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.

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

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At 麻豆原创 TechEd in 2026, 麻豆原创 announced a series of innovations in Joule Work, Joule Studio, and 麻豆原创 Business AI Platform.

From building and extending AI agents to connecting business data and governing AI experiences, see how 麻豆原创 helps customers move towards the Autonomous Enterprise. Hear 麻豆原创 leaders and industry voices share what鈥檚 new, their perspectives, and more.

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

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

From vision to execution

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

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

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Designed for better business outcomes

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

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

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

Joule Work

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

麻豆原创 Business AI Platform

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

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

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

Physical AI by 麻豆原创

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

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

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

Joule Studio: Extend, build, and integrate your agents

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

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

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

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

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

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

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

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

麻豆原创 Business Data Cloud

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

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

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

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

Governance

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

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

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

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

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

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

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

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

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

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

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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Space is unforgiving. Vacuum, radiation, orbital debris, and solar storms can produce mission-critical crises at any moment. Adding to the complexity, onboard sensors and mission system telemetry produce too much time-sensitive data to be routed back to Earth for analysis. Latency has to be reduced to ensure the safety of the spacecraft. 

Enter 麻豆原创’s tabular AI foundation model, , which makes predictions directly from structured data鈥攅ven with some data missing鈥攁nd without requiring the usual model training or fine-tuning. Running directly onboard the ISS on HPE Spaceborne Computer-2, it supports anomaly triage in real-time telemetry data, predicts issues before failures escalate, reconstructs missing sensor values based on historical data, and generates explainable mission-health reports for astronauts and ground teams.

“This is proof that tabular AI on 麻豆原创 works at the edge of possibility,” said Mayank Shrivastava, vice president of Generative AI and Foundation Models at 麻豆原创 SE. “Space is the ultimate stress test for AI. If a foundation model can predict mission-critical failures in orbit, under radiation, energy, and connectivity constraints, imagine what it can do for a global enterprise. This is the same model that will predict supplier risk, cash flow anomalies, and equipment failures across your enterprise. What works in orbit works in your supply chain.” 

Imagine this: in orbit, data arrives in fragments, connectivity comes and goes, and you cannot send a technician when something starts to fail. You have to anticipate problems with a variety of signals. Enterprises face the same challenge on the ground: a supplier whose deliveries are quietly slipping, a payment pattern that鈥檚 drifting, a compressor that鈥檚 running hot. Predictive models that are able to spot early warning signs in a satellite are also built to find those same signals in your business data, so you can act before small problems become critical ones.

How 麻豆原创 strengthens business functions with models for structured data 

麻豆原创-RPT is part of 麻豆原创’s portfolio of tabular foundation models, or TFMs, that includes the model by Prior Labs, and , which orchestrates, manages, and governs TFMs, predictions, and workflows. Large language models (LLMs) excel at language and reasoning, but TFMs are built for the structured tabular data that underpin real-world operations, such as the sensor-driven data used in this project.  

麻豆原创-RPT is about 50,000 times more energy-efficient than state-of-the-art LLMs. This is especially important in space, where every watt is constrained. 麻豆原创-RPT is also highly adaptable. It requires no pre-training as conditions evolve. 

Outcomes that will be delivered onboard include real-time predictions for computer and mission health status, an overall mission risk score, an assessment of whether manual intervention is needed, human-readable explanations of AI-driven findings, and ad hoc reports for crew and ground teams. 

鈥淗PE Spaceborne Computer-2 continually demonstrates the advantages of having a high-performance compute solution aboard the ISS, supporting advanced technologies and on-going research in space鈥� said Scott Wiest, Vice President and Global Sales Chief Technologist, HPE Fellow. 鈥淒eploying 麻豆原创鈥檚 tabular AI on HPE Spaceborne Computer-2 shows what鈥檚 possible with real-time detection and prediction using AI in the ultimate edge environment.鈥� 

From orbit to enterprise 

The deployment of 麻豆原创-RPT on HPE鈥檚 Spaceborne Computer-2 on the ISS, by early 2027, will show how it strengthens business functions on earth. The constraints of space, including extreme energy limits, intermittent connectivity, radiation interference, and zero tolerance for failure are relevant to challenging use cases on earth. These include disaster relief operations, agriculture in remote areas, distributed manufacturing, and spread-out supply chains.  

Every insight aboard the ISS accelerates 麻豆原创’s road map for enterprise edge AI and key use cases that leverage structured data for the most important analytical and operational processes, even in the most challenging settings.  

Looking ahead, the mission lays the groundwork to support space-based data centers, deep space exploration, and other collaborations on space missions. Our long-term vision for space-based compute infrastructure is anchored in proven, efficient, and accurate AI. 

Explore how to deliver tabular AI on 麻豆原创, learn more about the specialized capabilities behind it, and try the 麻豆原创-RPT AI model with your own data on the . 

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

AI has gone from insight to action and now, with physical AI, it comes into the physical world.

Enterprises have made substantial investments in both AI and physical AI. AI helps business systems deliver remarkable insights and recommendations on financials, inventory, and supply chains. Physical AI creates value every day with physical systems鈥攕uch as industrial robots, humanoids, sensors, or robot dogs鈥攄eployed across production lines, warehouses, field service teams, and logistics networks.

As automation, robotics, and sensing technologies have matured across industries, the volume and richness of the physical signals coming from physical execution has grown substantially; physical operations today generate continuous intelligence about factors such as asset health, task completion, quality, throughput, and exceptions.

Today, however, there is a gap between what the business knows and the intelligence generated by physical operations on the ground. This gap is one of the most common sources of operational friction across industries. It contributes to delayed responses, ill-informed decisions, and physical operations that never enter the business record. As a result, revenue and margins decrease while risk exposure increases.

From enterprise intelligence to physical action

This year at 麻豆原创 TechEd, 麻豆原创 revealed how our physical AI removes this operational friction: the system that runs your business is the same system that controls your physical operations.

The enterprise knowledge in your business system triggers the physical actions; physical signals arrive with the business context already attached;聽physical operations in the field flow back into 麻豆原创 as structured data intelligence. Every physical action leaves a traceable, auditable record tied to the relevant business process.

The full context of your business is behind every physical action; verified outcomes are brought back into 麻豆原创 in a secure and governed way for follow-up actions.

When the system that runs your business is also the system that governs how, when, and why physical tasks are executed, the result is a fundamentally different kind of operational capability. For enterprises already running 麻豆原创, this means physical execution inherits the intelligence, context, and governance already in place.

This is how physical AI by 麻豆原创 transcends automation and extends the Autonomous Enterprise into the real world.

“Foxconn’s vision for intelligent manufacturing requires physical systems that understand business context, not just motion commands,” said Dr. Wang, head of Physical AI at Foxconn. “With physical AI by 麻豆原创, warehouse tasks from 麻豆原创 Extended Warehouse Management directly orchestrate our humanoid robots and autonomous transport systems. Asset inspections triggered by maintenance plans flow to our robotic platforms and return verified outcomes back to 麻豆原创. This closed loop between enterprise systems and physical execution is what sets this collaboration apart: the robot knows why it acts, and the business system knows what happened.”

How physical AI by 麻豆原创 capabilities are designed to transcend automation

1. Business-driven physical action

Physical AI is built to ground every physical execution in the business context: order priorities, safety rules, inventory status, and compliance constraints. Every action has a clear reason tied to business outcomes, and the business system remains in control of what gets executed, when, and why. The result is physical execution that is intelligent from the start, not just automated.

2. Physical evidence into digital workflows

Every physical task creates a complete audit trail and physical observations flow back into 麻豆原创 automatically, transforming field outcomes into business insights rather than isolated records in a separate system. Physical world events become inputs to the decisions and processes that follow.

3. Enterprise-governed execution

Built on the 麻豆原创 Business AI Platform, physical AI capabilities are designed to provide traceability, auditability, and human-in-the-loop control as first principles. Physical world data flows into 麻豆原创 Business Data Cloud as new structured data products, enriching planning, predictions, and operational strategy over time.

4. Vendor and hardware agnostic

Working across device platforms, physical systems, and AI models of the customer’s choosing, customers retain freedom over their physical layer. 麻豆原创 provides the business context, orchestration, and governance. Partners provide the physical execution.

Physical AI at work with customers and partners

础谤莽别濒颈办-尝骋: physical AI executes and records inbound storage tasks

础谤莽别濒颈办-尝骋 is a joint venture between Turkish durable consumer, Ar莽elik and South Korea鈥檚 LG Electronics, manufacturing air conditioners. Physical AI by 麻豆原创 enables a humanoid robot to execute and record inbound storage tasks. This has addressed the costly and persistent issue of incorrect storage placements in inbound logistics with potential savings of millions of euros.

• Associated 麻豆原创 solutions: 麻豆原创 Extended Warehouse Management
• Physical AI ecosystem partners:
• Video:

Intel: physical AI in autonomous warehouse operations

Intel is one of the world’s largest semiconductor manufacturers, operating a vast global warehousing and logistics network and serves as both a聽 proof-of-concept environment and a technology collaborator. By integrating 麻豆原创鈥檚 enterprise workflows and warehouse processes with Intel-powered edge AI and robotics technologies, warehouse transport tasks generated in 麻豆原创 are automatically translated into robot missions and executed autonomously in the warehouse.

麻豆原创 provides the business context, workflow governance, and process orchestration, ensuring that every physical action aligns enterprise priorities and operational goals. Intel provides the physical AI hardware and software foundation at the edge, delivering the compute AI and real-time robotics capabilities that enable autonomous systems to perceive, decide, and act in dynamic environments. The combined capabilities of 麻豆原创 and Intel create a closed-loop system; business decisions become physical actions and operational outcomes are continuously fed back into business systems.

In a proof of concept demonstrated at Intel鈥檚 Chengdu site in China, material movement is automated end-to-end, reducing both transport distances and repetitive manual handling. Physical AI capabilities from 麻豆原创 and Intel demonstrate how enterprise decisions can directly drive physical execution in logistics workflows.

• Associated 麻豆原创 systems: 麻豆原创 Extended Warehouse Management
• Physical AI ecosystem partners:

Holcim: industrial asset inspections

Holcim is a Swiss multinational building materials and sustainable construction company, operating in heavy industry. The company has significant infrastructure assets with inspection, maintenance, and safety use cases. Since attending the Embodied AI Jam held in August 2026, Holcim has implemented physical AI capabilities for asset inspections with industrial drones and quadruped dogs.

On-site quadruped dogs inspect conveyor belts in dark, dusty areas and the status of kilns in extreme temperatures. Drones inspect assets in heights difficult for humans to reach. And end-to-end automation starts and finishes in a 麻豆原创 system. In 麻豆原创鈥檚 asset management solution, the maintenance plan triggers an action for the industrial drone/quadruped dog; once the action is executed, a maintenance notification is created back into 麻豆原创.

• Associated 麻豆原创 systems: 麻豆原创 Plant Maintenance
• Physical AI ecosystem partners: , ,
• Video:

Start your physical AI journey today

From warehouse operations to asset maintenance, from safety inspections to keeping your operations compliant, physical AI capabilities from 麻豆原创 have the potential to transform your business. It connects the intelligence of your enterprise with real-world execution; the 麻豆原创 system that you trust to run your business is the same system that informs the actions of your physical systems, be it a robot, an industrial drone, or a robodog.

As a part of Industry AI, you can begin your physical AI journey today with warehouse operations and asset inspection use cases or a physical AI use case, tailor-made to your requirements. Contact 麻豆原创 via email for more information.

Lukasz Ostrowski is global head of Physical AI and Robotics at 麻豆原创.

The story began in 1996, when around 700 developers and technical experts gathered in Orlando, Florida, to explore a new generation of enterprise software.

鈥淚t wasn鈥檛 called ‘TechEd’ back then,鈥� explained Karl Kessler, vice president of Product Management for ABAP AI at 麻豆原创, who attended the Orlando event as a product manager for 麻豆原创 Technology. 鈥淚t was originally called the 麻豆原创 Technical Developers Conference.鈥�

The event came at a time of technological change. 麻豆原创 was opening up new ways to make 麻豆原创 R/3 business processes accessible over the internet. BAPI programming interface provided access to 麻豆原创 business logic through defined interfaces, while ALE made it easier to exchange messages between distributed systems. 麻豆原创 was also working on a 鈥渂usiness framework鈥� designed to integrate distributed business processes.

This raised a question that seems obvious today: how could enterprise software evolve into a platform for an increasingly connected IT landscape?

From technical training to hands-on experience

The event gave developers an opportunity to explore these new possibilities. But its format was still a far cry from the multifaceted platform for knowledge sharing, experimentation, and community that 麻豆原创 TechEd would become over the following three decades.

鈥淚n the beginning, it was all very academic,鈥� Kessler recalled. 鈥淭here were no keynotes or product demos on big stages. We went straight into an introduction to the architecture of 麻豆原创 R/3, and the program consisted mainly of lectures: less conference spectacle, more technical classroom.鈥�

The second event, held in Los Angeles later in 1996, offered a glimpse of how quickly the 麻豆原创 ecosystem was evolving. 麻豆原创 and Microsoft showcased new possibilities involving Internet Explorer, SQL Server, and Microsoft Office 97. Hands-on sessions were introduced for the first time, giving participants the opportunity to work with 麻豆原创 software themselves.

鈥淎nd 鈥榟ands-on鈥� really meant getting your hands dirty back then,鈥� Kessler emphasized. 鈥淧articipants had to install the software on their own laptops. Ready-to-use images that you can deploy in just a few clicks today? Not a chance.鈥�

For the first time, participants had access to a software environment where they could experiment for themselves鈥攕omething that has since become a staple of 麻豆原创 TechEd.

