International Space Station鈥檚 use of tabular AI and expanded partnerships demonstrate the art of the possible with 麻豆原创 AI

BERLIN 鈥� (NYSE: 麻豆原创) today announced a series of AI innovations at its annual 麻豆原创 TechEd conference that expands its portfolio for developers.

The enhancements to the Joule Studio and Joule Work solutions and 麻豆原创 Business AI Platform, including the latest tabular foundation model from 麻豆原创, offer new tools to build and extend AI agents, a richer data and business context for those agents to reason with, and strengthened governance controls to run them responsibly across the enterprise.

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 use no-code and pro-code tools to build and extend agents, choosing from more than 70 AI models. Customers in the Joule Studio early adopter program also can design and run n8n workflows in Joule Studio, with broader availability to follow. The 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph solutions 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. Importantly, 麻豆原创 customers鈥� data remains their data. 麻豆原创 does not share customer data with anyone to train or improve large language models.

鈥溌槎乖� TechEd has always been where builders shape what comes next,鈥� said Philipp Herzig, CTO of 麻豆原创 SE. 鈥淭his year, we are showing how we are moving from AI learning your business to running it. Joule Work is where you put your intent and get work done, in one experience across the entire 麻豆原创 landscape and beyond. That is the shift we are delivering at scale. Everything underneath runs on 麻豆原创 Business AI Platform, delivering faster time to value through out-of-the-box business understanding, higher accuracy, enterprise-readiness that is secure, governed and compliant from the first deployment, and a unique context layer built on over 50 years of process and industry expertise, encoded into every agent action. This is AI built where real work happens.鈥�

Joule Work and Joule Studio Make the Autonomous Enterprise Real

New capabilities for Joule Work will help make AI agents more effective at handling complex business processes via open protocols including Agent2Agent (A2A) and Model Context Protocol (MCP). These innovations advance Joule Work to better understand context, make decisions and execute multistep business workflows across enterprise systems and business domains:

• New agentic thinking capabilities that plan and orchestrate the steps assistants and agents need to reliably complete complex, multistep business workflows across 麻豆原创 landscapes.
• New capabilities in 麻豆原创 Knowledge Graph will incorporate data from additional 麻豆原创 and non-麻豆原创 sources that are unique to each customer. Users can query and explore their customized data in natural language and get accurate answers grounded in their own information.
• Advancements in interoperability allow third-party agents to securely invoke Joule Agents through a governed agent gateway. This enforced access control enables agents from different providers to collaborate across business workflows without sacrificing governance or control.
• New 麻豆原创 Enterprise AI Gateway solution will extend governance to AI interactions, offering a centralized way to manage AI traffic across the enterprise that will consolidate authentication, policy enforcement, routing, monitoring, usage tracking and auditing.
• New Joule Assistant and 麻豆原创 AI Agent Hub expand governance capabilities, and a new AI Governance Assistant helps customers meet compliance requirements for the EU AI Act. These are designed to address critical barriers to scaling enterprise AI: keeping AI systems classified, compliant and operating within defined organizational guardrails and context.

麻豆原创 also announced that Joule Studio has new observability and administrative controls to give developers and administrators visibility into agent behavior, model usage and spend. Joule Studio is 麻豆原创鈥檚 AI-first development environment that is accessible through Joule Work for building and extending enterprise agents. Joule Studio will run a time-bound promotion for select customers, where deploying and running custom agents on the 麻豆原创-managed Joule Studio runtime will remain free through December 2026. The design-time tooling for building and testing agents and n8n workflows with AI-assisted development will remain free under fair-use limits.

麻豆原创 Business Data Cloud as the Grounding Engine of the Autonomous Enterprise

麻豆原创 Business Data Cloud Ontology Manager in 麻豆原创 Business Data Cloud (麻豆原创 BDC) will provide an AI-native authoring experience for building ontologies across 麻豆原创 and third-party data. By helping AI understand how data from different teams, systems and applications relate to one another, AI agents can more accurately reason and make decisions across the enterprise. The solutions complement 麻豆原创 Knowledge Graph, which provides rich business context for 麻豆原创 data.

