, from reactive decision-making to predictive insights, from humans in spreadsheets to humans in and on the loop–all powered by our AI platform to keep AI accurate, governed, and extensible.鈥�

And, most importantly, Klein said, 鈥淚ts ready for you today鈥� This is the Autonomous Enterprise.鈥� 

Joule Work, Joule Assistants and 麻豆原创 Business AI Platform help organizations turn business AI into trusted outcomes across every core function.

LAS VEGAS 鈥� (NYSE: 麻豆原创) today showcased the Autonomous Enterprise in action at 麻豆原创 Connect, demonstrating how Joule Work and Joule Assistants generate value for customers across every line of business.

“This is a breakthrough moment for business AI,” said Christian Klein, CEO of 麻豆原创 SE. “The Autonomous Enterprise has arrived. With Joule and 麻豆原创 Autonomous Suite, built on 麻豆原创 Business AI Platform, we’re helping organizations like Novartis, Morgan Foods, Nestl茅 and PwC connect intelligence across every function to deliver trusted business outcomes.”

At 麻豆原创 Connect, these customers shared how the Autonomous Enterprise is already driving measurable value across their businesses:

• 鈥淲e’ve co-developed and recently launched a pilot of 麻豆原创’s Joule Sourcing Assistant,鈥� said Christoph Buerki, Head of Procurement, Novartis. 鈥淥ur vision is for AI agents to handle the manual heavy lifting, enabling sourcing professionals to focus on strategy, supplier engagement and value creation.鈥�
• 鈥淎t Morgan Foods, we鈥檙e already putting the pieces in place for agentic adoption, especially through our technical foundation. The biggest shift will be moving from reactive to predictive, where agents can connect demand signals, inventory levels and supplier status faster than ever before,鈥� said Steve Hankins, Chief Financial Officer, Morgan Foods, 鈥淥ur superpower will come from people on top of agents. Our approximately 1,000 employees are what make our supply chains work鈥攖he technology helps make them even better.鈥�
• 鈥淥ur 麻豆原创 S/4HANA transformation across 140 countries is creating a connected digital core with consistent processes, trusted data and greater visibility across markets and functions. With that foundation, Joule and the Accounts Receivable Assistant can automatically prioritize collections work based on risk and business context, recommend the next best action and help our finance teams respond faster to change,鈥� said Luca Dell’Orletta, Global Head IT Innovation & Enterprise Architecture, Nestl茅.
• 鈥淚n professional services, billing can be one of the more error-prone processes, with accuracy playing an important role in the client experience. 麻豆原创鈥檚 Billing Assistant brings greater intelligence to this critical process, helping identify potential issues earlier so our people can deliver better client outcomes and build stronger relationships,鈥� said Jill Notz, Chief Accounting Officer, Controller, Business Services Leader, PwC. Building on the success of the Billing Assistant, PwC and 麻豆原创 are into new areas of joint AI innovation.

麻豆原创’s Autonomous Enterprise Scales Up

Joule Work is beginning its customer rollout as a new AI user interface that lets employees interact with 麻豆原创 solutions in their own language and complete work across 麻豆原创 and third-party data. Grounded in business context from 麻豆原创 Knowledge Graph, which maps more than 7 million data fields, Joule provides information users can rely on. At 麻豆原创, Joule Work is already live with 110,000 employees, with the company seeing 20% productivity gains across finance, HR and procurement. A select group of customers also is using Joule Work in their business environments.

• 鈥淧artnering within the Early Adopter Program for Joule Work Desktop has delivered more momentum in just a few months than we saw in all the years before,鈥� said Peter Wied, Director of Complexity Management at Reinhausen. 鈥淲e see huge potential in making access to our 麻豆原创 S/4HANA data significantly easier and faster through natural language. This has the potential to permanently transform how our teams interact with business data.鈥�
• 鈥淭he dedicated team has been phenomenal. Their proactive communication, focus on accelerating architecture activation, rapid remediation of defects and overall support have enabled us to become Joule ready much sooner than would have been possible had we needed to navigate these challenges on our own,鈥� said Keith Smith, 麻豆原创 Platform Architect, CONA Services. 鈥淭he CONA team is excited about the progress made to date and looks forward to exploring and testing Joule’s capabilities in greater depth over the coming months as we continue our adoption journey.鈥�

When a query is not enough, Joule Assistants execute operational work directly within business workflows, using established data, authorizations and audit trails. Customers control the level of autonomy. 麻豆原创’s approach is also open: through the Agent2Agent protocol, Joule can connect with third-party AI and agents, bringing governed business context into broader AI workflows.