Where technology meets community

In the years that followed, this shift continued. With the introduction of 麻豆原创 NetWeaver, technologies like Java, portals, the web, and XML became increasingly prominent alongside the traditional ABAP world. 麻豆原创 TechEd was evolving from an event where technology was explained into a place where developers could try it out and discuss it for themselves.

The community, too, began to play a bigger role. The 麻豆原创 Developer Network created new opportunities for exchange: knowledge was no longer shared only from the stage, but also between sessions, online, and across company boundaries.

Kessler witnessed this evolution firsthand. From 1996 until the onset of the pandemic, he attended every 麻豆原创 TechEd event in the U.S. and Europe. He initially took part as a product manager and speaker, later helping shape the program as a track lead and, for several years, an agenda lead.

Throughout the 2000s, 麻豆原创 TechEd increasingly became a community experience. Kessler has particularly vivid memories of the events in Las Vegas: 鈥淒uring the day, there were lectures and four-hour hands-on sessions. But the day didn鈥檛 end when the official program did. People went out for dinner together, saw shows, or met up at the hotel in the evening. Many of us were there for practically a whole week. And if we had time afterward, we might take a trip to the Grand Canyon or Death Valley.鈥�

By then, 麻豆原创 TechEd had become much more than a conference program. Alongside the technical content, it was the encounters, conversations, and shared experiences that helped shape the community.

New formats emerged from around 2000 onward, further strengthening that community spirit. Demo Jam gave developers a stage to showcase what they had built. The Community Clubhouse created space for conversations beyond traditional presentations. Roadmap sessions offered a look at the 麻豆原创 technologies to come, while hands-on sessions turned attendees from listeners into active participants.

鈥淥ver time, exchanging ideas became at least as important as the technology itself,鈥� Kessler recalled. 鈥淭he more complex the topics became, the more important it was to share experiences, try things out together, and learn from one another.鈥�

Technology was no longer simply presented. It was explored, discussed, and developed further–together.

When the future becomes the present

Throughout the 2000s, the technological foundations of enterprise software continued to evolve. Initially, the focus had been on connecting 麻豆原创 systems with other applications through open interfaces and web technologies. Increasingly, however, a different question came to the fore: what if vast amounts of data could be made available almost in real time, without having to move and process them so extensively first?

Around 2010, 麻豆原创 TechEd brought this idea to life. Vishal Sikka introduced 麻豆原创 HANA and in-memory computing, an approach that processes data directly in main memory. It challenged established assumptions about database performance. Initially focused on analyzing large volumes of data, 麻豆原创 HANA later became a foundation for applications as well.

For 麻豆原创 TechEd, this marked another step forward. The event was no longer just about learning new technologies; it was also about exploring their potential and understanding what they could mean in practice. An idea that had once sounded like science fiction became the starting point for new applications and business processes.

鈥淚t was the transition from 鈥榃hat if?鈥� to 鈥楾his is how we do it,鈥欌�� Kessler said.

Meanwhile, the community continued to evolve. The 麻豆原创 Mentor program gave particularly engaged community members a new role as sounding boards, ambassadors, and representatives of the wider community. Technology and people were evolving together.

鈥淣ot every prediction from back then came true exactly as expected,鈥� Kessler reflected. 鈥淪ome ideas disappeared, while others developed in completely different ways. But time and again, concepts that had sounded futuristic at 麻豆原创 TechEd went on to become part of everyday working life.鈥�

With 麻豆原创 Fiori, 麻豆原创 S/4HANA, and the cloud, new topics arrived at 麻豆原创 TechEd that would go on to shape the 麻豆原创 landscape. Developers could experience and discuss how enterprise software was changing鈥攁nd what those changes meant for their own work.

The focus was no longer just on what a system could do technically. Increasingly, the questions were about how enterprise software should be used: on which devices, through which interfaces, with what data, and with how much flexibility.

The future was becoming more tangible. It was no longer confined to architecture diagrams and technical concepts; it was visible on screens, in mobile applications, and in cloud environments.

At 麻豆原创 TechEd, these changes were not just presented but explored and developed further together. What once opened up new possibilities became an integral part of the 麻豆原创 landscape.

The next future Is already taking shape

Over the years, 麻豆原创 TechEd continually found new ways to bring technological developments to life and bring the 麻豆原创 community together. New formats, greater interaction, and hands-on experiences had all helped shape the event. The community had long since become much more than an audience: it was an essential part of what made 麻豆原创 TechEd what it was.

Then came 2020. 鈥淭he global pandemic suddenly took away so much of what we had come to take for granted,鈥� Kessler said. 鈥溌槎乖� TechEd still took place, but it went digital. There were no exhibition halls where people could meet, no spontaneous reunions or conversations in the corridors, no discussions after a session that unexpectedly sparked a new idea.鈥�

Hands-on experiences and exchanges with others had to take place under entirely different conditions. Something that had grown over many years could not simply be replicated online, one-to-one. 鈥淭hat鈥檚 when we realized just how much 麻豆原创 TechEd depends on personal interaction,鈥� Kessler recalled.

One of Kessler鈥檚 most memorable experiences shows how important that interaction was, even behind the scenes: the 鈥渟peaker ready room.鈥� Every year, around 130 speakers gathered in a large ballroom during 麻豆原创 TechEd week to prepare for their sessions and help one another out. If they ran into problems with connectivity, licenses, updates, or integrating different components, they worked together to find solutions鈥攐ften on the spot and under time pressure.

Over the years, this gave rise to a speaker community made up of familiar faces and newcomers who worked alongside 麻豆原创 IT and the event organizers to make 麻豆原创 TechEd happen. For Kessler, the ready room embodies the event鈥檚 distinctive sense of teamwork. Everyone wanted the sessions to succeed and participants to have the best possible experience. The shared mindset was simple: we鈥檒l make it work鈥攖ogether.

In 2026, 麻豆原创 TechEd finds itself at another technological turning point. Artificial intelligence is changing how software is developed, used and increasingly executed. Developers are working with AI-powered tools. Applications can take on tasks. Agents can execute processes and interact with one another.

鈥淲e have no more idea today of what will be commonplace 10 years from now than developers in 1996 could have known which of their futuristic ideas would eventually become standard practice,鈥� Kessler said. 鈥淏ut that鈥檚 exactly the point. The future looked different at every 麻豆原创 TechEd, yet the questions behind it have remained much the same: how can technology better support people? How can systems work together? How can we make complex processes simpler? What becomes possible when technical boundaries are pushed?鈥�

So, what connects the old hands attending their 30th 麻豆原创 TechEd with the young talents joining for the first time in 2026?

Kessler鈥檚 answer is simple: 鈥淲e all want to know what comes next鈥攁nd discover together what鈥檚 possible.鈥�

This month at , we announced a wave of 麻豆原创 Business AI innovations all built on the same technology foundation that powers our that we are now delivering to our customers and partners, allowing them to add even more value in the future.

We showed how the future of enterprise software is built on an AI-native architecture, powered by 麻豆原创 app, data, and AI foundation. With this approach, we are enabling a platform shift across the tech stack in a non-disruptive fashion, empowering developers to work faster and smarter using the frameworks and tools of their choice.

麻豆原创 HANA Cloud and 麻豆原创 Business Data Cloud: powering our AI-native future

麻豆原创 HANA Cloud is the database for 麻豆原创鈥檚 AI-native software architecture and the foundation of our broader data fabric strategy. At 麻豆原创 TechEd, we announced new AI capabilities for 麻豆原创 HANA Cloud that spur AI innovation.  

For example, Model Context Protocol (MCP) support for 麻豆原创 HANA Cloud is now generally available. This provides direct access to rich multi-model engines. Agents can be grounded in full enterprise data context: navigating relationships across customers and suppliers, understanding geographic dependencies through spatial data, and performing semantic searches through vector embeddings — all within a single in-memory engine.  

We鈥檙e also expanding 麻豆原创 HANA Cloud knowledge graph engine capabilities (Q1 2026) so customers can automatically generate knowledge graphs from 麻豆原创 HANA Cloud metadata. What used to take weeks of manual modeling can now happen automatically in minutes. But that鈥檚 not all. We鈥檙e also enabling agentic memory in 麻豆原创 HANA Cloud. With long-term memory, AI agents can memorize past inputs and decisions — learning and remembering just like humans — and become continuously smarter.

These advances show that 麻豆原创 HANA Cloud is truly powering an AI-native future. .

Bringing together the power of 麻豆原创 BDC and Snowflake

We are bringing the power of Snowflake together with 麻豆原创 Business Data Cloud (麻豆原创 BDC), calling it 麻豆原创 Snowflake. This partnership enables zero copy data sharing with Snowflake via 麻豆原创 BDC Connect.

Enterprises already using Snowflake today can leverage 麻豆原创 BDC Connect to integrate their existing instances of Snowflake with 麻豆原创 BDC, giving them seamless, real-time access to combined, semantically rich 麻豆原创 with non-麻豆原创 data in 麻豆原创 BDC. 麻豆原创 Snowflake will be made generally available in Q1 2026, and 麻豆原创 BDC Connect for Snowflake in H1 2026. Find more information here.

麻豆原创-RPT-1: a new category of AI models

One of our most exciting announcements at 麻豆原创 TechEd was the launch of our first enterprise relational foundation model 麻豆原创-RPT-1, pronounced: 鈥渞apid one.鈥�

Businesses run on structured data. But large language models (LLMs) struggle with a general understanding of table structures and associated semantics. This requires the use of machine learning, or 鈥渘arrow AI,鈥� for tasks like classification, regression, and more. But classical machine learning necessitates training a model on each task, which easily can lead to hundreds of separate models.

麻豆原创-RPT-1 puts them all into one single, pre-trained model that understands relational business data and predicts business outcomes. Unlike language, image, or video models, 麻豆原创-RPT-1 accurately predicts business based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk, and more.

We believe that 麻豆原创-RPT-1 is a super capable foundation model today. It provides up to 2x better prediction quality compared to narrow models and 3.5x better prediction quality as compared to LLMs. .

麻豆原创-RPT-1 comes in three versions. 麻豆原创-RPT-1-small is for super-fast predictions and 麻豆原创-RPT-1-large is for highest accuracy. Both will be generally available in Q4 2025 in the generative AI hub in AI Foundation. 麻豆原创-RPT-1-OSS is the open-source version, available in Hugging Face and GitHub.

You can test 麻豆原创-RPT-1 today with your data or our use case data samples via no-code UI or via API in the new 麻豆原创-RPT-1 playground, an intuitive and interactive space to test for free and open to everyone and .

We are continuously adding new capabilities to AI Foundation and models to the generative AI hub, empowering developers to experiment with orchestration tools and leading models to scale AI development and productization across 麻豆原创 and non-麻豆原创 environments. For example, Perplexity is now generally available in the generative AI hub, so users can correlate business data with external data from the internet. Evaluation Services and Prompt Optimizer, in close collaboration with NotDiamond, are now also generally available in AI Foundation, freeing up users to adopt the most appropriate model for their use cases without the need for rewriting prompts. .

Digital sovereignty made in Germany, for Europe

Digital sovereignty is becoming increasingly important, reflecting the need for regional AI services that align with local regulations, standards, and values. As an example, Europe will benefit from its own strong, trustworthy infrastructure to support innovation, data protection, and ethical AI.

AI Foundation, including various models and all the services we offer, is already available on our own cloud infrastructure. As a next step, we are expanding our 麻豆原创 Cloud Infrastructure offering in our 麻豆原创 data center in Walldorf, Germany, to Deutsche Telekom through the Industrial AI Cloud project, providing secure, high-performance infrastructure for AI innovations across public institutions, defense, and society. 麻豆原创 delivers 麻豆原创 Cloud Infrastructure, 麻豆原创 Business Technology Platform, and applications 鈥� including our AI Foundation with frontier AI from Mistral, Cohere, and others 鈥� on Telekom鈥檚 Munich data center. Both companies uphold the highest standards of data protection, security, and reliability.

This marks a milestone as more European companies join the Industrial AI Cloud project, advancing applied AI across Europe with trusted, business-embedded solutions that unlock the full potential of industry data. See the announcement here.

Enabling customers to build, extend, share, and orchestrate AI agents

To help manage Joule Agents and Joule skills, we have introduced the concept of AI Assistants 鈥� role-based AI teammates, accessed through Joule 鈥� like a financial assistant that brings together agents for cash collection, treasury, and more. We will provide AI Assistants in Joule for every core business role, offering our users an agentic experience like never before.

Out-of-the-box Joule Agents are powerful, but we know that every company has unique requirements. We believe AI should adapt to users鈥� systems, not the other way around, so we are enabling them to use Joule Studio to extend 麻豆原创鈥檚 pre-built agents with custom fields, tools, and reasoning logic while retaining all the deeply grounded integration capabilities 麻豆原创 provides. Joule Studio also provides low-code tools to build custom agents that integrate with all other Joule Agents, Joule skills, and 麻豆原创 BDC.

Using a low-code approach, users can build Joule Agents visually with natural language and drag-and-drop. But we also want to meet the needs of developers who want ultimate flexibility. Our pro-code approach gives developers the freedom to build agents using the agentic framework of their choice 鈥� for example, LangGraph, CrewAI, Google鈥檚 Agent Development Kit, and more. 麻豆原创 Cloud SDK for AI now supports agentic development, ensuring these pro-code agents can be seamlessly integrated and giving developers the best of both worlds: deep integration and full flexibility.