Additionally, 麻豆原创 is expanding its lakehouse capabilities with Dremio, adding native support for Apache Iceberg and Apache Polaris plus a high-performance, federated query engine that will enable unified data access without moving or copying the data. 麻豆原创 expands its partnership with Databricks, bringing the full scope and latest innovations of data engineering and enterprise AI capabilities into 麻豆原创 Business Data Cloud as 麻豆原创 Databricks. Core innovations such as Lakeflow, Agent Bricks, Unity Gateway, and Lakebase will help customers to simplify data management and provide a strong data foundation for their AI journey. With a deeper integration to 麻豆原创 Business Data Cloud, a new 麻豆原创 Application Data Foundation, as well as an integration between Joule and Genie, 麻豆原创 and Databricks are helping customers establish an enterprise-wide lakehouse as the foundation for the Autonomous Enterprise and Joule Work.

The 麻豆原创 Reltio solution, recently announced at 麻豆原创 Connect, will bring multidomain master data management to 麻豆原创 BDC. It unifies and enriches customer, supplier and product records across 麻豆原创 and non-麻豆原创 systems into trusted, reusable data products that can power applications and AI agents.

Tabular Foundation Model Trusted for Business, Validated in Space

麻豆原创 announced its latest tabular AI model, 麻豆原创-RPT-2.0, which will expand beyond classification and regression to support anomaly and outlier detection, and richer AI-powered analysis through embeddings. These native, multi-table capabilities will further reduce manual engineering while delivering faster, deeper insights from structured data. In addition, a new 麻豆原创-RPT-TS model will help customers predict values across time-indexed business metrics for key forecasting use cases using the 麻豆原创 Integrated Business Planning solution without requiring custom machine learning.

In partnership with HPE, 麻豆原创 also announced the deployment of 麻豆原创-RPT on the HPE Spaceborne Computer-2 aboard the International Space Station (ISS), one of the most significant edge-compute validations of any enterprise AI model to date. 麻豆原创-RPT is planned to run directly onboard the ISS to support anomaly triage, predict issues before failures escalate, reconstruct missing sensor values from historical data, and generate explainable mission-health reports for astronauts and ground teams 鈥� all without requiring data to be sent back to Earth for analysis.

鈥淭he same trusted 麻豆原创-RPT model that powers global supply chains also can work in space,鈥� said Herzig. 鈥溌槎乖粹�檚 deep-rooted, proven expertise has led to the International Space Station鈥檚 use of our tabular foundation model to predict potential mission-critical failures in orbit. With 麻豆原创-RPT and TabPFN from Prior Labs, we bring the unique capabilities of tabular AI into the enterprise and across every frontier.鈥�

Out-of-the-Box Assistants and Agents Define Our Advantage

For five decades, 麻豆原创 has built the systems of record that run your business. We will deliver, out of the box, a suite of assistants and agents across five horizontal domains all running on 麻豆原创 Business AI Platform 鈥� with industry specialization 鈥� that provides a baseline for systems of intelligence and systems of action.

Ecosystem Partners Help Expand Platform Capabilities

The global 麻豆原创 partner ecosystem brings new capabilities to the ongoing operation and extension of solutions on 麻豆原创 Business AI Platform, helping customers advance toward the Autonomous Enterprise. At 麻豆原创 TechEd, the following partner announcements were highlighted:    

A collaboration with Intel closes the loop between enterprise workflows and autonomous robots, bridging informational technology and operational technology through physical AI.

In addition, new 麻豆原创 solution extensions with , and extend autonomous capabilities for customers, helping them run their core processes end to end, without handoffs between systems.

Beyond technology, 麻豆原创 is empowering its strong developer community with new enablement and engagement with tens of thousands joining for leading up to 麻豆原创 TechEd, new reference architectures, tutorials and hands-on opportunities attendees can try onsite at the event and at home.

To learn more about today鈥檚 announcements visit our .

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About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE: 麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit .

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漏 2026 麻豆原创 SE. All rights reserved.
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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.

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About 麻豆原创

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

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 (212) 653-9600, aimee.leabon@sap.com, EDT
麻豆原创 麻豆原创 Room; press@sap.com

This press release was created with the assistance of artificial intelligence and has been reviewed by the editorial team.

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.
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.鈥�

AI Innovations Aim to Boost Business Productivity by up to 30 Percent;
Partnerships with Perplexity and Palantir Bring out Customers鈥� Best

ORLANDO 鈥� At its annual 麻豆原创 Sapphire conference, (NYSE: 麻豆原创) unveiled innovations and partnerships that put the power of Business AI in every user鈥檚 hands, revolutionizing the way work gets done.

From a virtually omnipresent Joule assistant to an expanded network of Joule Agents that work across systems and lines of business, 麻豆原创 heralds a new era that democratizes access to Business AI and can drive productivity gains of up to 30 percent.

