As AI is deployed across business processes, effective decision-making now hinges on unifying fragmented data and business context. AI agents can generate results quickly, but good judgement depends on deep understanding of how an organization runs. They need access to data across the enterprise, trusted views of business entities, and an understanding of the relationships, processes, and rules that define how the business operates.

麻豆原创 Business Data Cloud (麻豆原创 BDC) is the trusted data foundation for 麻豆原创 Business AI Platform, providing a business data fabric that connects data, semantics, processes, and policies, while preserving business context that gives data meaning. The innovations announced this week extend these capabilities making enterprise data more accessible and providing agents with the trusted business context for successful AI.

Extend the business data fabric with an open lakehouse foundation

Enterprise data is distributed across sources, including databases, warehouses, and data lakes. Making that data accessible across environments often requires duplicating copies, increasing costs and making it harder to maintain consistent business context.

With the completed acquisition of Dremio, 麻豆原创 is enhancing the open lakehouse foundation for business users and AI agents to access, share, and work with distributed data across the landscape.

• Discover, govern, and share data across platforms: 麻豆原创 BDC now supports native Apache Iceberg and a universal catalog powered by Apache Polaris, providing an open and governed data foundation for users and Joule to consume data. Together with 麻豆原创 BDC Connect, this builds on 麻豆原创鈥檚 commitment to platform interoperability with zero-copy data sharing across partners including Databricks, Snowflake, Google, AWS, and Microsoft.
• Query distributed data with zero copies: Supported by Dremio鈥檚 high-performance SQL query engine, users can federate access across lakehouses and relational databases including SQL Server, MongoDB, Oracle, and Postgres without moving data. Through integration with Joule Work, business users can ask complex questions in natural language and get answers grounded in trusted 麻豆原创 and custom data products and the business context behind them, without needing to know where the data resides or how to query it.
• Accelerate analytics and AI autonomously: For developers, maintaining query performance requires building summary tables, updating materialized views, or creating separate BI extracts to keep analytical queries fast enough for analytics and AI. This engineering work is reduced with Dremio鈥檚 Reflection technology, which learns from how your data is being used and automatically precomputes and optimizes the results your workloads need. As workloads change, Reflections adapt with them, removing the need for manual performance tuning while delivering sub-second performance at scale for analyst queries and high-concurrency agentic workloads.

鈥淲ith 麻豆原创 Business Data Cloud, we are building an enterprise foundation that unifies, governs, and is ready for innovation, so analytics and AI can scale without compromising the quality, compliance, and trust.”

Ganesh Sivakumar, Global Chief Data Analytics & AI Officer, Mead Johnson Nutrition

Accelerate your journey to the cloud

We also announced an expansion of the partnership with Databricks, bringing the latest set of industry-leading data platform capabilities including Lakeflow, Agent Bricks, Unity Gateway, and Lakebase to 麻豆原创 Databricks in 麻豆原创 Business Data Cloud.

麻豆原创 Application Data Foundation makes data from 麻豆原创 applications available for consumption in your 麻豆原创 BDC engine of choice, including 麻豆原创 Databricks. This provides a cloud-native way to access data in 麻豆原创 Business AI Platform, helping to lower the total cost of ownership and reduce time to value. 麻豆原创 data products can subsequently be shared across the business data fabric using 麻豆原创 BDC Connect for zero-copy access, avoiding the creation of additional pipelines, data copies, and silos.

For example, a manufacturer could use historical sales, inventory, and supply chain data to develop AI models for demand forecasting. The data is accessible from 麻豆原创 BDC, where data professionals can enrich it with third-party data and create custom data products in 麻豆原创 Databricks. Data engineers can build models on a semantically rich foundation, helping produce forecasts grounded in logic that is already embedded in 麻豆原创 systems while accelerating model development.

Build a trusted system of context across domains

Unified master data is only as valuable as the governance applied to it. Across the enterprise, the same customer, supplier, or product can appear differently across systems with different identifiers, attributes, and relationships. For an AI agent, understanding that these records represent the same business entity is essential for making trusted decisions.

Earlier this year, we announced the acquisition of Reltio, adding capabilities to help customers cleanse, harmonize, and unify data across systems. With the acquisition now complete, is now generally available in 麻豆原创 BDC, bringing multi-domain master data management natively into the business data fabric.