No matter how you want to build agents, an important question is how to integrate them into the larger ecosystem beyond 麻豆原创. We鈥檙e making Joule Agents fully compatible with the agent-to-agent (A2A) protocol soon, so agents can discover and collaborate with each other.

A2A exposes rich semantics describing an agent鈥檚 capabilities, allowing both 麻豆原创 and third-party agents to work together seamlessly. We are collaborating with partners 鈥� AWS, Google, Microsoft, ServiceNow, and more 鈥� to standardize this protocol for full interoperability. This capability will allow Joule to orchestrate tasks across multiple agents, both 麻豆原创 and non-麻豆原创, increasing automation and productivity across the enterprise. Read more here.

To manage and govern agents across the enterprise, is now generally available, providing centralized control of 麻豆原创 and non-麻豆原创 agents. In addition, is available now for tracing agent actions, benchmarking against KPIs, and identifying bottlenecks or opportunities for agents to further improve business.

No 麻豆原创 TechEd without ABAP news

The ABAP journey continues with 麻豆原创-ABAP-1, which will be available in the generative AI hub in Q4 2025. Trained on ABAP code, it is designed to build ABAP AI use cases, enabling developers to build smarter, custom AI solutions in modern ABAP code. .

In addition, ABAP Cloud development is coming to Visual Studio (VS) Code. The new ABAP Cloud extension for VS Code delivers a streamlined, file-based development experience with built-in AI assistance. Powered by an ABAP language server, it will initially support 麻豆原创 Fiori UI service development and expand to additional ABAP Cloud scenarios over time. This brings ABAP development into the same environment where developers already build with UI5 and CAP. General availability is planned for Q2 2026. .

What鈥檚 next: embodied AI and quantum

麻豆原创 TechEd is always an opportunity to look to the future. This year, that future includes not just humans, but also autonomous devices, including humanoid robots.

By integrating Joule Agents natively with robots, 麻豆原创 is bringing business logic into the physical world, enabling a wide range of autonomous devices to operate with enterprise context. We highlighted our strategic partnerships with robotics companies and system integrators to serve customers like Sartorius, Bitzer, and Matur Fompak, demonstrating how our expanding physical AI ecosystem enables robots to understand business processes and execute complex tasks autonomously.

Early proof-of-concept deployments show Joule successfully integrated with 麻豆原创 business applications and autonomous systems across asset performance, logistics, field services, and warehouse operations. While still in the pioneering stage, these implementations illustrate how 麻豆原创 is extending Joule to serve both human users and autonomous devices, shaping the future of enterprise AI.

Read more about the partnerships and implementations here.

AI is a new compute paradigm that changes everything. But there is another compute paradigm on the horizon: quantum computing. It鈥檚 early days, but 麻豆原创 is driving the future of enterprise computing with a vision to help businesses get ready for quantum computing.

麻豆原创 is not building quantum hardware; instead, we are focusing on creating quantum algorithms for business applications. These solutions are simple to deploy 鈥� on when needed, off when not 鈥� and are designed to be hardware-agnostic, collaborating with partners such as IBM to ensure seamless integration without re-platforming. This approach will enable organizations to unlock operational efficiency and drive better business results at enterprise scale.

I couldn鈥檛 be more excited about what鈥檚 next for our customers鈥� future as we bring 麻豆原创鈥檚 AI-native architecture to life.

Philipp Herzig is CTO of 麻豆原创.

We鈥檙e entering a new era, according to 麻豆原创 Executive Board Member Muhammad Alam, in charge of 麻豆原创 Product & Engineering: 鈥渢he era of agentic AI, where AI moves from being just a tool to becoming your trusted teammate.”

Joined on stage by 麻豆原创 CTO Philipp Herzig and Michael Ameling, president of 麻豆原创 Business Technology Platform, Alam set the stage during the kick-off keynote, saying: 鈥淎s developers, you are not on the sidelines of this AI revolution. You are the revolution. And we are here to supercharge you for what鈥檚 next.鈥�

Supercharging developers

Together they dismissed suggestions that AI will replace developers. 鈥淭he truth is that developers aren’t going away,鈥� Alam said. 鈥淭hey are getting supercharged. They’re becoming the architects of smart connected businesses. So, the real question isn’t if we need developers; it’s how fast can we empower them to thrive and lead in this AI-native era?鈥�

Ameling, Alam, and Herzig outlined 麻豆原创’s vision and highlighted the integration of AI, data, and intelligent agents to transform business processes and drive innovation. They also emphasized the importance of a unified data fabric and the deployment of advanced AI models and agentic technologies within the 麻豆原创 ecosystem.

In this new business environment, they noted that every enterprise is becoming a data company and every user experience鈥攆rom the front line to the boardroom鈥攊s becoming AI-driven.

鈥淎s developers, you don鈥檛 just code anymore,鈥� Alam said. 鈥淵ou also design intelligent workflows and supervise AI agents to shape real business outcomes.鈥� To help developers do this, 麻豆原创鈥檚 strategy centers on empowering them with applications, connecting them with data, and supercharging them with AI.

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麻豆原创 BTP is the foundation for AI agents

All this runs on 麻豆原创 Business Technology Platform (麻豆原创 BTP), which serves as the foundation for building and managing AI agents鈥攂oth 麻豆原创 built and custom developed. While 麻豆原创 BTP is the engine, the Business Transformation Management portfolio is the navigator, ensuring technology translates into real business impact. It aligns strategy, process, and people, turning AI-driven potential into sustainable transformation at scale.

Alam identified three broad themes during the keynote:

First, he said 麻豆原创 is continuing to make 麻豆原创 more open. As part of that, he announced 麻豆原创 Snowflake, which brings the full data and AI capabilities of Snowflake as a solution extension to 麻豆原创 Business Data Cloud (麻豆原创 BDC). This partnership is exactly what joint customers have been asking for, Alam shared.

Second, he said 麻豆原创 will provide developers with the most context-rich agentic platform, including the most deeply grounded set of ready-to-use agents that developers can customize to their needs. 鈥淭oday, you鈥檒l hear about creating custom Joule Agents using low-code and pro-code tools with agent builder in Joule Studio as part of 麻豆原创 Build鈥� Alam said.

鈥淲e鈥檙e also standardizing AI agent interoperability with the agent-to-agent protocol, allowing your agents to collaborate securely across ecosystems,鈥� he added.

Third, he announced 麻豆原创鈥檚 first foundation model built specifically for structured business data: 麻豆原创 RPT-1, pronounced 鈥溌槎乖� Rapid One.鈥� 鈥淥ur relational pretrained transformer delivers enterprise-grade accuracy and scale, outperforming both LLMs and AutoML for tabular AI, which is critical for building reliable high-value agents,鈥� Alam said.

鈥淚n short, we鈥檙e embracing an open ecosystem, supercharging agents with deep process and data context, and giving you the tools to amplify AI and agents,鈥� he said. As the keynote continued, Ameling and Herzig detailed these and other innovations, explaining how they will help developers work smarter and achieve more.

麻豆原创 BDC and Snowflake

Ameling expanded on the new partnership with Snowflake, which will bring Snowflake鈥檚 fully managed data and AI capabilities to 麻豆原创 customers. Together with the introduction of 麻豆原创 BDC Connect for Snowflake, he said this will result in cost savings and simplified data landscapes. 鈥淭his enables you to integrate 麻豆原创 and non-麻豆原创 data products seamlessly between 麻豆原创 BDC and Snowflake, so that you can deploy intelligent applications faster and share across your preferred data marketplace.鈥�

Ameling also positioned 麻豆原创 HANA Cloud as 鈥渢he database AI was looking for.鈥� With 麻豆原创 HANA Cloud and 麻豆原创 BDC, 麻豆原创 provides the best business data fabric to help address these challenges, he said. 鈥淲ithout 麻豆原创 HANA Cloud, you would have one database for each and every data representation, which leads to disaggregated data siloes that limit your AI potential. With 麻豆原创 HANA Cloud, we have all these powerful engines in one integrated, multi-model database.鈥�

Building on this, Herzig emphasized that every business requires a strong data foundation because 鈥淎I is nothing without well-organized data…On top of the data foundation sits our AI Foundation that allows you to not only use the latest frontier AI technologies out there in the market, but also to extend 麻豆原创鈥檚 out-of-the-box AI capabilities and build your own experiences deeply contextualized in your business processes and data.鈥�

麻豆原创 pioneers a tabular foundation model

Herzig then explained that 麻豆原创 RPT-1 was designed to address a crucial problem for developers and enable them to deliver much better predictive capabilities, enterprise-grade accuracy, and scale to business customers.

Until now, Herzig said, 鈥淲e still had to go back to good old machine learning 鈥o train what we call 鈥榥arrow鈥� AI models that are specifically made for each [business] task. Therefore, you really had to train a hell of a lot of models.鈥� For example, to solve 10 predictive tasks across 10 different entities like company codes or plants would require training 100 different models.

鈥淲hat we really want to do is get rid of all these models and just introduce one giant model that only requires a small amount of data to learn from,鈥� he said. That鈥檚 what 麻豆原创 RPT-1 is. 鈥淲e believe 麻豆原创 RPT-1 is the most capable predictive foundation model that鈥檚 out there today,鈥� he said, delivering much higher prediction quality while being very fast and super-efficient in terms of resource requirements.

Joule, Joule Agents, and AI assistants

The 麻豆原创 CTO also emphasized that the company is committed to providing developers with the most context-rich agentic platform. He noted that 麻豆原创 has already shipped 20 Joule Agents across lines of business and will have approximately 40 by the end of the year, and that these agents can leverage more than 2,100 pre-delivered Joule skills. In addition, more than 300 embedded AI scenarios across product lines are available for customers to date, including Joule Agents, growing to 400 use cases in total by the end of the year.

Ameling added: “Our promise is simple: build with intent. You describe the outcome and 麻豆原创 Build uses AI agents to generate code, logic, and UIs for you, all with seamless access to your applications and data while you stay in the flow… [麻豆原创] Joule for Developers enables vibe coding experiences to make intent-based development simple and intuitive.”

麻豆原创 is taking it one step further by providing extensions to work with VS code, Windsurf, Cursor, OpenAI Codex, Claude Code, Cline, and more directly in 麻豆原创 Build. “You choose a tool, and we meet you where you are,” Ameling said. He also announced that fine-tuned ABAP LLMs with ABAP 1 on AI Foundation will be published in Q4 this year.

麻豆原创 is redefining how developers interact with AI through innovations in Joule and agentic AI, but also in terms of physical AI. Herzig welcomed Torsten G. Mueller, Group CIO and COO BPS at Sartorius, to discuss how the partnership between Sartorius, NEURA Robotics, and 麻豆原创 is bringing to life robots that understand the what, when, and how based on live business context. 鈥淭his is how we鈥檙e really imagining the future, right? Humans and robots working in harmony through Joule, through AI,鈥� Herzig said.

Quantum computing

Looking to the future, Herzig ended the keynote by talking about another 鈥渃ompute paradigm that is still hard to seize鈥�: quantum computing. While he made it clear that 麻豆原创 is not building a quantum computer, 麻豆原创 is teaming up with quantum hardware leaders, like IBM鈥攚hose Director of Research and IBM Fellow Jay Gambetta joined via video鈥攖o help evaluate quantum computing for business processes and applications.

鈥淲e believe quantum will join classical and AI compute in your stack, and we鈥檙e embedding it into the processes and apps you鈥檙e already using, so it just shows up in the workflows of your enterprise,鈥� Herzig said. 鈥淎nd of course, with the cloud, we scale it all. Now, 麻豆原创 has got you covered, and you鈥檝e got your business covered.鈥�

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At 麻豆原创 TechEd in 2025, 麻豆原创 announced innovations and partnerships, including a new collaboration with Snowflake to equip developers to turn business data and AI into real business outcomes.

Here, 麻豆原创 executives share how 麻豆原创 brings AI deep into the development process to level up how developers build. Learn more.

Across industries, operational tasks often require real-time judgement and adaptation to constantly changing conditions. Traditional automation falls short here, as physical execution remains disconnected from business logic.

麻豆原创 addresses this gap with its , which extends the impact of into physical operations. By enabling robots to act with business awareness, they can autonomously execute complex tasks while interpreting real-time enterprise context. This allows organizations to adapt more quickly and reliably to dynamic operational environments.

麻豆原创 is uniquely positioned to deliver these innovations due to decades of experience building business applications deeply embedded in the processes that power the modern enterprise. This allows 麻豆原创 to integrate robotics into business functions seamlessly, and in a way no other company can.  As a result, autonomous robots operate as an extension of enterprise systems鈥攏ot as isolated automation鈥攅nabling consistent, context-aware execution in dynamic environments.

To explore how embodied AI can support your operations, contact us . Current capabilities available for productive use or testing span a range of warehouse and operational scenarios, including picking and placing, asset inspection, quality inspection, and safety inspection.

The following examples illustrate these capabilities across real-world use cases.

Enabling autonomous warehouse execution at Bosch

One of the many strategic initiatives and are working closely is in the area of , where Bosch and 麻豆原创 is collaborating with to explore how physical AI can transform warehouse automation. In a joint proof of concept, 麻豆原创鈥檚 embodied AI capabilities and Aimbo鈥檚 Industrial Embodied AI deployment middleware enabled Aimbo鈥檚 humanoid robot, Anigon, to execute warehouse tasks based on real-time operational context from 麻豆原创 Extended Warehouse Management (麻豆原创 EWM), including delivery data, order priorities, and storage locations.