鈥溌槎乖� combines the world鈥檚 most powerful suite of business applications with uniquely rich data and the latest AI innovations to create a flywheel of customer value,鈥� said 麻豆原创 CEO Christian Klein. 鈥淲ith the expansion of Joule, our partnerships with leading AI pioneers, and advancements in 麻豆原创 Business Data Cloud, we鈥檙e delivering on the promise of Business AI as we drive digital transformations that help customers thrive in an increasingly unpredictable world.鈥�

AI that boosts productivity

麻豆原创鈥檚 generative AI assistant Joule can be everywhere you work, delivering personalized answers on everything you need to be more productive.

Joule can accompany business users throughout their day, in and out of the 麻豆原创 application universe, to find data, surface real-time insights and streamline workflows. Joule鈥檚 new ubiquity includes an action bar powered by WalkMe that studies user behavior across applications, turning the assistant into an always-available, proactive AI that can anticipate users鈥� needs before they arise — always adhering to .

A collaboration with Perplexity, an AI-powered answer engine company, enhances Joule鈥檚 ability to draw on structured and unstructured data to solve complex business problems. Powered by Perplexity and the 麻豆原创 Knowledge Graph, Joule now instantly answers questions with structured, visual answers — such as charts and graphs — grounded in real-time business data within 麻豆原创 workflows. For example, a user could ask the tool how recent external events might impact their business and get a forecast based on both current events and the company鈥檚 own business data.

麻豆原创 also unveiled an expanded library of Joule Agents that reimagine business processes and workflows from the ground up. Fueled by the world鈥檚 most powerful real-time business data and orchestrated by Joule, these AI agents work across systems and lines of business to anticipate, adapt and act autonomously so organizations can stay agile in a rapidly changing world. Partnering with industry leaders, 麻豆原创 offers an ecosystem of interoperable agents that can execute end-to-end processes. The new agents span customer experience, supply chain management, spend management, finance, and human capital management.

Finally, 麻豆原创 introduced an operating system for AI development that transforms how enterprises build, deploy and scale AI solutions. AI Foundation gives developers a single entry point for building, extending and running custom AI solutions at scale, making it the first real operating system for Business AI. A new prompt optimizer, designed collaboratively with the frontier AI lab Not Diamond, also helps developers create more effective AI prompts quickly, reducing work on complex use cases from days to minutes.

Data that drives smarter decisions

麻豆原创 also introduced new intelligent applications in 麻豆原创 Business Data Cloud, each built for a specific line of business. These applications can continuously learn, simulate outcomes and guide actions using business-critical data, detecting changes to optimize processes, anticipate needs, and collaborate with both human and artificial thinkers to drive meaningful impact. The People Intelligence application, for instance, optimizes team performance by transforming people and skills data into workforce insights and AI-driven recommendations.

Additionally, 麻豆原创 and Palantir are partnering to facilitate joint customers鈥� cloud migration journey and modernization programs. Seamless connectivity between Palantir and 麻豆原创 Business Data Cloud will enable customers to build a harmonized data foundation across their enterprise landscape. Together the companies will responsibly deliver essential outcomes and support customers, including the U.S. government, to quickly adapt to changes and disruptions.

Applications that accelerate cloud adoption

The company also announced 麻豆原创 Business Suite packages, which are designed for customers to simplify the adoption of 麻豆原创 cloud solutions that address their specific business challenges. 麻豆原创 Build is embedded in these packages, so organizations can customize applications to meet their unique needs.

Finally, 麻豆原创 unveiled a new solution that helps customers transition to the cloud faster. With Joule as the entry point and drawing on insights from 麻豆原创 solutions including 麻豆原创 Signavio and 麻豆原创 LeanIX, the solution delivers personalized guidance and actionable recommendations tailored to an organization鈥檚 transformation objectives and can help deliver up to 35 percent faster time to value.

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About 麻豆原创

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

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However, there are challenges when we move beyond text to apply these models to structured, tabular data, which is essential for enterprise business operations. This imbalance comes partly because of the availability of training data. Text used to train models is plentiful, often consisting of text scraped from the internet, whereas tabular data, especially data with multiple linked tables, is scarce.

To bring AI advancements to the enterprise sector, researchers working on training and benchmarking the performance of these models in an enterprise setting need realistic tabular data.聽That’s why 麻豆原创 developed “Sales Autocompletion Linked Business Tables” (SALT), a curated dataset that includes anonymized data from a customer鈥檚 enterprise resource planning (ERP) system.