麻豆原创 Reltio鈥檚 AI-based entity resolution matches and reconciles records across 麻豆原创 and third-party systems to create unified, 360-degree views of business entities, including customers, suppliers, products, and other critical entities. By bringing together trusted attributed, relationships and business interactions 麻豆原创 Reltio gives business users and agents a more complete understanding of these entities across sources. These trusted profiles can be made available as governed data products in 麻豆原创 BDC, giving Joule and Joule agents consistent, connected business context to support decisions and actions.

Turn enterprise knowledge into reusable business context for AI

Every organization operates according to a defined set of business rules, relationships, processes, and policies that determine how decisions are made. 麻豆原创 Knowledge Graph provides rich business context across your data, connecting thousands of entities, relationships, and rules, creating a comprehensive view of the enterprise. Agents working on specific tasks need access to the context most relevant to the business problem they are solving, rather than the full enterprise model.

At 麻豆原创 TechEd Berlin, we introduced 麻豆原创 Business Data Cloud Ontology Manager, a new AI-assisted experience that enables teams to create domain-specific views of 麻豆原创 Knowledge Graph for applications, agents, or defined business scenarios. These curated views bring together the entities and relationships most relevant to each use case, grounding agents in the right business context they need to solve a particular business problem.

For example, a collections agent needs to understand customers, invoices, payment terms, and credit risk. 麻豆原创 Business Data Cloud Ontology Manager lets teams create and publish a domain view for collections. This context helps Joule Agents connect information across these business entities, supporting answers and recommendations that reflect the relationships and rules relevant to the scenario. Because these views inherit definitions and governance from 麻豆原创 Knowledge Graph, teams can reuse the same business semantics in views for related use cases, such as cash flow or order management, without having to redefine those concepts.

Get started

Irfan Khan is president and chief product officer of 麻豆原创 Data & Analytics.

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 .

Visit the . Get 麻豆原创 news via  and .

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:
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�

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.

It鈥檚 a noticeable shift that could fundamentally change how enterprises operate. But according to Manish Sood, CEO of Reltio, an 麻豆原创 company, and co-author of Agentic Intelligence, it also exposes a challenge companies have wrestled with for decades: fragmented, inconsistent, and poorly connected data.

鈥淭here is tremendous enthusiasm around agentic AI right now, and for good reason,鈥� said Sood. 鈥淎lmost every company is experimenting with it in some form or the other. But there is a very significant gap between ambition and underlying readiness.鈥�

Research conducted by Harvard Business Review Analytic Services underscores that readiness gap: 94% of organizations surveyed are exploring or implementing agentic AI, while only 15% believe their data foundation is truly ready.

鈥淎n AI demo can tolerate a lot of imperfections and gaps in the data,鈥� said Sood. 鈥淎n AI agent operating inside a real business context cannot. The moment we give software the ability to make decisions or to take action, the quality, timeliness, and context of underlying data becomes much more consequential.鈥�

AI highlights legacy data challenges

Sood believes fragmentation, trust, and governance are three recurring barriers when it comes to AI adoption. Because data remains scattered across hundreds or even thousands of enterprise systems, governance models built for traditional enterprise systems must evolve for an environment in which AI agents can initiate actions.

While these are familiar challenges, the speed and autonomy AI introduces is much different.

Historically, humans have compensated for fragmented systems via tapping into institutional knowledge. Employees know which spreadsheet to check, which colleague to call, or which exception to make. AI agents do not inherently possess that organizational context.

鈥淚f a human has incomplete information, we can often recognize it, ask another question, or find someone who knows the history,鈥� said Sood. 鈥淚n comparison, an autonomous agent may simply act.鈥�

The question for enterprises therefore shifts from whether they can build an AI agent to whether they can trust the environment in which that agent operates.

From digital filing cabinets to connected intelligence

Many organizations still operate around what Sood refers to as the 鈥渇iling cabinet鈥� model. When businesses digitized, individual functions created their own systems for marketing, finance, sales, supply chain, service, and human resources. Moving those systems to the cloud did not necessarily eliminate the underlying silos.

鈥淭he intelligence age requires us to move from storing information to connecting that information,鈥� he said.

This connected foundation supports what Sood and his co-author describe as 鈥渃o-agency,鈥� a new relationship between people and intelligent machines.

Historically, enterprise technology followed a relatively simple model: humans made decisions and machines executed them. AI changes that equation. Machines can contribute speed, scale, pattern recognition, and continuous operation, while people provide judgment, experience, accountability, and an understanding of nuance.