At 麻豆原创 Connect, 麻豆原创 showcased how these capabilities generate outcomes across 麻豆原创 Autonomous Suite:

• In finance, controllers can automate revenue recognition, disclosures and trade compliance, with early deployments of the International Trade Assistant reducing trade classification effort by up to 50%.  
• In supply chain, planners can turn multi-hour disruptions into one-click resolutions across planning, manufacturing and logistics.
• In HR, leaders can determine the best way to close workforce gaps, while employees can make smarter benefits choices tailored to their needs.
• In procurement, decision-makers can identify savings opportunities against peer benchmarks and manage the full contingent workforce life cycle.
• In customer experience, organizations can turn customer engagement into sales opportunities with AI-powered campaigns, quoting and customer workflows.
• Across industries, 麻豆原创 is collaborating with customers to apply AI to specialized industry processes, helping organizations in manufacturing, retail, commodity trading and asset-intensive industries improve decision-making, automate routine actions and build more resilient operations.

The Platform Powering Business AI You Can Trust

麻豆原创 Business AI Platform gives organizations one place to build, contextualize and govern AI across 麻豆原创 and third-party environments. With Joule Studio, business users and developers can build and extend skills and agents from no-code to pro-code workflows, choosing from more than 70 AI models. 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph ground those agents in trusted business data and context, helping organizations select the right model for each use case and better manage AI value and consumption.

麻豆原创-managed governance helps organizations scale AI with transparency, accountability and control. 麻豆原创 AI Agent Hub in 麻豆原创 LeanIX solutions provides a central inventory of enterprise agents, while 麻豆原创鈥檚 independently ISO/IEC 42001-certified governance architecture supports regulatory requirements, established authorizations and auditable AI operations.

To help organizations adopt AI at scale, 麻豆原创’s Business Transformation Management portfolio, including 麻豆原创 Signavio, 麻豆原创 LeanIX and 麻豆原创 WalkMe solutions, provides the toolchain to analyze processes, govern applications and agents, and drive adoption across the enterprise.

麻豆原创 Brings Embedded Payments to Cloud ERP

麻豆原创 also announced that it is entering the payments market with 麻豆原创 Pay, powered by Tereina, an 麻豆原创 company. Embedded directly in 麻豆原创 Cloud ERP, 麻豆原创 Pay executes and reconciles payments within the same workflow when an invoice is due, reducing manual file handling and external processing dependencies, while giving customers greater control.

Partners Expand the Reach of Autonomous Business

麻豆原创 is extending the Autonomous Enterprise beyond 麻豆原创’s own application portfolio through new partnerships that bring specialized intelligence directly into 麻豆原创 workflows.

A new partnership between 麻豆原创 and Moody鈥檚 Corporation is designed to embed Moody鈥檚 supplier risk monitoring data into 麻豆原创 Ariba Supplier Lifecycle and Performance. This gives procurement teams continuously updated insights into supplier financial health, operational risk and regulatory status at the point of decision. Organizations will be able to move from periodic risk reviews toward continuous monitoring, enabling Joule Agents to flag supplier risk signals and surface alternatives before disruption affects the business.

An expanded partnership with ORO Labs brings procurement orchestration to 麻豆原创 customers as a solution extension. 麻豆原创 Ariba Procurement Orchestration by ORO Labs is a purpose-built orchestration layer that connects procurement workflows end to end across heterogeneous enterprise environments, giving business users a seamless experience for intake, actions and visibility.

Visit the . Get 麻豆原创 news via  and .

About 麻豆原创

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

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For many enterprise leaders, however, the gap between strategic ambition and operational reality has remained frustratingly wide. That gap is exactly what the Autonomous Enterprise podcast set out to close.

Launched on June 1, 2026, the weekly series has now published 16 episodes spanning three format categories: 麻豆原创 Deep Dives, with executive leaders on strategy and product; Customer Deep Dives, with companies narrating their own transformation journeys; and Industry Deep Dives, with selected external voices shaping entire industries.