This initiative combined 麻豆原创 S/4HANA-driven workflows directly with autonomous robotic execution at a European Bosch facility. Trained and deployed by Aimbo in 10 working days, a single Anigon unit equipped with the universal dual grippers provided by Bosch was rapidly integrated to handle intralogistics workflows across three distinct bin configurations and three workstation layouts, creating a bi-directional data loop with 麻豆原创’s embodied AI connected to 麻豆原创 EWM. It successfully demonstrated how enterprise software, AI, and humanoid robotics can work together to enable more adaptive, scalable, and resilient industrial operations.

While the initiative remains in an exploration phase and was validated in dedicated beta and test system environments, the collaboration highlights the strong potential of embodied AI for intelligent warehouse automation and future enterprise-scale industrial AI applications.

“This achievement demonstrates what鈥檚 possible when bold collaboration meets enterprise innovation鈥攃onnecting AI, robotics, and 麻豆原创 to shape the future of autonomous operations. Together, we are moving physical AI from experimentation to enterprise-scale industrial AI with real operational impact.”

Gamze 脰zt眉rk, Head of CoE ERP Product Excellence, Bosch Digital

Optimizing warehouse inspections at Vodafone Procure & Connect

麻豆原创, together with 听补苍诲听, is demonstrating how embodied AI and humanoid robotics can help address operational inefficiencies and safety risks in warehouse environments. The collaboration highlights聽麻豆原创鈥檚 role in connecting robots to end-to-end warehouse processes and business context, helping enable more intelligent, responsive and traceable operations.

In a scenario test at 鈥檚 warehouse in Duisburg, Germany, 麻豆原创鈥檚 embodied and Accenture鈥檚 physical聽AI capabilities enable humanoid robots to perform inspections and monitor operations using real-time business context from 麻豆原创 systems, including inventory data, task priorities, and storage locations.聽The聽pilot聽demonstrates how robots can identify damaged or misplaced items, detect aisle obstructions, and feed findings directly back into 麻豆原创 systems for closed-loop traceability聽and faster operational response.

By reducing reliance on manual inspections and improving operational consistency, the collaboration shows physical AI can enhance both efficiency and safety in dynamic warehouse operations. Learn more about this collaboration聽.

Scaling vehicle validation at Mahindra & Mahindra

, a global automotive and industrial group, collaborated with 麻豆原创 to target a high-volume, error-prone process: manual validation of vehicle identifiers.

Here, 麻豆原创’s embodied AI uses 鈥檚 visual AI, Amalgamation AI, to cross-check real world observations to 麻豆原创 records, enriched by application data and context engine numbers, VIN plate, and Engraved Chassis Number, enabling fast, scalable vehicle validation.

This approach has the potential to increase throughput by up to three times, reduce manual verification effort by about 67%, and achieve data extraction accuracy of 95%-99%.

Optimizing material handling at Martur Fompak

, a global supplier of automotive seating systems, teamed up with 麻豆原创 to address throughput bottlenecks and limited shop floor responsiveness caused by manual material handling.

For this scenario, 麻豆原创鈥檚 embodied AI orchestrates a robot to remove materials across a live automotive manufacturing environment, guided by real-time business context from , including physical attributes and warehouse logistics.

“By assigning repetitive, non-value-adding, and physically demanding tasks to robots, we reduce physical strain and potential injuries while enabling our people to focus on safer, more meaningful, and higher value work that drives productivity and innovation.鈥�

脰zge G眉l艧ah Geydirir, 麻豆原创 Logistics Modules & Operational Excellence Manager at Martur Fompak

Early findings indicate throughput and work order completion improvements of up to five times, along with improved inventory accuracy.

Read the full customer success story and see the 2026 麻豆原创 Innovation Award recognition .

Advancing spare parts inspection at Tetra Pak

, a global leader in food processing and packaging solutions, partnered with 麻豆原创 to address warehouse bottlenecks and risk of errors in spare parts inspection processes.

This proof of concept shows 麻豆原创’s embodied AI and replicating parts of Tetra Paks’ 麻豆原创 Extended Warehouse Management inbound process in a lab, where a humanoid robot verifies the received items and packs them into plastic boxes for further processing. These results can be automatically recorded back into 麻豆原创, closing the loop between execution and system updates.

An 麻豆原创 expert estimates that this end-to-end automation will improve stock and quality accuracy by 10%鈥�20% and build in-house expertise in advanced technologies for continued productivity gains.

Reducing costly storage errors with autonomous inbound processing at 础谤莽别濒颈办-尝骋

, a global manufacturing joint venture, partnered with 麻豆原创 to address a costly and persistent issue with incorrect storage placement in inbound logistics. At the 础谤莽别濒颈办-尝骋 facilities, such errors lead to millions of euros in annual losses through rework, inventory discrepancies, and production disruptions.

In this ongoing proof of concept, 麻豆原创鈥檚 embodied AI enables a humanoid robot to execute and record inbound storage tasks guided by real-time context from (麻豆原创 EWM). Context-enriched 麻豆原创 EWM tasks are routed through 鈥檚 fleet management system, allowing the robot to scan barcodes, transport materials, and provide confirmations with 麻豆原创-level traceability.

This approach has the potential to reduce manual intervention and eliminate placement errors at the source, improving inventory accuracy and end-to-end traceability. By preventing rework and downstream production disruptions, it can significantly lower inbound handling costs while improving overall process quality.

麻豆原创 runs 麻豆原创: scaling autonomous logistics execution in 麻豆原创鈥檚 own warehouse

麻豆原创 partnered with , an innovative robotics startup, to validate embodied AI in a live warehouse environment, demonstrating how AI-driven robotics can enhance operational efficiency.

In this proof of concept, 麻豆原创鈥檚 embodied AI uses real-time business context from to select, prioritize, and orchestrate warehouse tasks through Cyberwave鈥檚 platform. Cyberwave validates tasks in digital twin environments before live deployment, while robotic policies are trained and improved using real-world data, simulation, reinforcement learning, and vision-language-action models.

This demonstrates the readiness of 麻豆原创鈥檚 embodied AI to support autonomous execution in real warehouse operations while maintaining a closed loop between robotic execution and 麻豆原创 system updates.

Cutting-edge experiments at BITZER demonstrate measurable productivity gains

, a leading name in refrigeration, air conditioning, and heat pump technology, teamed up with 麻豆原创 and NEURA Robotics to revolutionize warehouse logistics. In a recent pilot proof-of-concept, BITZER鈥檚 warehouse became a testing ground for one of Europe鈥檚 most advanced humanoid robot, , which was able to perform pick-tasks on its own in real time.

The tasks are selected by embodied AI agents. The process also integrates 麻豆原创鈥檚 business logic from through (麻豆原创 BTP). Prior to its warehouse deployment, 4NE1 was trained virtually using NVIDIA’s Isaac Sim software. This ensured the robot was fully prepared for real-world operations. By integrating embodied AI into warehouse operations, BITZER could reach 24/7 utilization and a high level of responsiveness.

The technology can add to human expertise, stepping in during demand fluctuations and peak periods. It also complements regular shifts with flexible and scalable support. This ensures operations remain agile and efficient, even under varying workloads. Thanks to a single source of truth, orders can be expanded or cancelled in near real time, as robots execute changes almost instantly. This improves reaction times and enables BITZER to maintain high service levels while optimizing the use of resources.

鈥淲e are excited to join the Embodied AI initiative with 麻豆原创 and NEURA. We believe this collaboration will enhance our operational efficiency and drive innovation in our processes.鈥�

Christian Stenzel, Vice President of Corporate Organization and IT, BITZER

Transforming warehouse operations at Sartorius

Imagine stepping into a warehouse in which intelligent machines work side-by-side with humans. The first proof of concept for embodied AI at shows it is possible and marks a milestone in the journey to next-level logistics.

鈥淪o far, like many others, our focus was on fixed automation, in which specialized equipment handles only a single task. Now we鈥檙e making automation intelligent, and far more dynamic, to help us navigate a fast-moving world.鈥�

Steffen Dietz, Manager of Business Process Management in Operations and Supply Chain, Sartorius

The proof of concept, also delivered through the partnership with 麻豆原创 and NEURA Robotics, demonstrates how cognitive robots can support manual workstations in an advanced warehouse environment. Here, the humanoid robot 4NE1 was trained with Sartorius products in NEURA Robotics鈥� lab. The solution builds on an 麻豆原创 S/4HANA migration and 麻豆原创 Extended Warehouse Management (麻豆原创 EWM) rollout in May, which established the foundation for leveraging the latest capabilities from 麻豆原创.

The result boosts efficiency and enhances operational resilience. 鈥淲e鈥檙e really happy to help spearhead this new age together with 麻豆原创 and NEURA,鈥� Steffen Dietz, manager of Business Process Management in Operations and Supply Chain at Sartorius, shared.

Robotics company partners

Building upon these successes, 麻豆原创 announced the following additional partnerships:

AgiBot

Agibot creates general-purpose embodied robot products and an application ecosystem. The company delivers a complete product portfolio and deploy across all major application scenarios.

“Our 麻豆原创 partnership transforms industrial automation by combining humanoid capabilities with enterprise intelligence that understands business context,” said Peng Zhihui, founder of .

.

ANYbotics

ANYbotics provides a full-stack autonomous inspection solution that combines autonomous robotics with inspection intelligence.

“Integrating this continuous flow of inspection intelligence with 麻豆原创 makes operations not only autonomous but truly intelligent, where issues are predicted, understood, and prevented before they affect production,鈥� said Dr. P茅ter Fankhauser, CEO and co-founder of .

.

Apptronik

makes AI-powered humanoid robots designed to collaborate thoughtfully with humans, initially in critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.

Today, Apptronik鈥檚 flagship robot, Apollo, excels at flexible work in dynamic environments, supporting industrial tasks like warehouse picking, placing, and material handling while adapting to changing demand on the floor. With 麻豆原创鈥檚 business context guiding every action, these humanoid robots can execute warehouse tasks with the right priorities at the right time so automation stays aligned with real operational needs.

Booster Robotics

Booster Robotics provides T1 humanoid robots for warehouse operations and field maintenance.

“Our humanoid platforms, with 麻豆原创’s intelligence, creates an adaptive automation foundation that understands business processes and operational context,” said Cheng Hao, CEO of .

.

Boston Dynamics

delivers mobile robots for industrial inspection and safety monitoring, enabling autonomous patrols, anomaly detection, and rapid incident response across large-scale facilities.

By connecting robots鈥� real-time site awareness with 麻豆原创鈥檚 enterprise intelligence, organizations can turn inspection data into safer operations and faster, more informed decisions.

Galbot

Galbot’s fully autonomous, general-purpose humanoid robots have been deployed across a wide range of applications, including industrial, logistics, retail, and healthcare sectors. Powered by proprietary vision-language-action models, the Galbot G1 autonomously performs complex tasks such as precise parts sorting, industrial bin handling, and end-to-end pharmacy operations. These models enable Galbot robots to rapidly adapt to dynamic environments, ensuring high precision and efficiency even in challenging real-world conditions.

“Our collaboration with 麻豆原创 marks a key milestone in transforming how robots understand and operate within enterprise environments. By integrating business context awareness into our robots, we’re creating automation that seamlessly adapts to shifting operational priorities in real time,” said He Wang, founder and CEO of .

.

Humanoid

Humanoid offers reliable HMND 01 humanoid robots that provide cost-effective industrial automation and warehouse solutions, with modular designs that enable configurations for logistics operations, asset monitoring, and scalable field service applications.

“麻豆原创’s AI platform gives our robots intelligence to adapt and scale with enterprise needs, which creates flexible automation,” said Artem Sokolov, founder and CEO of .

RobotEra

develops humanoid robots designed for enterprise operations and, together with 麻豆原创鈥檚 embodied AI, enables robots to execute end-to-end 麻豆原创 EWM workflows鈥攆rom material verification and visual inspection to exception handling鈥攇uided by real-time business context.

By integrating 麻豆原创鈥檚 operational intelligence with RobotEra鈥檚 humanoid robotics platform, customers can enable automation that understands warehouse priorities, handles exceptions, and completes 麻豆原创 EWM tasks with enterprise-grade reliability.

Unitree Robotics

Unitree Robotics provides advanced quadruped Go2 robots for warehouse navigation and asset inspection, plus G1 humanoids with human-like dexterity for logistics operations, alongside industrial B2 models for outdoor facility maintenance.

“麻豆原创 embodied AI agents will revolutionize enterprise autonomous operations from warehouse management to predictive maintenance across facilities,” said Wang Xingxing, CEO and founder of .

.

Robotics enablement partners

麻豆原创 also introduced the following robotics enablement partners to connect humanoid and mobile robots, optimize intralogistics, streamline inspections, and orchestrate physical assets enabled by 麻豆原创 Business AI and automation technologies:

Accenture

partners with 麻豆原创 to advance physical AI for enterprise operations, helping organizations connect digital business processes with autonomous execution.

The collaboration brings 麻豆原创鈥檚 process and data intelligence together with Accenture鈥檚 expertise in robotics, digital twins, and operational transformation to support use cases such as inspection, risk detection, and workflow optimization.

Aimbo AG

delivers the industrial embodied AI deployment Middleware that connects enterprise software with robotic execution. The company鈥檚 vision is to build robust, closed-loop system capabilities spanning models, data, evaluation, and deployment.

True value for industrial leaders lies not in a powerful model alone, but in a reliable system that integrates deeply into the factory鈥檚 operations, continuously creating production value and improving with every cycle.

Capgemini

Capgemini explores the value that can be derived from the convergence of advanced technologies such as agentic and multi-agent AI systems, humanoid robotics, reinforcement learning, spatial computing, real-time 3D environments, and conversational AI. and 麻豆原创 are jointly exploring physical AI to help organizations gain a competitive edge.