鈥淚f the human sees one version of the customer and the agent sees another, you don鈥檛 have co-agency; you have conflict,鈥� said Sood.

Turning insight into action

These implications extend beyond technology architecture as agentic intelligence requires companies to rethink strategy, investment, workflows, governance, and the relationship between people and machines. It鈥檚 a notable shift that moves from insight to action.

鈥淚nsight by itself does not create economic value. Action does,鈥� Sood shared.

For example, predicting that a piece of industrial equipment is likely to fail can provide valuable information. But an intelligent system capable of coordinating maintenance, locating the necessary parts, and scheduling service before the failure occurs can potentially transform that insight directly into a business outcome. In order for this to happen, however, trusted information needs to be available when and where decisions happen.

According to Sood, four responsibilities are necessary for organizations preparing for this environment:

1. Connect critical enterprise data.
2. Contextualize it with relationships, history, policies, and interactions.
3. Operationalize that trusted context at the point of decision.
4. Govern how AI agents act.

鈥淒efine where an agent can act, where a human needs to intervene, how decisions are traced, and how systems learn from mistakes,鈥� Sood explained. 鈥淭he goal isn鈥檛 simply to make more data available to AI. The goal is to make the enterprise understandable through AI.鈥�

Building for the intelligence age

As AI models continue to improve, Sood believes competitive advantage will increasingly come from proprietary data, business context, workflows, processes, and feedback mechanisms surrounding them.

Trust will also become more consequential as organizations delegate greater authority to AI. As a result, companies will need confidence not only in what an agent knows, but where its information came from, how current it is, and what boundaries govern its actions.

鈥淭he companies that win won鈥檛 simply be the companies with the most sophisticated AI,鈥� said Sood. 鈥淭hey will be the companies that create environments in which AI can understand the business, operate with trusted context, and act responsibly.

麻豆原创 Business Data Cloud (麻豆原创 BDC) is the data foundation of that platform, the business data fabric that anchors universal business context, serving as the trusted knowledge core聽for every enterprise application and agent.聽

The future of agentic organizations will be driven by AI with the deepest organizational knowledge. That future doesn’t start with AI models; it starts with whether your data foundation can give agents the business context they need to act autonomously. 

Today, we are introducing innovations that move organizations closer to becoming an autonomous enterprise.

Turn all your data into business outcomes 

A business data fabric architecture ensures every agent, application, and decision draws from the same trusted business context. And today, we are introducing new business data fabric capabilities that bring multi-model, unified master data, and embedded governance to your agentic foundation. 

• 麻豆原创 HANA Cloud natively available in 麻豆原创 Business Data Cloud:聽麻豆原创 HANA Cloud聽is聽now a聽core聽component of 麻豆原创 Business Data Cloud.聽As the AI database聽for 麻豆原创 BDC,聽麻豆原创聽HANA Cloud provides a聽unified聽in-memory engine聽for agents to reason across transactional, analytical, and multi-model workloads聽such as spatial, graph, and vector.聽In practice, this means agents can navigate relationships across customers and suppliers, analyze geographic dependencies, or perform semantic search in real time.聽And because every workload runs on a single in-memory engine with native workload management, inference time drops dramatically, lowering TCO and improving the predictability of AI聽costs.聽With 麻豆原创 HANA Cloud, 麻豆原创 Databricks, and 麻豆原创 Snowflake, 麻豆原创 Business Data Cloud delivers聽intelligent compute for聽every data and AI workload.
• Reltio in 麻豆原创 Business Data Cloud: With the completed acquisition of Reltio, 麻豆原创 is bringing multi-domain master data management capabilities directly into 麻豆原创 Business Data Cloud, helping customers unify, cleanse, and harmonize data across 麻豆原创 and third-party聽sources. Reltio鈥檚 AI-based entity resolution identifies and merges related records聽into a single, consistent view of business entities.聽Low-latency delivery and Model Context Protocol support enable real-time, multi-agent workflows across聽your data landscape: a procurement agent, for example, can assess supplier risk and trigger action almost instantly using trusted, real-time data. Together, this becomes a golden record system of context that Joule Agents use to deliver faster time-to-value for business AI.
• 麻豆原创 Master Data Governance natively available聽in聽麻豆原创 Business Data Cloud:聽Unified master data is only as valuable as the governance applied to it. To ensure data is AI-ready, governance must聽shift聽from regulator to value accelerator. 麻豆原创 Master Data Governance is now a core component of 麻豆原创 Business Data Cloud, governing master data and policies across聽your聽business data fabric.聽This results in聽embedded聽AI governance that accelerates agent deployment, ensuring every agent operates on data聽products聽that聽are聽verified聽and aligned to your business policies.聽
• 麻豆原创 AI Core integration with 麻豆原创 Business Data Cloud: 麻豆原创 is introducing deeper integration between 麻豆原创 Business Data Cloud and 麻豆原创 AI Core, enabling AI models to be grounded directly in trusted business data, semantics, and governance. Batch inference can now be embedded into business-ready data products, continuously enriching the data that powers Joule with predictions, classification, and聽AI聽outputs.聽聽