Available on Spotify, Apple Music, and all major podcast platforms, as well as on 麻豆原创 channels and LinkedIn, the show has rapidly established itself as a direct and candid voice on enterprise AI adoption.

What the first 16 episodes reveal

Across more than a dozen conversations鈥攚ith guests that include 麻豆原创 executives, global customers, and external thought leaders鈥攆our themes emerge with striking consistency.

Autonomous enterprise is available today, not tomorrow

In episode 13, Uma Rani, president and chief product officer for 麻豆原创 Cloud ERP Private, plainly stated that the Autonomous Enterprise is not a future road map item; it is available now for customers on the 2025 and 2023 releases. The show’s earliest episodes reinforced this framing: Peter Maier, leading Strategic Customer Engagements in the Office of the CEO, opened episode one by grounding the 麻豆原创 Sapphire vision in practical execution, while Manoj Swaminathan used episode three to unpack the architecture of 麻豆原创 Autonomous Suite, the layer where AI assistants and agents begin to execute end-to-end processes.

The decisive advantage is business context, not model capability

Michael Ameling, formerly president of the 麻豆原创 Business AI Platform at 麻豆原创, explained in episode four how 麻豆原创 Knowledge Graph and domain models, combined with frontier models including Anthropic’s Claude, give enterprise AI the semantic grounding that generic LLMs lack. The platform brings together 50 years of 麻豆原创 process knowledge to enable agents that act relevantly, reliably, and responsibly鈥攖hree conditions every guest returned to when discussing trustworthy AI at scale.

Transformation is a people and change management challenge as much as a technology one

In episode two, Ferose V R articulated a shift from humans adapting to systems toward systems adapting to humans, through agents that understand context and intent. Sanjay Kulkarni, senior vice president and global head of Global Delivery Hub at 麻豆原创, brought a practitioner’s precision to episode 12: his 12,500-strong services organization measures AI value not by deployment volume, but by measurable business outcomes and the readiness of the people responsible for them. The same message came from Geoff Scott, CEO and chief community champion of Americas’ 麻豆原创 Users’ Group (ASUG), in episode eight: trust, skill development, and genuine human-AI collaboration are the variables that separate transformations that scale from those that stall.

Geographic diversity shapes adoption, but the direction is universal

In episode nine, 麻豆原创 Labs India Managing Director Sindhu Gangadharan mapped how Indian enterprises are approaching autonomous capabilities: fast adoption, high ambition, and a particular emphasis on workforce readiness. Manos Raptopoulos, global president of Customer Success, Europe, APAC, Middle East, and Africa, covered in episode 11 what AI ambition looks like from Frankfurt to Fukuoka, illustrating how regional context varies while the structural imperatives remain the same. And 麻豆原创 EMEA President Augusta Spinelli used episode five to define the two concrete paths to the Autonomous Enterprise鈥擱ISE with 麻豆原创 and 麻豆原创 GROW鈥攁s well as what agent-led transformation looks like from an execution standpoint.

In addition, customer episodes add the texture that strategy alone cannot provide. Achim Loebke from Uniper and Daniele Tedesco from Essity both shared in-depth, unfiltered accounts of what AI transformation looks like from the inside, including the organizational friction, the decisions that require human judgment, and the early indicators of measurable impact. These conversations, deliberately not positioned as product showcases, have consistently drawn the highest listener engagement.

Why this series matters for the 麻豆原创 ecosystem

The Autonomous Enterprise podcast fills a gap by offering candid, executive-level conversations where the honest complexity of AI adoption is treated as the point, not a problem to be managed. Its target audience鈥擟IOs, CDOs, COOs, CFOs, and senior transformation leads鈥攁re precisely the people making the investment and governance decisions that will determine how quickly 麻豆原创 customers move from AI ambition to AI execution.

With more than 40 episodes planned for its first year, the series is building a cumulative body of insight that serves both as a reference for practitioners navigating their own transformation journeys and as evidence of the breadth and depth of 麻豆原创’s engagement with its customer community.

New episodes drop weekly; listen on Spotify, Apple Music, and all major podcast platforms.