Cyberwave 

Cyberwave聽connects 麻豆原创 systems to the physical world through its physical AI platform, which integrates robots, sensors, and digital twins into enterprise workflows.

鈥淭ogether with 麻豆原创, Cyberwave turns enterprise data into coordinated physical action鈥攂ridging the gap between digital intelligence and real-world operations through physical AI,鈥� said Simone Di Somma, founder of .

FairConsult 24|7

partners with 麻豆原创 to deliver embodied AI-enabled warehouse execution, combining 麻豆原创鈥檚 business context-driven task intelligence with FairConsult 24|7鈥檚 fleet management system to orchestrate enterprise-connected robotic operations.

Together, they unite 麻豆原创鈥檚 real-time operational priorities with fleet-level control, enabling robots to execute warehouse tasks with transparency, governance, and measurable performance.

Fujitsu

brings physical AI and visual intelligence capabilities through its Amalgamation AI offering, enabling intelligent image processing and data extraction that can be deployed to edge devices including cameras and robots.

Together with 麻豆原创, Fujitsu’s AI-driven visual capabilities integrate with 麻豆原创’s agent-based workflows to capture and validate real-world inputs鈥攕uch as vehicle identifiers in automotive inspection鈥攄irectly within 麻豆原创 systems for downstream business processing.

HCLTech

HCLTech provides automation expertise through its AI Force platform and 麻豆原创 integration capabilities, leveraging in collaboration with 麻豆原创 to accelerate generative AI-led robotics solutions.

“Our collaboration with 麻豆原创 enables cognitive robotics to seamlessly integrate with enterprise systems, transforming business operations through automation,” said Vijay Guntur, CTO and head of Ecosystems at .

KINEXON

KINEXON brings physical AI to day-to-day material flow management, helping customers scale mixed-fleet operations with a vendor-agnostic orchestration platform for autonomous mobile robots (AMRs), automated guided vehicles (AGVs), and manual vehicles.

“Our collaboration with 麻豆原创 infuses business-driven agentic reasoning into real-world material movement planning and execution, maximizing utilization and throughput,” said Dr. Alexander Huettenbrink, co-CEO of .

LG CNS

, a South Korea鈥揵ased IT and AI transformation company with presence across Asia, partners with 麻豆原创 to deliver AI-driven enterprise transformation.

Combining 麻豆原创鈥檚 business context and application intelligence with LG CNS鈥檚 PhysicalWorks, a robot training and multi-vendor robot orchestration and control platform, the collaboration, starting with PoC initiatives, enables AI-driven automation and optimization using real-time data across business processes.

Lighthouse

Lighthouse transforms business complexities into streamlined digital solutions, leveraging expertise across 麻豆原创 Intelligent Asset Management, 麻豆原创 Business AI, and 麻豆原创 BTP. 

鈥淓mbodied AI has huge potential for use cases, including asset and site inspection, health and safety, and quality inspection to deliver more resilient, flexible operations. We see major customer needs today, such as hazardous environments on offshore platforms in the oil and gas industry, utilities, and transportation,” said Urs Gehrig, managing director of Business Development at .

valantic

and 麻豆原创 are partnering to bring embodied AI to real-world operations. By merging 麻豆原创鈥檚 context-driven orchestration with valantic鈥檚 integration expertise, they enable high-precision, automated asset inspections. This collaboration transforms insights into immediate, coordinated action, ensuring safer operations, stronger compliance, and reliable asset performance. It’s embodied AI, made practical.

Through these strategic alliances, 麻豆原创 continues to lead the evolution from traditional robotic tools to those that empower autonomous operations, informed by deep business context.

To explore how 麻豆原创 technology makes proofs of concepts possible in robotics, explore the . To get involved in 麻豆原创’s Embodied AI initiative, .

Dr. 艁ukasz Ostrowski is head of Embodied AI and Robotics at 麻豆原创.

At 麻豆原创, we believe in creating an ecosystem that simplifies your data landscape and preserves the mission-critical business context of all of your data. This is made possible with a business data fabric.

This architectural approach marks the culmination of decades of progress 鈥� from cubes to warehouses to lakehouses 鈥� now converging toward a business data fabric architecture that brings the true meaning of data together for AI projects to succeed at scale.

Simplify your data landscape with a business data fabric

Earlier this year, we announced 麻豆原创 Business Data Cloud (麻豆原创 BDC), making end-to-end business data fabric capabilities available for everyone that relies on mission-critical 麻豆原创 data, including fully managed 麻豆原创 Databricks.

This week at 麻豆原创 TechEd Berlin, we announced the 麻豆原创 Snowflake solution extension for 麻豆原创 BDC that brings Snowflake鈥檚 data and AI capabilities directly to 麻豆原创 customers.

Together with 麻豆原创 BDC, this provides organizations with the flexibility to choose the right compute and storage for every data and AI workload 鈥� extending their business data fabric while maintaining governance, interoperability, and semantics. As a solution extension, 麻豆原创 Snowflake will be available directly through 麻豆原创, providing a simplified operational experience for customers.

For many customers, integrating data across multi-cloud and hybrid environments adds complexity, especially when bringing transactional and analytical workloads together. Too often, that process comes with a hidden data tax: it strips away the business context and semantics that give data its meaning.

We recently announced 麻豆原创 BDC Connect, a capability that gives customers bi-directional, zero copy data and metadata sharing in 麻豆原创 Business Data Cloud with their existing Databricks and Google Cloud environments. Today, we are excited to announce 麻豆原创 BDC Connect for Snowflake, making it dramatically easier for customers to harmonize and govern mission-critical data wherever it resides.

Connect all your data

As organizations simplify their data landscapes, building a trusted data foundation rooted in business context has become a top priority. A found that nearly half are heavily investing more in harmonizing business data, underscoring the growing importance of using data built on industry standards.

To help accelerate this progress, 麻豆原创 delivers fully managed data products as a core component of 麻豆原创 Business Data Cloud, now with new innovations that expand coverage and connectivity:

• Data product studio: A new capability in 麻豆原创 BDC allows users to create, model, and manage reusable data products using both visual tools and SQL-based transformations from a single workspace. Users can define schema, lineage, and logic to blend 麻豆原创 and non-麻豆原创 data into governed assets with full version control and lifecycle management. It is a simpler way to cross-pollinate data products across lines of business and ensure a consistent definition of data across your business data fabric.
• Additional 麻豆原创 data products: are now available through 麻豆原创 BDC, spanning 麻豆原创 Cloud ERP, 麻豆原创 SuccessFactors, sustainability, and customer experience solutions and more 鈥� bringing even broader coverage across business domains.
• Bi-directional data sharing: We announced data sharing between 麻豆原创 BDC and 麻豆原创 HANA Cloud, unlocking the value of data across both transactional and analytical workloads. Customers can reuse existing objects, such as 麻豆原创 HANA calculation views, directly in 麻豆原创 BDC 鈥� preserving business logic, KPIs, and governance as they extend models across their business data fabric.

Amplify your agents and applications with an AI database

We鈥檙e expanding 麻豆原创 HANA Cloud with new capabilities that make it the AI database for building agents and intelligent applications, helping developers connect and understand all types of data: structured, spatial, graph, vector embeddings, and more, all within a single in-memory engine.

Today, we鈥檙e expanding these multi-model capabilities with three major innovations that make it easier to build agentic AI experiences.

• Expanded knowledge graph capabilities to enable accurate agents: 麻豆原创 HANA Cloud knowledge graph engine will now enable customers to automatically generate knowledge graphs from 麻豆原创 HANA Cloud metadata. The automated graph will include tables and columns and can demonstrate data relationships. These knowledge graphs are customizable and composable, so developers can review data mapping, modify the graph structure, execute a semantic search, and use it to ground agents with the business context it needs to reason accurately.
• Model Context Protocol (MCP) support for 麻豆原创 HANA Cloud: We鈥檙e expanding 麻豆原创 HANA Cloud with Model Context Protocol (MCP) support, giving Joule Agents access to rich multi-model engines in 麻豆原创 HANA Cloud. While SQL remains the default database standard, MCP allows Joule Agents to go beyond rows and columns to interface with unstructured data 鈥� understanding relationships, locations, and meaning across all data types. In practice, that means Joule Agents can navigate relationships between customers and suppliers, analyze geographic dependencies through spatial data, and perform semantic searches through vector embeddings.
• Tabular AI capabilities: This integration with 麻豆原创 AI Core allows users to run AI workloads such as forecasting, anomaly detection, and predictive modeling directly on structured business data from 麻豆原创 HANA Cloud. Along with ready-to-use tabular AI models, customers also gain access to the 麻豆原创-RPT-1 AI model, a new transformer-based foundation model. Embedded natively in 麻豆原创 HANA Cloud, this delivers predictions without task-specific pre-training, enabling developers to use simple SQL procedures to bring AI closer to their 麻豆原创 data and generate semantically rich outputs.

Get started today

A business data fabric provides a deeply integrated ecosystem for all your data. And with 麻豆原创 Business Data Cloud and 麻豆原创 HANA Cloud, you have a fully managed solution for every data and AI workload that ensures business context remains intact across your data landscape.

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

Build custom agents on the most context-rich agentic platform

I鈥檓 excited to share the ability to build custom agents in will be generally available in December. New capabilities include AI-assisted agent design, system-triggered agents, extensibility of 麻豆原创-delivered Joule Agents, centralized enterprise-grade agent monitoring, support for Agent-to-Agent (A2A) protocol, and support for Model Context Protocol (MCP). Read our in-depth and to learn more.

鈥淛oule Studio鈥檚 agent builder connects 麻豆原创 analytics with merchandising systems, powering Accenture鈥檚 Optisell agent to surface at鈥憆isk inventory, recommend pricing actions, and simplify merchandise alert configuration — all without heavy custom development.鈥�

Catherine Nguyen, Global Lead for 麻豆原创 Business Group AI Strategy and Adoption, Accenture

For developers building pro-code agents, (麻豆原创 BTP) offers comprehensive support for popular open-source frameworks, like Crew.AI and LangGraph, and provides end-to-end identity, authorization, governance, and integration capabilities.

MCP support for is now available, providing with direct access to rich multi-model engines. This allows agents to be grounded in full data context: navigating relationships across customers and suppliers, understanding geographic dependencies through spatial data, and performing semantic searches through vector embeddings 鈥�&苍产蝉辫;all within a single in-memory engine. 

Additionally, 麻豆原创 HANA Cloud knowledge graph engine can now automatically generate knowledge graphs from 麻豆原创 HANA Cloud metadata. What used to take weeks of manual modeling can now happen automatically in minutes.

We鈥檙e also enabling agentic memory in 麻豆原创 HANA Cloud. With long-term memory, AI agents can persist context across long-running sessions and memorize past input and decisions, just like humans do, to become continuously smarter.

I鈥檓 excited to share several innovations, including our first enterprise relational foundation model (RPT), 麻豆原创-RPT-1, accompanied by a no-code testing playground environment and prompt optimizer service. New capabilities are continuously added to our , empowering developers to experiment with leading models and orchestration tools and scale AI development and productization across 麻豆原创 and non-麻豆原创 landscapes. .

Vibe on 麻豆原创 Build using the tools of your choice

New local give developers the ability to use agentic tools for development and preferred code assistants, such as Cursor, Windsurf, Claude Code, Cline, and OpenAI Codex — all while maintaining enterprise-grade governance and clean-core alignment.

An 麻豆原创 Build extension pack for Visual Studio (VS) Code is now available to simplify development of CAP, Fiori, UI5, and mobile applications. This makes it easier for VS Code developers to build faster and deploy apps on 麻豆原创 BTP. Looking ahead, we will publish our extensions on the Open VSX Registry to simplify the onboarding of these tools and provide similar native development experiences on other integrated developer environments (IDEs).

Increase velocity with 麻豆原创 Joule for Developers

, the best code assistant for 麻豆原创 development, empowers developers of all skill levels to build more efficiently by leveraging comprehensive, AI-infused developer tools to deliver precise, contextualized outcomes powered by purpose-built, 麻豆原创-centric AI models. This frees-up time to be more productive, creative, and proficient in accelerating ABAP, Java, JavaScript, and visual tool-based application development and automation of 麻豆原创 processes.

New enhancements to 麻豆原创 Joule for Developers, such as within and capabilities, help developers modernize legacy systems and meet enterprise-grade governance and security requirements. These improvements also boost productivity, enhance code quality, and support cloud transformation goals.

Developers working in can now use AI-assisted content creation to quickly generate comments, create workspace content, and summarize documents. AI responses can also draw directly from user-specific folders and workspace content, providing users with trusted insights tailored to their roles, while maintaining compliance and secure role-based document grounding. 

Looking ahead, new ABAP large language models (LLMs) trained on ABAP code and specialized for ABAP development will be released next year.

Connect everything to boost productivity

Developers build the bridges that keep businesses running. We鈥檙e making that work easier and faster with . The following innovations make API and agent-driven automation easier, ensure reliable end-to-end security with real-time monitoring, and boost developer productivity.

We are continuing to embed AI capabilities directly into 麻豆原创 Integration Suite. will now automatically provide targeted recommendations and intelligent healing to resolve common API anomalies such as error spikes, latency surges, or abnormal traffic patterns. Developers can also ask Joule to about the most used APIs to gain deep API insights and understand usage patterns.

MCP Gateway support will enable customers to expose custom APIs and integration flows that can be consumed by AI agents. This feature introduces the ability for customers to enrich custom agents built in Joule Studio or extend Joule Agents by integrating data from third-party and legacy 麻豆原创 systems, composed as MCP tools for smooth agentic consumption. This approach standardizes access, centralizes governance and security, and simplifies discoverability in the .