SALT is specifically designed to support researchers working on AI models for real-world business contexts and can be accessed on and .

Challenges of getting and working with enterprise data

Providing the research community with realistic enterprise data like SALT has been challenging. Data privacy, confidentiality, and commercial interests make obtaining large, clean, high-quality enterprise datasets difficult for training models and benchmarking them for specific use cases. This means there is a growing gap between what researchers are working on and what actual enterprise data looks like.

In addition to the problem of availability, enterprise data is complex. First, business data is usually stored in multiple interconnected tables. For example, a sales order entry may be linked to numerous tables, such as customer IDs connected to a supplier table containing address information. Second, tables are inherently heterogeneous in the data type they can contain. One field may be text, while the other contains numerical or categorical values. Finally, business data frequently shows significant column imbalances, meaning that, for example, a specific product category makes up 90 percent of all sales orders while others are rarely used.

The best way to help researchers develop enterprise models for these challenges is to provide accurate enterprise data.

SALT dataset

Accurate enterprise data is a bottleneck in AI research. The SALT dataset alleviates this bottleneck by providing the research community with the first real ERP dataset. It uses actual industry data collected by an ERP system that records sales orders. It has been minimally processed to protect privacy.

鈥淭here is a gap between academia and industry in terms of data. It cannot be closed easily because of privacy,” says Tassilo Klein, one of the 麻豆原创 researchers behind the dataset. 鈥淏ut we want to enable the research community to work on real problems, not just simulated problems.鈥�

ERP systems help organizations manage core business operations like finance and spending. With millions of entries and extensive, interconnected relational tables focused on sales, the SALT dataset replicates customer interactions in an ERP system. SALT’s realistic enterprise data means it is a perfect basis for helping models understand the characteristics of business data and validate their performance through benchmarking. It also should help researchers develop better foundation models for linked business data.

Getting this right will advance enterprise automation, as many enterprise business processes are heavily centered around data in structured tabular formats. Even though this data plays a crucial role in enterprise day-to-day activities, the generative AI revolution has yet to tap into them.

“SALT is a first step to providing researchers with authentic representative industry data that gives a glimpse into actual enterprise data; for now, we are starting with just one customer and use case,” shares Johannes Hoffart, CTO of Business AI at 麻豆原创. “However, we plan to publish more datasets that cover a diverse set of customers and use cases that, along with SALT, can serve as a basis for pre-training, adapting, as well as benchmarking models.”

Collaboration with academic institutions is also a motivation for publishing this data.

“At 麻豆原创, we hope to collaborate with academic partners who usually can only publish their results on open repositories,” Klein says. “Another hope for the dataset is encouraging more people to explore and validate new methods that help foundation models better deal with tabular enterprise data.”

What 麻豆原创 is doing

Alongside its investment in the open research community with SALT, 麻豆原创 is building 麻豆原创 Foundation Model to handle enterprise tabular data. This table-native AI model aims to accelerate time-to-value for predictive tasks on tabular data, offering a model that can work with tabular data out-of-the-box with little or no additional training data. The , published alongside SALT, provides a first glance at how this model could look.

Knowledge graphs are critical here. They work by exposing metadata 鈥� the who, what, and when of data 鈥� making relationships between information accessible. This provides a structured, interconnected representation of the data that AI models can easily understand and utilize. With the help of 麻豆原创 Knowledge Graph, 麻豆原创 Foundation Model can be scaled and adapted to a wide array of diverse use cases with some lightweight fine-tuning.

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People were confiding their deepest thoughts to the chatbot and experts predicted that within a few years, conversations with chatbots would be indistinguishable from those with humans. It certainly took more than just a few years, but here we are at the edge of another stage of AI evolution.

Evolution of AI agents

Artificial intelligence has evolved significantly since 1966, advancing from basic rule-based systems to highly autonomous decision-making systems.

In the 1990s and early 2000s, relied on predefined keyword responses but lacked the ability to adapt to complex queries. By the 2010s, intelligent virtual assistants such as Alexa and Siri enhanced user interactions and introduced AI into everyday life through smart home integrations. In the 2020s, task-specific began to emerge, each tailored to perform specialized tasks. For instance, AI-driven personal finance assistants can analyze spending patterns and suggest savings plans, while AI-powered content moderation tools scan social media platforms to identify harmful content.