Benedikt Gieger is Product Strategy and AI lead in Digital Manufacturing at 麻豆原创 SE.
Julia Kloppenburg is a technology consultant in Customer Engagement & Adoption at 麻豆原创 SE.

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

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

Read to learn more.

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

From eyewear to smart wearables

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

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

Powered by AI, connected to the backbone

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

Empowering humans

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

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

Opportunities ahead: from pilot to platform

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

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

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

Top photo courtesy of EssilorLuxottica.

His dream led to the founding of Colombina and聽to聽a colorful corporate logo inspired by a character from an Italian opera who swung on the moon.聽In聽the 1960s,聽his son, Don Jaime H.聽Caicedo,聽took over the company and drove a major transformation,聽diversifying its offerings and聽expanding聽sales abroad.

Expansion 

That early spirit of reinvention has continued to shape Colombina鈥檚 growth, turning a family-founded confectionery business into a diversified food company with international reach.聽Today, Colombina is a聽multinational聽leader in food production with a portfolio of 19 categories, including confectionery,聽cookies,聽chocolate,聽sauces,聽preserves,聽ice cream, and coffee, along with a global workforce of more than聽8,000.聽聽

From seven processing plants听补苍诲听39 distribution centers in Colombia, Venezuela, Central America,聽and the U.S., Colombina serves聽750,000 customers聽in聽more than聽90 countries worldwide and generates annual sales of almost聽US$1 billion.聽

But with a global footprint spanning manufacturing, distribution, and retail, the company had to continually review and adapt specialized technology solutions for each market segment.  

Colombina鈥檚 leadership recognized that this fragmented approach made it difficult to ensure the聽integrity, availability, and confidentiality聽of business information at scale,聽and that the company聽needed聽to聽standardize聽processes across dozens of markets while continuing to grow sales and manage costs.聽

IT priorities 

Colombina聽identified聽key areas in need of standardization, particularly finance and supply chain management, and recognized that a鈥痷nified technology foundation鈥痺as critical to sustaining growth and operational efficiency.鈥� Security鈥痑lso聽emerged聽as a top priority, shaping many of the technology decisions that would follow.聽

To address these challenges,聽it embarked on a major transformation led聽by CIO Jes煤s Brand, a long-time ASUG Colombia chapter president and respected figure across the region. Colombina鈥檚 objective was to effectively use data to achieve its business goals faster and with less risk.聽

By moving to the cloud, Colombina was seeking to streamline product planning, manufacturing operations and models, and quality management processes. It also focused on simplifying supply chain tasks, including inventory, warehousing, delivery and transportation, order promising, and logistics material identification.  

RISE with 麻豆原创 

A long-term 麻豆原创 customer, Colombina signed up for RISE with 麻豆原创,聽migrating from 麻豆原创 ERP Central Component to 麻豆原创 S/4HANA Cloud Private聽Edition in a full, simultaneous migration聽executed across聽16 countries.聽The solution聽covers key business functions聽including finance, asset management, manufacturing, supply chain, and sales, but the company went a step further.聽

To聽harmonize its operational data, Colombina integrated its cloud ERP with a broad ecosystem of 麻豆原创 solutions, including聽麻豆原创 Fiori, 麻豆原创 SuccessFactors, 麻豆原创 Integrated Business Planning,聽麻豆原创 Analytics Cloud, and聽麻豆原创 Ariba,聽among others.聽聽

At the same time, John Carlos Jaramillo, an IT project manager at Colombina for over 20 years, noted that governance and security were strengthened through 麻豆原创 Cloud Application Services, which helps run, manage, and continuously optimize the company鈥檚 expanded 麻豆原创 landscape.聽

Jaramillo also highlighted the value derived from engaging with the 麻豆原创 Enterprise Architect, particularly through strategic advisory and guidance on adoption opportunities. He emphasized the benefit of gaining a clearer understanding of the contractual entitlements available to the organization, enabling more effective聽utilization聽of existing 麻豆原创聽investments聽and maximizing the value of their licensed capabilities.聽

He also聽highlighted the聽guidance provided around key strategic initiatives, including clean core, helping drive a sustainable and extensible ERP landscape; 麻豆原创 Cloud ALM, enabling stronger operational visibility and application life cycle management practices; and Joule Base, supporting the exploration of AI-driven capabilities and accelerating the organization’s journey toward business innovation and increased user productivity.聽