To jumpstart integration projects, you can access hundreds of out-of-the-box integration adapters as well as pre-built API, event, and integration content on .

Gain security and operations built in, not bolted on

麻豆原创 BTP provides developers with a unified environment to manage applications, secure data, and scale solutions seamlessly across 麻豆原创 and third-party systems. With core centralized application lifecycle, interoperability, security, and administration capabilities, developers can easily and quickly resolve errors and boost team productivity. Read the full list of enhancements in the .

Explore at your own pace

If you missed attending 麻豆原创 TechEd in person or virtually, please be sure to read the full list of announcements in the  and watch .

Wherever you鈥檙e at in your journey, there are easy ways to get started:

• Visit to start a
• Explore and new 
• Register for our upcoming on December 9
• Read more in “Introducing 麻豆原创 Snowflake, New Data Fabric Innovations for 麻豆原创 Business Data Cloud and 麻豆原创 HANA Cloud“

Michael Ameling is general manager and chief product officer of Business Technology Platform and a member of the Extended Board of 麻豆原创 SE.

BERLIN 鈥� At 麻豆原创 TechEd in 2025, (NYSE: 麻豆原创) brings AI deep into the development process to level up how developers build.

New AI-driven capabilities in the 麻豆原创 Build solution, an expanding data ecosystem and powerful Joule Agents empower developers to move from idea to impact with unprecedented speed and confidence. As AI transforms the nature of professional work, 麻豆原创 also pledges to equip 12 million people worldwide with AI-ready skills by 2030.

鈥溌槎乖粹�檚 announcements today give developers the tools they need to deliver at the speed of AI,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE. 鈥淚nnovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the driver’s seat — where they belong.鈥�

Opening the Developer Ecosystem

麻豆原创 Build, the company鈥檚 flagship solution for enterprise application development and automation, now gives developers more freedom to build, extend and automate using the tools they love most.

For instance, developers who prefer agentic development solutions like Cursor, Claude Code, Cline and Windsurf can now use 麻豆原创 development frameworks with new 麻豆原创 Build local Model Context Protocol Servers. Visual Studio Code users will be able to access 麻豆原创 Build capabilities directly in their development environment with a new 麻豆原创 Build extension. This extension will also be made available later on Open VSX Registry for other development environments. 麻豆原创 and n8n also announced plans for an integration so Joule Studio agents and n8n agents can work together.

And with new agent building capabilities in Joule Studio, developers have the tools they need to extend 麻豆原创鈥檚 ready-to-use agents and build new agents grounded in 麻豆原创 business data and context that can act autonomously based on changing business conditions.

Putting Data to Work

Every intelligent application starts with trusted data. 麻豆原创 is giving developers more ways to put that data to work through 麻豆原创 Business Data Cloud.

The solution now connects with more of the data and AI platforms developers use every day. A new 麻豆原创 Snowflake solution extension for 麻豆原创 Business Data Cloud brings Snowflake鈥檚 fully managed data and AI capabilities directly to 麻豆原创 customers, giving them the flexibility to choose the right compute and storage for each data and AI workload, while maintaining governance, interoperability and business context. 麻豆原创 also announced a new 麻豆原创 Business Data Cloud Connect partnership with Snowflake. This complements existing integrations with Databricks and Google Cloud, giving developers more freedom to choose how they work with 麻豆原创 data.

With a new data product studio capability in 麻豆原创 Business Data Cloud, developers can turn raw data into ready-to-use assets known as data products that support analytics, AI and application development.

An expanded capability in the 麻豆原创 HANA Cloud knowledge graph engine can automatically generate knowledge graphs. This capability maps relationships across 麻豆原创 database tables, columns and data models, revealing how data fits together and why it matters. Developers will be able to see how their data connects across systems and uncover underlying business insights.

Bringing AI Autonomy to Life

麻豆原创 is evolving its AI portfolio to give developers the intelligence and orchestration power they need to take AI from insight to action.

麻豆原创 introduced its first enterprise relational foundation model, a new class of AI that predicts business outcomes rather than the next word in a sentence. 麻豆原创-RPT-1, or the first-generation Relational Pre-trained Transformer, can make fast and accurate predictions for common business scenarios like delivery delays, payment risk or sales order completion. 麻豆原创 launched a free playground environment for developers today.

New AI assistants in Joule coordinate multiple agents across workflows, departments and applications, bringing automation and autonomy to life. These assistants plan, initiate and complete complex tasks spanning finance, supply chain, HR and beyond. Today, 麻豆原创 introduces new agents built for technical users. For example, an agent for business process analysis will help teams understand how processes run, identify inefficiencies and uncover opportunities to optimize workflows and drive measurable improvements.

Lastly, as AI changes the nature of work for everyone, 麻豆原创 is pledging to equip 12 million people worldwide with AI-ready skills by 2030. 麻豆原创 will expand hands-on training and certification programs that integrate practical AI-ready tools, including through its partnership with online learning platform Coursera.

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鈥�.

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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:
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Marcus Winkler, +49 6227 7-67497, marcus.winkler@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
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BOZEMAN, Mont. and WALLDORF, Germany 鈥�&苍产蝉辫; (NYSE: SNOW), the AI Data Cloud company, and (NYSE: 麻豆原创), a global leader in enterprise applications and business AI, today announced a new collaboration to enable organizations to leverage Snowflake鈥檚 AI Data Cloud and 麻豆原创 Business Data Cloud (麻豆原创 BDC) together with semantically rich data.

The joint effort will make Snowflake鈥檚 data and AI platform available as an 麻豆原创 solution extension for customers of the 麻豆原创 BDC solution. The new offering, 麻豆原创 Snowflake solution extension for 麻豆原创 Business Data Cloud, unites 麻豆原创鈥檚 deep expertise in mission-critical business processes and semantically rich data with Snowflake鈥檚 unified platform capabilities for building AI and machine learning solutions. 麻豆原创 and Snowflake are also enabling zero-copy sharing between 麻豆原创 BDC and Snowflake to help customers get richer insights, build enterprise-grade intelligent applications, and unlock AI-enabled innovation that fuels business transformation.

鈥淏y tightly integrating 麻豆原创 and Snowflake, we鈥檙e making it simple for enterprises to connect their critical business data with its rich context in 麻豆原创 with the power of seamless AI app and data agent development at scale in Snowflake,” added Christian Kleinerman, EVP of Product, Snowflake. 鈥淓nterprises can now innovate faster with Snowflake and 麻豆原创 BDC and seamlessly share data between the platforms鈥攝ero-copy and fully governed.鈥�

麻豆原创 Snowflake brings Snowflake into the open data ecosystem of 麻豆原创 BDC and the business data fabric鈥攅mpowering customers with greater openness and choice while extending 麻豆原创 BDC with Snowflake鈥檚 AI, analytics, data engineering, Marketplace, and collaboration capabilities. Customers can use 麻豆原创 BDC with 麻豆原创 Snowflake as a cloud-scale compute and storage option to extend the value of their data. Leveraging bidirectional, zero-copy data access data and AI teams can work with semantically rich 麻豆原创 data products in real time, within a unified governance framework. As a result, customers can harmonize 麻豆原创 and non-麻豆原创 data while optimizing total cost of ownership across workloads and build agents and AI applications in 麻豆原创 Snowflake fueled by trusted 麻豆原创 data products.

鈥淏ringing Snowflake to 麻豆原创 Business Data Cloud empowers our customers with openness and choice,鈥� said Irfan Khan, President and Chief Product Officer for 麻豆原创 Data and Analytics, 麻豆原创 SE. 鈥淭ogether, we combine 麻豆原创鈥檚 decades of leadership in mission-critical business applications with Snowflake鈥檚 modern data platform to deliver a unified, enterprise-ready, and 麻豆原创-supported experience that extends the value of business data across the entire ecosystem.鈥�

With 麻豆原创 Snowflake, customers can:

• Build a trusted, AI-ready data foundation to harmonize 麻豆原创 and non-麻豆原创 data: Unify their data landscape with an integrated business data fabric鈥攅nabling more seamless zero-copy sharing, enriched modeling, and a complete, business-ready view of their data in real time for all data engineering, analytics, and AI and machine learning workflows across the enterprise.
• Accelerate AI business value with semantically rich data: Simplify AI governance, ground AI in organizational knowledge, and build tailored agents鈥攈elping to ensure more secure, context-rich, and intelligent applications across the enterprise.
• Develop intelligent applications grounded in mission-critical business data: Build, deploy, and continuously optimize intelligent applications faster with a harmonized and democratized data foundation powered by semantically rich, trusted data products that accelerate the pace of innovation and production.

In addition to 麻豆原创 Snowflake, the partnership also includes 麻豆原创 Business Data Cloud Connect for Snowflake, a capability enabling bidirectional, zero copy data sharing with Snowflake. Enterprises already using Snowflake can leverage 麻豆原创 BDC Connect to integrate their existing instances of Snowflake with 麻豆原创 Business Data Cloud for more-seamless, zero鈥慶opy access, providing Snowflake users with real-time access to semantically rich 麻豆原创 data products鈥攚ithout duplication.

麻豆原创 and Snowflake are supporting thousands of customers, including industry leaders like AstraZeneca, as they transform their industries with this partnership.

“AstraZeneca is constantly pushing the boundaries of science and is pioneering in life-changing medicines,鈥� said Russell Smith, Vice President of ERP Transformation Technology, AstraZeneca. 鈥淒ata and AI are central to achieving this aim, and our close collaboration with 麻豆原创 and Snowflake compliments our ability to access, process and analyze real-time data. This announcement will accelerate our mission and recognizes that every minute matters to make breakthroughs for patients.”

麻豆原创 Snowflake is planned to be generally available in Q1 2026. 麻豆原创 BDC Connect for Snowflake is planned to be generally available in H1 2026.

Learn more

• Learn more about the power of 麻豆原创 and Snowflake together .
• Discover what makes the 麻豆原创 Business Data Cloud unique .
• For more information on 麻豆原创 Snowflake, visit the and read this .
• Stay on top of the latest news and announcements from Snowflake on and .

Visit the . Get 麻豆原创 news via  and .

About Snowflake

Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 12,000 customers around the globe, including hundreds of the world鈥檚 largest companies, use Snowflake鈥檚 AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone. Learn more at (NYSE: SNOW).

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:
麻豆原创: Scott Malinowski, scott.malinowski@sap.com
麻豆原创 麻豆原创 Room; press@sap.com
Snowflake: Tom Hannigan, press@snowflake.com

This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake鈥檚 business strategy, (ii) Snowflake鈥檚 products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake鈥檚 products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading 鈥淩isk Factors鈥� and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events.
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The keynote featured demos of new cross-portfolio solutions and innovations, showing how they can work together to tackle challenges like scaling and expanding into new markets, managing complexity, and delivering personalized experiences. “Innovation is only as powerful as the product experience it delivers,鈥� Alam said. 鈥淚t’s one thing to have cutting-edge tools at your disposal, but it’s another thing to integrate those technologies into the products you rely on every day to solve real-world challenges.”

Faster, Better, Automated

Since last year’s 麻豆原创 TechEd, has delivered more than 1,000 road map items, has a year-to-date overall availability again above 99.99%, and has grown an adoption rate over 60% year over year. The 27,000 customers and 2,800 partners are a testament to the huge demand we are seeing for 麻豆原创 BTP.

This is why the platform’s availability is being expanded to eight 麻豆原创-managed data centers across the globe, in addition to running on infrastructure provided by our partners AWS, Microsoft, and Google. With that, we are bringing 麻豆原创 BTP closer to customers.

Along with the expanded coverage comes more learnings, guidance frameworks, integration flows, and use cases. 麻豆原创 Business Accelerator Hub and 麻豆原创 Discovery Center will be bolstered with more assets and use case-specific guidance, especially around AI and business process transformation.

To further the developer experience, the next release of solutions will include ABAP Cloud, making 麻豆原创 Build the go-to for building extensions. Along with the new 麻豆原创 Build extensibility wizard, which helps to create extensions in 麻豆原创 S/4HANA, 麻豆原创 is making it easier for customers to build extensions while keeping a clean core. This is critical to ensure that customers鈥� systems remain consistent, maintainable, and upgradeable.

麻豆原创鈥檚 AI copilot will also be integrated into 麻豆原创 Build, so developers can be more productive and take advantage of the efficiencies and automation AI can provide. With Joule studio, the copilot will even be able to draw on structured data stored in third-party systems, better enabling developers to design custom skills for Joule in a low-code environment.

And to help developers ground large language models (LLMs) in the 麻豆原创 semantical model and build business apps, the new 麻豆原创 Knowledge Graph solution provides the connection between natural language and 麻豆原创鈥檚 metadata. The solution provides a deeper level of understanding across 麻豆原创鈥檚 data landscape, enabling more informed, data-based decision-making. Together with 麻豆原创 HANA Cloud knowledge graph engine and 麻豆原创 HANA Cloud vector engine, developers can build generative AI models rooted in 麻豆原创-specific business semantics, helping to build a more reliable, enterprise-ready AI experience accurately and effectively.

A New Level of Productivity

Not only are 麻豆原创 Build solutions enabled with Joule, but 麻豆原创 is continuing to integrate AI across its entire portfolio, including ERP, customer experience, human capital management, localization, spend management, sustainability, supply chain management, and more. As announced at 麻豆原创 Sapphire in 2024, 80% of 麻豆原创鈥檚 most-used workflows will be covered by Joule by the end of this year.

