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

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

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

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

From display to decision

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

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

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

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

Co-innovation at the heart of industry

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

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

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

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

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

From industry expertise to intelligent action

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

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

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

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

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

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.

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

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

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

Frontier LLMs are not enough

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

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

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

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

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

What makes Industry AI different

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

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

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

Getting up close with customers

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

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

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

Benefits

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

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

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

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

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

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

What comes next

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

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

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

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

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

Click the button below to load the content from YouTube.

Always allow YouTube

The business AI imperative

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

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

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

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

ERP as the foundation for business AI

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

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

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

麻豆原创 Business AI Platform

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

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

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

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

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

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

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

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

Industry AI: H&M and Sector-Specific Transformation

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

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

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

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

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

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

Closing: The Autonomous Enterprise

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

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

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

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

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

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

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

Introducing 麻豆原创 Business AI Platform

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

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

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

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

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

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

Designing the Autonomous User Experience

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

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

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

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

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

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

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

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

.

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�.

Note to editors:
To preview and download broadcast-standard stock footage and press photos digitally, please visit . On this platform, you can find high resolution material for your media channels.

For customers interested in learning more about 麻豆原创 products:
Global Customer Center: +49 180 534-34-24
United States Only: 1 (800) 872-1麻豆原创 (1-800-872-1727)

For more information, press only:
Aim茅e Leabon, +1 646-799-3277, aimee.leabon@sap.com, EST
Marcus Winkler, +49 622 776-74-97, marcus.winkler@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.
Please consider our . If you received this press release in your e-mail and you wish to unsubscribe to our mailing list please contact press@sap.com and write Unsubscribe in the subject line.