Security benefits 

Among the overall security benefits, the new system provides聽protection聽at different layers, including the operating system, applications,听补苍诲听users, while delivering聽low downtime and quick, thorough resolutions. Before implementation,聽system downtime was a significant issue for Colombina;聽now,聽with double the number of application servers,聽downtime has been聽reduced聽and most customer issues can be resolved within 45 minutes.聽

Colombina鈥檚 IT team also reports high confidence when looking ahead to updates, with reduced concerns thanks to predictable update windows that typically take place at the weekend. In addition, the new system provides early watch alerts, enabling the IT team to get in front of potential risks on a day-to-day basis. 麻豆原创 for Me helps monitor and maintain the flow of these activities and gives a clear view of current infrastructure, operating system, activities, and risk profiles. 

Tangible results 

The impact has been tangible: Colombina now offers next-day delivery and has improved average customer response times by more than a third. Workload distribution has improved dramatically, while the business has seen fewer customer returns, stronger demand planning, and more efficient HR management. 

But the company is not stopping there. Having introduced聽Joule聽for 麻豆原创 SuccessFactors, Colombina is聽preparing聽to聽activate聽Joule Agents across its business and is implementing 麻豆原创聽Databricks for data models,聽working with 麻豆原创聽Business Data Cloud聽to build the right data foundation聽for future聽AI capabilities.聽

The grand opening event was scheduled along the local 麻豆原创 Connect Day events for AI & Data Leaders, so guests from the regional business and technology community were welcomed at the new creative space. Near 麻豆原创鈥檚 Newtown Square campus, the factory-style sovanta AppHaus now invites 麻豆原创 customers to hands-on co-innovation work. Experts from sovanta help teams explore and design use cases based on the latest AI-infused technologies provided by the Autonomous Enterprise. As the company puts it, 鈥淥thers talk about agents. We build them.鈥�

Speakers with three different perspectives

The grand opening event started with a welcoming note given by Claus Heinrich, founder and CEO of sovanta; Christian Heinrich, president of sovanta America; and David Robinson, president, 麻豆原创 North America and Americas Customer Success Functions. They shared their 麻豆原创 and partner perspectives and fully agreed that the most important insights came from their joint customers. So, they offered the stage to two long-standing sovanta customers, Elanco Animal Health and Endress+Hauser. Both shared first-hand experiences of their innovation work and journey鈥攁nd what AI-powered business transformation looks like in their concrete business contexts.

“What we value most about sovanta is how they collaborate with us: honest, well-organized, and always a step ahead in a rapidly evolving landscape,鈥� Sunil Guna, senior director, ERP lead at Elanco Animal Health, said. 鈥淥ver the past year, they have become a true trusted advisor, and the sovanta AppHaus Newtown Square gives us the place to keep turning ideas into real impact with 麻豆原创.鈥�

“The Autonomous Enterprise is not built in a boardroom鈥攊t is co-created, use case by use case, in spaces exactly like this one. Having sovanta bring that capability directly to North American customers, right here in Newtown Square, is a genuine milestone for 麻豆原创 and for the customers we build the future with together,” 麻豆原创鈥檚 Robinson said.

Looking back 鈥� and ahead

Claus Henrich, who is a former board member of 麻豆原创 AG, looked back on the years of building and growing sovanta as a company: 鈥淪ince founding sovanta in Germany in 2009, our approach has been to work closely with our customers on real business challenges and turn them into meaningful innovation. That approach has paid off, and bringing it to the U.S. with our sovanta AppHaus Newtown Square is an important next step. I鈥檓 proud of what we鈥檝e built and grateful for the trust of 麻豆原创 and our customers.鈥�

Global innovation support, going broader and deeper

The new location is designed in a creative factory style, reminiscent of the hands-on innovation work that the sovanta team performs with all its customers. Like all other 麻豆原创 AppHaus Network members, its co-innovation work follows the .