Looking ahead, autonomous AI systems are rapidly advancing. , composed of multiple independent agents, can collaborate to achieve a complex workflow beyond the ability of an individual agent. Tasks are coordinated between agents, as opposed to individual agents that often require human coordination and intervention between tasks. For example, in manufacturing, AI agents can independently optimize production lines, while in healthcare, AI systems are assisting in surgery by making real-time adjustments during procedures. Autonomous systems are also being deployed in logistics to manage inventory and optimize warehouse operations without human intervention.

Expanding capabilities of agents

Today, AI agents are like super-efficient digital teammates 鈥� smart systems equipped to perform tasks autonomously, learning from experience and adapting along the way.

Today鈥檚 agents have core capabilities like these:

• Planning: Agents go beyond executing single actions; they orchestrate processes, breaking down complex problems and mapping out efficient, step-by-step approaches.
• Reflection: Unlike traditional software, agents reflect their actions in real time and learn from mistakes. They self-correct and iteratively reason through the problem until they find the best solution. This capability allows them to handle more irregular, complex challenges, makes them more effective over time.
• Tool Usage: AI agents can use external tools 鈥� like calculators, APIs, databases, and even other AI models 鈥� to expand their capabilities, broadening the scope of tasks they can accomplish.
• Collaboration and Multi-Agent Interactions: Agents aren鈥檛 limited to working solo. They thrive in cooperative ecosystems, coordinating with other specialized agents and humans, leveraging their unique expertise to achieve a shared goal.

Why is AI agent innovation accelerating at this moment?

The answer lies in the remarkable advancements in foundation models. These models allow AI to handle complex data and produce outputs like code, text, or media that are tailored to specific tasks. They enable systems to think through problems deeply and autonomously, mirroring human cognitive processes.

For instance, the latest reasoning models like OpenAI’s o1 and o3 are game changers. They do not just perform tasks; they use real-time computing power to “think” and generate human-like outcomes. And the progress is mind-blowing: o3 scored over 80 percent on a human-like reasoning test while its predecessor, GPT-4o, scored only two percent on the same test just one year earlier.

With such rapid advances, AI agents are getting better at automating and enhancing business decisions, truly pushing the limits of what autonomous systems can achieve.

What makes 麻豆原创 unique in this space?

麻豆原创 Business Suite offers a unique advantage in the era of agentic AI. It is not just a collection of solutions; it’s a powerhouse for transformation that boosts continuous innovation. Here鈥檚 the simple breakdown:

• Applications: With , cloud ERP applications, 麻豆原创 Business AI, and 麻豆原创 Business Data Cloud come together to deliver exceptional business value 鈥� all powered by 麻豆原创 Business Technology Platform (麻豆原创 BTP). In this way, 麻豆原创鈥檚 business applications and technology platform aren鈥檛 siloed tools; they integrate processes end-to-end. This integration ensures that every action taken within these applications is based on trusted, business-critical data at its source.
• Data: All the data, whether from 麻豆原创 or other systems, is collected and unified in 麻豆原创 Business Data Cloud. This makes it a single, trustworthy source that breaks down data-silos and fuels advanced AI-driven insights. Because AI is only as good as the data you feed into it.
• AI: Joule helps employees coordinate intelligent agents to work together, breaking down barriers between functions and enabling real-time, company-wide improvements. Unlike others who might use AI in limited areas, 麻豆原创 integrates AI throughout your entire organization, enhancing efficiency and resilience on a large scale.

麻豆原创 is in a unique position to turn AI agent technology into business value given the breadth of applications and data we offer, allowing automation of tasks along all key business processes. Our domain knowledge is grounded in real-world business data and our process know-how is maintained in 麻豆原创 Signavio and 麻豆原创 Knowledge Graph. Bringing it all together, our unified entry point with our AI co-pilot Joule enables us to use AI agents to automate processes and augment decision-making.

Joule agents are collaboration experts

Thanks to our fully integrated approach, we are designing a system of collaborative Joule agents that work within and across the suite to support every business function, solving complex challenges and driving cross-enterprise productivity. Businesses don鈥檛 need hundreds of AI agents, just the right ones with the right skills, grounded in the right data, with the right guidance from 麻豆原创鈥檚 end-to-end business processes.

agents are available in all parts of the business, delivered out of the box with Joule and 麻豆原创鈥檚 suite of applications. This enables the transformation of entire business processes 鈥� end to end.

Agentic AI represents a transformative leap in business technology. By bringing together structured data management, seamless system integration, and advanced task automation, AI agents empower teams to operate with efficiency, accuracy, and agility.