“This is where 麻豆原创 Business Data Cloud fits into the vision: not as a centralized system, but as an enabler of cultural change through its unique capabilities. These capabilities allow teams to preserve mission-critical business context across financial and non-financial data.”

Jannie Affeld, VP Finance Systems and ERP, Google 

Transform outcomes with Joule Agents 

麻豆原创 is bringing agentic AI directly into the business data fabric through Joule Agents, introducing new capabilities that streamline data management, analytics, and planning through a conversational experience: 

• Data product search and creation: Joule Agents simplify how users discover and create data products. With natural language prompts, users can identify relevant 麻豆原创 and third-party data sources, perform joins and transformations automatically, and apply business context and governance policies.  
• Automated planning and analytical modeling: Joule Agents enable data modelers and planning teams to generate analytical and planning models using AI. By defining dimensions, granularity, and data sources, users can automatically create models aligned with best practices. Teams can also initiate planning cycles, manage versions, and apply calculations without deep technical expertise. 
• Easily聽surface business聽insights:聽Business users聽can聽ask complex analytical questions in natural language and receive context-aware insights across lines of business. Powered by governed data products聽in 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph,聽Joule聽understands relationships, processes, and business logic聽to deliver聽more accurate and complete answers without requiring manual exploration.
• 麻豆原创 Analytics Cloud story generation: Joule accelerates 麻豆原创 Analytics Cloud story creation by transforming data models, queries, and business context into dashboards and visualizations automatically. Users can continue the conversation, drilling into KPIs, identifying drivers, and exploring trends in a single workflow. 

Extend context across your open data ecosystem

Last year, we introduced 麻豆原创 BDC Connect, a capability to share data and metadata with zero copies, preserving meaning across every cloud and platform. We are excited to announce 麻豆原创 BDC Connect for Amazon Athena, continuing our promise of openness and choice.

This enables 麻豆原创 data products to be discovered and consumed directly within AWS without replication or loss of context. As a result, teams can build analytics, applications, and AI agents faster while ensuring they operate on trusted, governed business data.

Together with existing partners across Snowflake, Databricks, Google BigQuery, and Microsoft Fabric, 麻豆原创 Business Data Cloud delivers a connected, open data ecosystem so organizations can extend business context across their entire landscape with zero copies. 

General availability is planned for H2 2026.

“Compute can happen anywhere, data can stay at the source when needed, but business context is managed once, centrally, in 麻豆原创 Business Data Cloud.”

Malin Persson, CIO at Ericsson

Get started today 

Build your trusted foundation for agentic AI with 麻豆原创 Business Data Cloud.  

Irfan Khan is president and chief product officer of 麻豆原创 Data & Analytics.

The acquisition helps customers make their 麻豆原创 and non-麻豆原创 enterprise data AI-ready and will provide customers with the tools they need to unify, cleanse and harmonize data across sources for superior enterprise-wide agentic AI.

Visit the . Get 麻豆原创 news via  and .

Media Contacts:
Aim茅e Leabon, +1 (646) 799-3277, aimee.leabon@sap.com, EST 
Daniel Reinhardt, +49 151 168 10 157,鈥�daniel.reinhardt@sap.com, 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.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.  

Once closed, the acquisition will strengthen 麻豆原创 Business Data Cloud (麻豆原创 BDC)鈥攊ntegral for 麻豆原创’s AI-First and Suite-First strategy鈥攁nd accelerate the evolution of 麻豆原创 BDC to a fully interoperable enterprise data platform for enterprise-wide agentic AI. It will provide customers with the tools they need to unify, cleanse and harmonize data across sources for superior enterprise-wide agentic AI.