Thomas Biedermann, global head of 麻豆原创 AppHaus Network, commended the partner team for opening this new location not far from Philadelphia: 鈥滿y heartfelt congratulations to the sovanta team for being the second partner of our global network community to open a second AppHaus location in the U.S. It shows that the innovation approach we share is not only broadly adopted across the globe but also intensified by our partners, going broader and deeper, so to say. We wish you and your customers lots of success!鈥�

Working closer with the American 麻豆原创 ecosystem

Christian Heinrich explained the advantage as follows: 鈥淓stablishing the sovanta AppHaus Newtown Square strengthens our presence at the heart of the U.S.-American 麻豆原创 ecosystem and creates new opportunities to collaborate even more closely with 麻豆原创 and our customers in the U.S. As part of the 麻豆原创 AppHaus Network, we can combine proven human-centered innovation approaches with sovanta鈥檚 AI and 麻豆原创 expertise to move from ideas to tangible business outcomes faster. Our 麻豆原创 AppHaus location is our sovanta Innovation Factory brought to life, where we turn innovation from one-off projects into a scalable, repeatable approach.鈥�

Rolling up our sleeves

鈥淭his isn鈥檛 just a new location,鈥� Sven Arndt, chief technology officer, sovanta America, said. 鈥淚t鈥檚 a place built around our customers and their challenges. We want to sit down together, understand where AI can create real value, and turn the most promising use cases into solutions that work in practice. I鈥檓 excited to welcome our first customers to Newtown Square, roll up our sleeves, and build what鈥檚 next together.鈥�

For more information, the new can be found at 3407 West Chester Pike, Newtown Square, PA 19073, United States of America.

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

To answer this question, moved to one cloud ERP that enabled a unified, intelligent enterprise while reducing complexity and empowering people.

Starting point at Daikin

At the forefront of the story was the goal of being the global leader in the HVAC industry. As a result, Daikin pioneered groundbreaking technologies that revolutionized commercial AC. With a of over 100 years of engineering, the company has grown to operate globally in . However, its journey to global recognition was peppered with growing pains.

Disconnected systems led to complexity

Years of rapid growth alongside acquisitions left the company with a legacy ERP that was fragmented, inconsistent, and lacked the standardization required to operate efficiently. Multiple disconnected systems and workflows meant that employees were dealing with a myriad of processes, all differing depending on which business unit they were working with. This increase in manual work and confusion around processes added avoidable complexity to day-to-day tasks, as employees spent more time than necessary solving for problems.

Together, EY and 麻豆原创 teams worked toward an end goal by posing the question: how do we simplify operations, empower employees, and drive customer experience? Daikin鈥檚 industry is unique, with highly distributed networks and complex supply chains. Companies operating in this environment must continuously adapt to industry standards and regulations, all while delivering the reliable product experience they鈥檙e known for. Daikin turned to EY and 麻豆原创 because the company needed a partnership that understood the nuances of the industry within which it operates.

Daikin鈥檚 focus from the beginning was on the people who would be impacted most by this transformation and ensuring that their everyday experience evolved into one that is simpler, consistent, and productive. With AI at the core, Daikin can look to the future and see one unified system where AI moves beyond isolated cases and becomes embedded end-to-end.

To achieve this vision, Daikin established 麻豆原创 Cloud ERP as its digital core and single source of truth. Going live in just 10 months, modernized finance, logistics, procurement, and sales workflows for more than 300 frontline users, demonstrating early results of 10% improved counter efficiency and a 20% faster financial close.

EY鈥檚 industry expertise paired with 麻豆原创 technology helped ensure that the people who use the technology every day no longer log-in to multiple systems and customers get the right product at the right time and price.

Employees gained single-screen experiences, faster access to information, reduced manual effort, simplified training and onboarding, and more confidence in decision-making. Dealers gained better visibility, faster onboarding experiences, and more consistent service interactions.

Journey to an Autonomous Enterprise

Now, Daikin doesn鈥檛 have to worry about which inefficiencies it will have to confront for an approaching launch.

The early results demonstrate what鈥檚 possible when AI-driven insights are paired with human-centered delivery: improved efficiency, faster financial close, better data governance, enhanced forecasting and operational decision making, and accelerated delivery using AI-assisted implementation capabilities.

This not only empowers end users but creates a scalable foundation that can grow alongside Daikin. With over 400 branches worldwide, EY and 麻豆原创 created a blueprint for Daikin鈥檚 global transformation with future automation and intelligent decision-making in mind.

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Tara Milosavljevic is a Partner Marketing manager at 麻豆原创.