Joule is going agentic, meaning it can use AI agents powered by LLMs to help solve complex business problems on its own through planning, self-reflection, logical reasoning, and collaboration. For example, Joule can help resolve a customer dispute about an overdue bill by calling on AI agents specialized in contracts, invoices, and payment history. With each AI agent doing what they do best, they can work together to solve more complex business problems across functions and break down silos.

AI is best when it can be used in the flow of work and when it is meaningfully embedded as part of a unified user experience across the portfolio. Sometimes this is on a desktop, but often we look to use AI on our mobile devices. To make AI accessible anytime and anywhere, 麻豆原创 is introducing Joule into its mobile applications via 麻豆原创 Mobile Start 鈥� while still maintaining enterprise-level security and privacy and staying true to the company鈥檚 values of relevant, responsible, and reliable AI.

Clear and Customer-Centric, Not Complex

Ultimately, all the innovations announced at 麻豆原创 TechEd in 2024 and demoed in the opening keynote have one main goal: to make the lives of customers easier 鈥� whether it be through leveraging generative AI to accelerate development projects, making more informed business decisions with intelligent data applications, mitigating project management struggles with AI, confidently and compliantly localizing business operations, or enabling sales reps with intelligent selling tools to win deals.

“Technology should simplify, not complicate. It should bring clarity, not complexity,” Alam said. “This is why we’re constantly pushing the boundaries of what’s possible while keeping your business goals front and center.”

To learn more about all the solutions and innovations announced at 麻豆原创 TechEd in 2024, , , or explore more event news and coverage.

Gillian Hixson is an integrated communications specialist at 麻豆原创.

However, the promise of AI-driven transformation has yet to be fully realized. This is largely because true business transformation doesn’t hinge only on improving individual productivity; it’s about unlocking the power of collaborative capabilities that enable cross-functional teams to solve ever-evolving business challenges. This shift paves the way for a new era of automation, driving the next industrial revolution.

This year at 麻豆原创 TechEd, we extended our business AI strategy with a greater focus on transforming how businesses operate through autonomous, collaborative AI agents. Seamlessly infused into our AI copilot Joule, these agents span across business processes, breaking down silos and enhancing cross-functional collaboration.

At 麻豆原创, we believe that AI’s next frontier goes beyond isolated efficiencies and is about seamlessly unifying people, data, and processes to create business value and amplify the impact of outcomes.

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Beyond Productivity Gains

What makes different is its collaborative nature. Unlike other AI agents focused on single domains, Joule鈥檚 agents work together, crossing the boundaries of various business functions such as supply chain, finance, HR, and more. This approach enables our customers to solve multifaceted challenges in a way that isolated productivity tools simply cannot match.

鈥溌槎乖粹�檚 AI copilot Joule is uniquely positioned to provide significant value with AI agents,鈥� said Ritu Jyoti, GM and GVP of AI and Data, Market Research and Advisory, IDC. 鈥淲ith access to extensive contextual data and deep expertise across business functions like ERP, CRM, and HR, 麻豆原创 will enable agentic AI solutions that seamlessly integrate across the business to transform the future of work.鈥�

These AI agents are built with the ability to reason, plan, and execute; not just individually, but collectively, across multiple applications. 

Imagine asking Joule to help settle a payment dispute. This involves monitoring and assessing accounts, issuing warnings, analyzing data, proposing recommendations, and generating comprehensive reports. In this scenario, a collection agent would plan a strategy by dividing the broader task into manageable steps. These steps are then seamlessly executed by collaborative expert agents, such as an email agent, an invoice agent, and a support case agent, which automate the entire workflow, freeing employees to focus on more strategic, value-driven activities.

Joule doesn鈥檛 just automate isolated steps; it brings together agents to deliver connected, cross-functional outcomes. The ability to break down organizational and data silos is what makes Joule stand out. It is a trusted copilot that harnesses AI to solve complex challenges that require collaboration across teams and systems.

Drive Business Transformation

Joule’s design principles are rooted in 麻豆原创’s unmatched business process expertise and access to business data. This gives Joule a unique ability to deliver transformative outcomes across the enterprise. By positioning agents at the intersection of high autonomy and deep collaboration, Joule pushes the boundaries of what is possible today.

Soon, Joule鈥檚 AI agents will further evolve with the help of , a solution that will unlock the full value of data by connecting it with the rich business context captured in 麻豆原创 applications. For example, a collection agent tasked with researching a payment dispute will use 麻豆原创 Knowledge Graph to gather all the pertinent orders, invoices, and payments from the 麻豆原创 applications, enabling other agents to complete their tasks more efficiently.

Our vision for Joule is in sharp contrast to many AI agents on the market, which can struggle to connect across various applications. Most are designed to address specific, single-use cases such as sales, customer services, or finance, based on pre-built language models with prompt engineering. These agents operate within narrow contexts, and while they improve personal productivity, they do not fundamentally transform business processes.

With collaborative agents that influence entire workflows and deliver new business value, Joule stands at the forefront of the next evolution of AI-driven business transformation.

We first introduced our vision for collaborative AI agents at 麻豆原创 Sapphire in 2024, marking the beginning of a journey that has advanced significantly. With the developments announced this week at 麻豆原创 TechEd, the first set of Joule’s agents will become available in Q4 2024, including extensibility capabilities. More out-of-the-box agents will be released in 2025, as well as the ability for customers to create their own agents with .

With 麻豆原创, companies can expect a future where AI is a catalyst for truly unified and intelligent business operations. 

To learn more about collaborative agents in Joule, visit our page, check out the , and read about the recently released business AI innovations.

Philipp Herzig is chief AI officer of 麻豆原创.

麻豆原创 has announced the release of 麻豆原创 S/4HANA Cloud Private Edition 2023 FPS02, which includes advanced AI-assisted capabilities and the expansion of 麻豆原创’s copilot Joule, the cutting-edge digital assistant designed to transform the user experience.

Combined with a suite of other innovations, these features are set to deliver unprecedented efficiency and intelligence across various business functions.

Pioneering AI-Assisted Capabilities

麻豆原创 S/4HANA Cloud Private Edition 2023 FPS02 harnesses the power of artificial intelligence (AI) to automate routine tasks, provide predictive insights, and streamline decision-making processes. New capabilities in this release include:

• AI-Assisted Sales Order Fulfillment: Sales departments can now leverage AI to monitor and resolve order fulfillment issues to greatly enhance order accuracy and customer satisfaction. Sales orders are summarized on header and item level in natural language, fulfillment issues are identified and resolved, sales orders can be updated, and document flows are made transparent.
• Conversational Planning in Transportation Cockpit: The supply chain management module introduces AI-driven conversational planning. This feature allows users to interact with the system using natural language, simplifying complex transportation decisions and significantly reducing planning time.
• Inbound Cargo Document Creation: The automation of inbound cargo document creation through AI reduces administrative burdens and minimizes errors. It improves operational efficiency by reducing manual checks at the gate and waiting time for trucks.
• Maintenance Order Recommendations: In asset management, AI assists in predicting maintenance needs. The system analyzes asset performance data to recommend proactive maintenance strategies, helping extend asset lifespan and reduce downtime.

Introducing Joule for 麻豆原创 S/4HANA Cloud Private Edition: The Future of Digital Assistance

At the heart of this release is Joule, a revolutionary digital assistant designed to streamline user interactions and boost productivity across the 麻豆原创 ecosystem. Joule offers both transactional and navigational support, providing context-sensitive suggestions and shortcuts that significantly enhance the user experience.

With this release, Joule supports:

• Project Management Support: Joule simplifies project management tasks by offering intuitive navigation and transaction handling. This makes it easier for project assistants to manage complex projects, ultimately improving project flow and efficiency.
• Master Data Governance: Generative AI allows changes to master data and provides a summary of changes using natural language, reducing the time to change master data attributes and increasing the transparency of changes for reviewers.
• Hybrid Sourcing in Advanced Available-to-Promise (ATP): This functionality allows for a more dynamic approach in covering sales order demands, leveraging both internal and external suppliers to meet demand efficiently.

Additional Innovations Across Functional Areas

In addition to AI-assisted functionalities and Joule, this latest release is packed with enhancements aimed at refining procurement, finance, R&D, and service management.

Procurement

• Version and Pricing Comparison for Purchasing Contracts: Enables procurement professionals to efficiently evaluate different contract versions and pricing, ensuring optimal decision-making
• Controlled Information Access for Sourcing Projects: Enhances security by allowing defined access levels for contributors, safeguarding sensitive information and fostering secure collaboration

Finance

• Centralized Plan-Versus-Actual Analysis: Integrates plan data for a cohesive comparison between projected and actual financial performance, aiding precise budget management
• Subscription Order Management with 麻豆原创 GTS Integration: Streamlines handling of subscription-based revenue models and ensures compliance with global trade regulations

Manufacturing

• Renewed Production Planning and Detailed Scheduling KPI Monitor: Provides in-depth analysis of key performance indicators (KPIs), enabling informed production planning and scheduling decisions
• Engineer-to-Order Production Models: Facilitates seamless integration of engineering requirements into production plans, reducing lead times and aligning manufacturing with customer-specific configurations

R&D

• Simplified Handling of In-Planned Services in Lean Service Procurement: Ensures efficient processing of service requirements, reducing administrative overhead and accelerating procurement cycles

Service Management

• Combined In-House Repair and Field Service: Enhances service efficiency by enabling combined in-house repair and field service within a single service order
• Non-Serialized Products in In-House Repair: Broadens the range of items that can be managed and repaired under the same system, driving operational flexibility

Asset Management

• Inspection Checklists Management: A new 麻豆原创 Fiori app simplifies the management of inspection checklists. This new app shows an overview of inspection checklists in the Inspection Lots view, or the corresponding inspection characteristics in the Characteristics view. The planner can filter the result list and perform actions, like setting the usage decision.

Cross Topics

• Analyze Direct Project Transfers in Migration Cockpit: Facilitates smoother data migrations and offers analytical insights to ensure data integrity

Learn more about our recent innovations:

The 麻豆原创 S/4HANA Cloud Private Edition 2023 FPS02 release is a testament to our commitment to leveraging advanced technologies like AI and digital assistants to transform enterprise operations. With the introduction of AI-assisted capabilities and Joule, enterprises can expect significant improvements in efficiency, accuracy, and user experience.

As businesses worldwide navigate the complexities of the digital age, 麻豆原创鈥檚 latest innovations provide the tools needed to stay ahead of the curve, driving strategic growth and operational excellence.

For more on AI capabilities in RISE with 麻豆原创, join the upcoming
.

Maura Hameroff is senior vice president of ERP Product Marketing at 麻豆原创.

The addition of Mistral Large 2 broadens the choice of large language models (LLMs) that organizations can use for custom AI solutions that complement and extend 麻豆原创 applications. By hosting the model on 麻豆原创鈥檚 operated infrastructure, 麻豆原创 can better streamline its delivery and deployment of 麻豆原创 Business AI across the enterprise. This is 鈥媏ssential to鈥嬧�嬧�� meet regulatory requirements in certain regions or industries.聽聽

Customers, particularly those in heavily regulated industries, require solutions that not only address their innovation needs, but also ensure their data is not exposed to third-party entities. Customers with specific needs can adopt generative AI solutions with greater confidence, knowing their data is managed through a secure 麻豆原创 environment.聽

鈥淏y adding Mistral Large 2 to 麻豆原创鈥檚 generative AI hub, we鈥檙e providing our customers with a leading 鈥嬧�媗arge language model that runs in 麻豆原创 data centers and on European infrastructure. This provides customers with the ability to build innovative solutions and can help them to comply with regulations in certain regions or industries,鈥� shared Philipp Herzig, chief AI officer of 麻豆原创 SE. 鈥淥ur expanded partnership with Mistral AI reinforces 麻豆原创鈥檚 commitment to responsible AI, enabling businesses to harness the power of generative AI while maintaining control over their data and intellectual property — a key milestone toward more sovereignty for the European market.鈥�

鈥淲e 鈥媗ook forward鈥嬧�� 鈥媡o bring鈥媔ng鈥� Mistral鈥檚 advanced language models, including Mistral Large 2, to 麻豆原创 Business AI, providing AI solutions that help customers meet the highest standards for data security and regulatory compliance,” Gianna Lengyel, head of Business Development at Mistral AI, said. “Our partnership with 麻豆原创 enables businesses to benefit from state-of-the-art generative AI without compromising on control or compliance, particularly for organizations operating in highly regulated environments.鈥�

Mistral AI鈥檚 models are accessible via the generative AI hub in , making it simple to build generative AI use cases for 麻豆原创 applications. Customers can leverage these models to embed advanced AI capabilities into their 麻豆原创 applications or create custom solutions on 麻豆原创 Business Technology Platform (麻豆原创 BTP).聽聽

To learn more, customers can reach out to their 麻豆原创 point of contact or visit the site for further details on how to begin leveraging these AI capabilities in their specific use cases.

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 .

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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�

Two years ago, we introduced 麻豆原创 Build, a unified low-code environment that enables developers of all skill levels to create, extend, and automate business applications with drag-and-drop simplicity. Last year, we broadened its capabilities and integrated Java and JavaScript development with AI.

With more than 16,000 customers now leveraging 麻豆原创 Build globally, momentum continues to surge, enabling developers to build, automate, and innovate with greater speed and ease.

Today, we are thrilled to announce new enhancements of 麻豆原创 Build, combining the power of ABAP development, more generative AI capabilities, as well as deeper integration with 麻豆原创 S/4HANA and other business applications. 麻豆原创 Build streamlines extension creation and accelerates ERP modernization. It connects all 麻豆原创 development tools — ABAP, Java, JavaScript, and low-code — so teams can collaborate easily regardless of skill set. 