麻豆原创 is taking the necessary steps for the next era of enterprise management by embedding systems of AI agents into 麻豆原创 applications, fueled by context-rich business data, helping customers to realize the full potential of 麻豆原创 Business AI.

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WALLDORF 鈥� (NYSE: 麻豆原创) today unveiled groundbreaking AI innovations across a technology foundation that drives 87% of global commerce, putting its generative AI copilot Joule at the center of a new way of doing business.

At its annual 麻豆原创 TechEd conference, 麻豆原创 announced powerful new capabilities that complement and extend Joule, including collaborative AI agents imbued with custom skills to complete complex cross-disciplinary tasks. Other innovations include the 麻豆原创 Knowledge Graph, a next-generation solution poised to help developers unlock the full value of 麻豆原创 data by connecting it with rich business context, and new tools to ensure developers can continue driving Business AI innovation.

鈥溌槎乖粹�檚 innovation drives real business outcomes, and today’s advancements help customers harness the power of AI, data and new development solutions to catalyze growth,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product Engineering. 鈥淒rawing on 麻豆原创鈥檚 unmatched business and technology expertise, the AI innovations we鈥檙e announcing at TechEd forge a new human-AI partnership to transform the landscape of modern business.鈥�

Supercharging Joule

On the eve of its first birthday, Joule marks a watershed in how business gets done. 麻豆原创 introduces collaborative AI agents to a copilot that truly speaks the language of business, expands Joule鈥檚 capabilities to support 80% of 麻豆原创鈥檚 most-used business tasks and embeds Joule more deeply within the company鈥檚 portfolio.

Collaborative multi-agent systems deploy specialized AI agents to tackle specific tasks and enable them to collaborate on intricate business workflows, adapting their strategies to meet shared objectives. 麻豆原创 is infusing Joule with multiple collaborative AI agents that will combine their unique expertise across business functions to collaboratively accomplish complex workflows. These AI agents enhance productivity by breaking down silos and freeing workers to concentrate on areas where human ingenuity thrives. 

Two use cases debuted at TechEd showcase the agents鈥� transformative power:

• A dispute management use case employs autonomous AI agents to analyze and resolve dispute resolution scenarios including incorrect and missing invoices, unapplied credits and denied or duplicate payments.
• A financial accounting use case employs autonomous AI agents to streamline key financial processes by automating bill payments, invoice processing, and ledger updates while quickly addressing inconsistencies or errors.

Harnessing the Power of Data

麻豆原创鈥檚 AI innovations also draw on the company鈥檚 unmatched business data expertise. The new 麻豆原创 Knowledge Graph solution, accessible through 麻豆原创 Datasphere and Joule in Q1 2025, will give users a deeper layer of business understanding by seamlessly mapping relationships and context across 麻豆原创’s vast data landscape, empowering organizations to make better decisions with their data. By offering ready-to-use relationships between business entities like purchase orders, invoices, and customers, the solution can significantly reduce the complexity of manual data modeling. 麻豆原创 Knowledge Graph grounds AI in 麻豆原创-specific business semantics, which reduces the risk of inaccurate or irrelevant results and makes it easier for organizations to build intelligent applications and leverage generative AI more effectively.

Empowering Developers

麻豆原创 also launched a swathe of innovations for developers to continue driving Business AI innovation. New generative AI developer capabilities such as code explanation and documentation search in 麻豆原创 Build, the company鈥檚 platform for extending its solutions, will reduce development time for Java and JavaScript developers. 麻豆原创 Build is also adding an Extensibility Wizard feature that will let developers access 麻豆原创 Build directly from 麻豆原创 S/4HANA Cloud Public Edition, simplifying the extension process. Meanwhile, ABAP developers and fusion teams will get seamless access to ABAP Cloud development tools from 麻豆原创 Build.

Finally, 麻豆原创 announced that it has already fulfilled its pledge to upskill 2 million people worldwide by 2025. Through its , the company has lowered the world鈥檚 digital skills gap through role-based certifications, free training materials, hands-on opportunities for developers, and more. 麻豆原创 continues to expand its growing portfolio of AI-related learning opportunities, including courses on generative AI, AI ethics and 麻豆原创鈥檚 advanced AI tools and platforms.

Visit the 麻豆原创 News Center. Follow 麻豆原创 at .

About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE: 麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit .

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:
Joellen Perry, joellen.perry@sap.com, +1 (626) 265-0370, PST
Marcus Winkler, marcus.winkler@sap.com, +49 15157118691, CEST
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 2023 Annual Report on Form 20-F.
漏 2024 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.鈥