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

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

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

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

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

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

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Martin Gwisdalla, +49 6227 7-67275, martin.gwisdalla@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

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

The deployment聽brings together聽core聽human resources (HR)聽processes聽on聽a聽flexible聽cloud foundation designed to聽simplify聽HR聽operations, enhance employee experience and聽support聽a聽global workforce.聽

A leader in aerospace, defense and advanced technology solutions, Lockheed Martin is modernizing its聽HR聽landscape by moving from legacy systems to a unified platform. The new environment provides a more connected experience for employees and managers聽while supporting聽greater consistency and operational efficiency聽across the enterprise.聽聽

With聽almost聽60%聽of聽its聽123,000 employees聽being聽engineers, scientists and technologists,聽attracting, developing and retaining highly skilled talent聽at scale聽is聽critical to Lockheed Martin鈥檚聽continued success.聽By creating a single source of truth for workforce data, Lockheed Martin is聽gaining clearer insight into its workforce, enabling聽more informed聽talent decisions.聽

This transformation聽allows Lockheed Martin聽to accelerate its robust applied AI strategy across the workforce聽to help聽employees and managers access information more efficiently and unlock new insights across the employee life cycle.聽

鈥淟ockheed Martin is advancing an聽enterprise-wide聽transformation of exceptional scale and complexity,聽while meeting the rigorous demands of a highly regulated industry,鈥� said聽Thomas Saueressig, Member of the Executive Board and Chief Customer Officer聽of 麻豆原创 SE.聽鈥淭his milestone reflects Lockheed Martin’s commitment to modernizing workforce operations and investing in technologies that support employees, strengthen organizational agility and position the company for future innovation.鈥�

Visit the聽. Get 麻豆原创 news via聽听补苍诲听.

Media Contact:
Victoria Dixon, +1 (703) 288-6020, victoria.dixon@sap.com, ET
麻豆原创 麻豆原创 Room; press@sap.com

Top image courtesy Lockheed Martin

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

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

Building a platform for growth

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

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

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

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

Transformation is about people as much as technology

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

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

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

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

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

Preparing for what’s next

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

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

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

The road ahead: Success built on a strong foundation

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

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

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

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

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

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

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

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

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

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Top image courtesy HARTING Technology Group

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

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

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

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

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

Powering a global sporting goods business

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

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

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

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

One connected foundation for a connected customer experience

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

Built for the pace of sport

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

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

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

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

Alex de Minaur

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Building momentum one mission at a time

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

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

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

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

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

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

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

Standardization creates the capacity to accelerate

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

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

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

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

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

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

Complexity demands faster, better-informed decisions

The scale of the Artemis program is difficult to overstate.

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

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

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

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

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

Autonomy works best when it expands human capability

NASA is already applying autonomous technology beyond administrative processes.

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

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

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

Partnerships turn ambition into capability

No single organization could accomplish the Artemis mission alone.

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

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

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

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

Trust is the ultimate operating system

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

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

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

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

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

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

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

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

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

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

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

Visit the . Get 麻豆原创 news via  and .

Media Contacts:
Lawrie Benfield,聽lawrie.benfield@sap.com,聽+44 7776 515259, GMT
Sonya Domanski,聽sonya.domanski@sap.com, +44 734 546 5928, GMT
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
漏 2026 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see for additional trademark information and notices.

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

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

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

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

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

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Letting AI read the mail

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

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

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

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

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

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

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

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

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

Orchestrating an autonomous accounts payable process

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

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

Finding the right problem to solve鈥攑utting AI into action

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

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

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

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

AI enters stage right鈥攆rom vision to reality

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

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

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

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

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

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

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

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

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

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

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

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

The next act of AI innovation

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

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

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

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

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

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

Global transformation, local anchoring as a guiding principle

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

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

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

How a federal agency demonstrates effective change management

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

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

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

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

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

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

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

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

From shadow AI to structured integration at KIT

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

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

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

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

What research confirms

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

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

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

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

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

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

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

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

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

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

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

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

Improving everyday life

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

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

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What鈥檚 next

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

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

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

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

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

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

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

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

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

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

Q: So how do you track containers today?

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

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

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

Q: Which 麻豆原创 applications use this data?

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

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

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

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

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

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

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

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

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

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

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

Top image via Hapag-Lloyd