“Reltio is a natural fit with 麻豆原创,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering. 鈥淎cquiring them will further improve our position as a leading business AI provider, combining 麻豆原创 and non-麻豆原创 data to deliver data context that business AI requires. AI cannot reach its full potential when data is fragmented across business units, platforms and domains without connection or context.鈥�

By integrating Reltio after closing the acquisition, 麻豆原创 will make customers’ enterprise data fully AI-ready. Customers will be able to rely on trusted, high-quality data across 麻豆原创 and non-麻豆原创 sources that Joule and Joule Agents use to deliver faster time-to-value for business AI.

Reltio鈥檚 platform helps organizations manage and govern structured and unstructured enterprise data from start to finish. Its AI-based entity resolution identifies and merges related records from different formats and applications into one reliable 鈥済olden record鈥� system of context. Its cloud-native, AI-first design supports a single, consistent view of customers, products, suppliers, locations and employees across both 麻豆原创 and non-麻豆原创 applications. Customers running AI tasks will benefit from increased reliability and consistency of data, bundled in a single source of truth, improving business AI. With that, customers can trust that AI results are correct, and AI-interactions are resolved fast.

鈥淛oining forces with 麻豆原创 presents a tremendous opportunity for us to accelerate our mission,鈥� Reltio Founder and CEO Manish Sood said. 鈥淓nterprise AI needs trusted context that is open and interoperable across the heterogeneous IT landscapes our customers run. This combination accelerates our ability to deliver Reltio as the system of context across 麻豆原创 and non-麻豆原创 environments, while maintaining continuity for our customers and our partner ecosystem.鈥�

Reltio’s data cleansing, unification capabilities and agent-driven workflows will work alongside 麻豆原创 Business Suite applications to improve decisions, reduce integration complexity and deliver trusted, consistent data critical for successful business processes and AI use cases. Low latency delivery and support for the Model Context Protocol (MCP) enable real-time, multiagent workflows across 麻豆原创 and non-麻豆原创 environments, allowing AI agents, such as a procurement agent, to assess supplier risk and trigger actions almost instantly using trusted, real-time data. Reltio offers prebuilt, industry-specific 鈥渧elocity packs鈥� that include data models, rules, matching logic and integrations, and solutions tailored to sectors like life sciences, healthcare and financial services.

By integrating Reltio after closing the acquisition, 麻豆原创 intends to accelerate its customers’ ability to govern and expose master data as trusted and context-rich data products across multiple sources that serve both traditional analytics workloads and AI agents. Reltio will become a core capability within 麻豆原创 BDC, with a flexible commercial model where customers can purchase Reltio as a separate solution or with other 麻豆原创 products. The Reltio portfolio will also remain available as a standalone offering for the foreseeable future.

The transaction is expected to close in Q2 or Q3 of 2026, subject to customary closing conditions, including regulatory approvals.

Visit the . Get 麻豆原创 news via  and .

About Reltio

Reltio is a leader in data unification and management, delivering cloud-native, AI-native master data management (MDM) to help enterprises create trusted data and unlock context intelligence for analytics, automation, and agentic AI. Designed for complex, multi-vendor environments, Reltio helps organizations unify, cleanse, harmonize, govern, and activate core data from multiple sources in real time鈥攁cross 麻豆原创 and non-麻豆原创 systems. The Reltio Data Cloud uses advanced entity resolution, continuous data quality, and relationship intelligence within an intelligent data graph to connect data across systems and reveal the full context behind customers, products, suppliers, and other key business entities. This enables organizations to reduce data friction, improve operational execution, and accelerate time to trusted decisions. For more information, visit .

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:
Marcus Winkler, 麻豆原创, +49 151 571 18 691, marcus.winkler@sap.com, CET
Aim茅e Leabon, 麻豆原创, +1 (212) 653-9600, aimee.leabon@sap.com, EST
Daniel Reinhardt, 麻豆原创, +49 151 168 10 157, daniel.reinhardt@sap.com, CET
Ilaina Jonas, 麻豆原创, +1 (646) 923-2834, ilaina.jonas@sap.com, EST
麻豆原创 麻豆原创 Room;听press@sap.com
Kevin Keenan, Reltio, +1 (987) 844-6203, kevin.keenan@reltio.com, PST

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.