Let鈥檚 look at what developers can achieve with 麻豆原创 Build.

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麻豆原创 Build with ABAP offers developers capabilities to easily extend their 麻豆原创 S/4HANA and other business applications, utilizing Joule鈥檚 generative AI capabilities. It facilitates in-context collaboration between developers and business users to build business custom apps and processes efficiently, all without compromising on security and governance.

– Brad Shimmin, Chief Analyst, AI Platforms, Omdia

Streamline 麻豆原创 Application Development and Automation

麻豆原创 Build is the only solution offering direct development access within 麻豆原创 S/4HANA for easier customizations. Developers can quickly create scalable and secure 麻豆原创 S/4HANA extensions — both on stack, to extend applications within 麻豆原创 S/4HANA, and side by side, to extend applications on 麻豆原创 BTP — while following 麻豆原创’s clean core best practices.

Key capabilities include:

• The new 麻豆原创 Build extensibility wizard in 麻豆原创 S/4HANA Cloud for extending custom fields, logic, applications, and process automations while maintaining in-app business context
• Accelerating the creation of guided experiences with pre-built content to extend 麻豆原创 business applications across business processes such as spend management, HR, procurement, and more

Accelerate Development with Joule-Powered AI Capabilities

Envision a development tool where anyone, from beginners to seasoned pros, can rapidly bring their ideas to life with generative AI. Joule is the co-pilot that developers need for all 麻豆原创 development because it is uniquely trained on 麻豆原创 data and processes.

New generative AI capabilities include:

• Code explanation, code completion, business object generation, and unit test generation
• Documentation search and instant assistance from 麻豆原创 Help and 麻豆原创 Community within 麻豆原创 Build
• Workflow automation and approval recommendations
• The ability to extend Joule, allowing developers to create, deploy, monitor, and manage custom AI skills with drag-and-drop simplicity

Foster Fusion Team Development

麻豆原创 Build breaks down silos, enabling collaboration across development teams without barriers. Developers can achieve greater flexibility and scalability together as a team through:

• 麻豆原创 Build lobby, the central entry point for all development tools, now featuring access to ABAP, to build any type of extensions on 麻豆原创 BTP
• Streamlined navigation between ABAP, Java, JavaScript, and low-code tools while maintaining full business context for efficient full-stack application development
• Centralized lifecycle management and governance for all 麻豆原创 projects

Get Started Today

麻豆原创 Build is the gateway to faster, more efficient application development and automation for you and your organization.

• Want to learn more? Visit for more details.
• Ready to see 麻豆原创 Build in action? Sign up for the .
• Curious to learn more about 麻豆原创 Build? Read 鈥�.鈥�

JG Chirapurath is chief marketing officer for Business Technology Platform at 麻豆原创.

*General availability: The new capabilities will be available in Q4 2024, with additional AI features rolling out through Q1 2025

Today, one quarter ahead of our road map, we offer more than 100 high-value generative AI capabilities that are ready to use out-of-the-box across 麻豆原创 cloud applications.

From navigating regulatory challenges to addressing complex HR policy questions to generating comprehensive sustainability reports, 麻豆原创 Business AI capabilities are driving real business value for our customers.

For example, a company with 2,000 employees can expect to improve its top line by up to 3.5%* and gain efficiencies for the company鈥檚 bottom line of up to 1.4%* by adopting available 麻豆原创 Business AI features. These productivity gains are relevant for developers and business users alike, as well as for the entire C-suite and all lines of businesses — from the CFO to the chief human resources officer, chief procurement officer, and more.

And this only accounts for AI features that we released in the last 12 months.

AI innovations delivered in Q3 2024 include:

• 麻豆原创 S/4HANA Cloud Public Edition has a flurry of AI-assisted improvements, from smart summaries and filters to natural language explanations of asset depreciation as well as accelerating your financial close. In addition, Joule can now proactively detect and generate proposals for missing compliance configurations around U.S. internal tax. Joule is now also generally available in 麻豆原创 S/4HANA Cloud Private Edition.
• In supply chain, conversational search features in 麻豆原创 Product Lifecycle Management, 麻豆原创 Integrated Business Planning for supply chain, and 麻豆原创 Asset Performance Management allow users to perform informal searches more efficiently and access information more easily. In 麻豆原创 Field Service Management, the AI-assisted activity summary is now generally available. The AI-enabled visual inspection feature has been expanded to 麻豆原创 Asset Performance Management.
• In 麻豆原创 Business Technology Platform (麻豆原创 BTP), a new AI capability in 麻豆原创 Integration Suite lets developers automatically generate integration flows by simply describing the scenario, meaning designing time is three times faster*. 麻豆原创 Build Code provides generative AI to help developers build applications up to 30%* quicker and reduce application development costs by up to 30%*.
• Sustainability experts can now automate AI-enabled environmental, societal, and governance (ESG) reports, reducing the time needed to collect relevant ESG metrics manually from 30 minutes* to 30 seconds*, building on ESG or own report templates and using the data available in 麻豆原创 Sustainability Control Tower.
• 麻豆原创 Signavio offers AI-assisted process recommender and AI-assisted performance indicator recommender features, which can provide preconfigured process models and quick recommendations on performance indicators. The new AI-assisted process analyzer capability allows users to ask questions in natural language and receive immediate insights, democratizing process mining and empowering non-technical users to conduct deep analysis.

While we鈥檙e incredibly excited about what we can offer customers today, there is so much more to come still this year and beyond. That said, we will increase our target of delivering more than 100 generative AI use cases by the end of 2024. We鈥檙e only accelerating the pace of innovation, so that across our entire range of solutions, customers can find 麻豆原创 Business AI capabilities that offer businesses large-scale ROI benefits and, on an individual level, improve employees’ day-to-day work.  

Today at this year鈥檚 麻豆原创 TechEd, we are announcing multiple innovations that will revolutionize how we work and make businesses more agile and productive. With , including collaborative agents and knowledge graphs, these benefits will become even more impactful and relevant for business users across industries and geographies. Of course, we鈥檙e doing all of this while ensuring that our 麻豆原创 Business AI features are relevant, reliable, and responsible.

Read on for a detailed accounting of the new AI innovations we have released to customers in Q3 2024, and to learn more about what鈥檚 ahead.

• ERP & Finance
• IT & Platform
• Sales, Services, Marketing and Commerce
• Industries
• Procurement
• Supply Chain
• Sustainability

麻豆原创 Business AI in ERP & Finance

麻豆原创 S/4HANA Cloud Private Edition: Joule
General availability

Available since Q2, users have been able to directly navigate to business applications. Now, the integration of  in 麻豆原创 S/4HANA Cloud Private Edition allows for:

• Conversational search, allowing users to perform informational searches up to 95%* faster with quick and seamless access to the relevant content from 麻豆原创 Help Portal by asking Joule in natural language for the information they need.
• Up to 95%* faster insights into relevant business objects for purchasing experts and shipping, without the need to navigate to the application that provides the insights. This includes, for example, purchase requisitions, sales orders, and more. 

Get started .

麻豆原创 S/4HANA Cloud Public Edition: AI-assisted smart summarization
Beta release

customers can now benefit from a much smoother 麻豆原创 Fiori experience by using AI-assisted smart summarization jump-starts. This feature allows users to collaborate or follow up by extracting the essential information from the object pages of the 麻豆原创 Fiori Elements application that they use. Users can expect more than five minutes* saved per summarization.

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麻豆原创 S/4HANA Cloud Public Edition: AI-assisted easy filter
Beta release

The AI-assisted easy filter capability helps sales representatives select data of list report-based 麻豆原创 Fiori applications in natural language instead of defining filter values manually. This streamlines the filtering experience to quickly get the required information.

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麻豆原创 S/4HANA Cloud Public Edition: AI-assisted explanation of fixed asset deprecation keys
Beta release

Asset accountants can use AI-assisted explanation of fixed asset deprecation keys to analyze asset values per deprecation area. The feature explains depreciation types and calculation procedures in natural language, increasing the accuracy of financial and tax statements. Users can save up to 60 hours*鈥� of onboarding efforts per country version and 0.5 days*鈥� for analyzing asset values during period closing times.

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麻豆原创 S/4HANA Cloud Public Edition: AI-assisted configuration for U.S. tax jurisdictions
Beta release

Tax accountants can now simplify the process of maintaining U.S.-internal tax configuration with the help of Joule. The AI copilot proactively detects and generates proposals for missing compliance configurations. This ensures faster onboarding for businesses in the United States with less complexity and greater accuracy and reliability compliance with the Tax Authority website integration. Companies can expect a reduction of 50-90%* in effort to set up U.S. sales and use tax configuration.

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麻豆原创 S/4HANA Cloud Public Edition: AI-assisted financial business insights
Beta release

With the help of , employees in finance departments can analyze and summarize data from their cost center review booklet into actionable financial business insights. This empowers them with a complete picture, including root cause analyses, suggested rectifications, and written reports. It also increases efficiency by prioritizing workload, automating repetitive tasks, and recommending actions. Finance costs also get reduced via immediate visibility into critical KPIs, AI-generated reports, and instant sharing. With this feature, business users can expect to save up to 50% of their time per summarization of financial data in a review booklet and 2x accelerated decision-making.

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麻豆原创 S/4HANA Cloud Public Edition: AI-assisted localized form template management, AI-assisted merging for payment formats
Beta release

We introduced two generative AI-assisted localization apps as part of the localization-as a-self-service for 麻豆原创 S/4HANA Cloud Public Edition:

• The Manage Localized Form Templates app allows application owners to extend localized forms, such as payment receipts, to meet their specific requirements. The feature reduces the time and cost for localized form template creation by up to 95%* compared to a manual mapping in a third-party tool.

• With the Generative AI Merge for Payment Formats feature in the Payments app, business users can create customized payment formats using generative AI-generated mappings. This simplifies adapting payment formats to local standards, increasing accuracy and reducing by up to 60%* the time and cost to update payment formats compared to a manual review of the changes.

麻豆原创 Advanced Financial Closing: AI-assisted error resolution
Beta release

The AI-assisted error resolution feature helps controllers and accountants to understand the root causes of issues that arise during financial closing. It provides step-by-step guidance for fixing the error and the option to manually email the resolution to the expert in charge. By identifying errors and analyzing root causes faster, users can reduce efforts to investigate and remediate errors in automated closing tasks by up to 90%* (approximately two hours saved* per financial close), ensuring faster financial closing and lower support and compliance costs.

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麻豆原创 Signavio: AI-assisted process recommender and AI-assisted performance indicator recommender
General availability

Business process consultants can now benefit from new generative AI-powered capabilities in 麻豆原创 Signavio.

The AI-assisted process recommender capability helps to obtain preconfigured process models based on a database of more than 5,000 best practices from 麻豆原创, allowing process experts to move fast from initial exploration to process design and reducing the effort needed for a first process draft to start the process modeling. This reduces the time invested from business process consultants by up to 30%*, and the costs of designing a new process or redesigning an existing one can be reduced by 30%*.

The AI-assisted performance indicator recommender feature helps users obtain quick recommendations on process performance indicators (PPIs) relevant to their business processes and problems. This allows users to solve problems through a measurement approach based on a repository of thousands of KPIs and PPIs. The time and cost of refining process performance indicators can be reduced by up to 20%*.

麻豆原创 Signavio: AI-assisted process analyzer (text-to-insights)
Beta release

With the new AI-assisted process analyzer capability, users can ask questions in natural language and receive immediate insights that can accelerate business decisions by up to 50%*. It democratizes process mining and empowers non-technical users to conduct a deep analysis and obtain valuable knowledge.

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麻豆原创 Business AI in IT & Platform

麻豆原创 Integration Suite: Generation of integrations
General availability

Developers can now automatically generate integration flows in . Users only need to describe the integration scenario and the tool will then create an integration flow with pre-configured connectors based on their description and inputs. This helps to quickly integrate processes, services, applications, events, and data across heterogeneous system landscapes and enhance productivity.

With over 3,600 prebuilt integration flows, users can reduce the time needed for designing the integration by a factor of three*, resulting in cost savings with improved quality, as well as reduced implementation effort.

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麻豆原创 Build Code: Support 麻豆原创 S/4HANA Cloud service integration for data model and app logic
General availability

, 麻豆原创鈥檚 generative AI-based code development solution, allows developers to build applications up to 30% faster. This can reduce application development costs by up to 30%*. The integration of Joule, optimized for Java and JavaScript application development, provides a turnkey environment for coding, testing, integrations, and application lifecycle management.

Starting in Q3, Joule will help developers to generate code for integrated 麻豆原创 S/4HANA Cloud services. This is started by creating a full-stack application, importing an external 麻豆原创 S/4HANA Cloud service, and defining the data model and services with Joule.

In addition, developers can use Joule to enhance the data model they created before by adding associations between the external 麻豆原创 S/4HANA Cloud entity and local entities, and exposing the external entity to a local service via a projection afterward.

Joule also supports custom logic generation for reading the associated external entity and to perform further operations on the exposed external entity.

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麻豆原创 HANA Cloud: Generate 麻豆原创 HANA SQL statements with the SQL console for 麻豆原创 HANA
General availability

麻豆原创 HANA database application developers can use the new conversational SQL console in 麻豆原创 Business Application Studio to generate 麻豆原创 HANA SQL statements. The statements are based on the requirements specified in a special natural language prompt, which can be run directly in the SQL console. The resulting statement includes the correct 麻豆原创 HANA syntax.

Note: To use the 鈥楻un Prompt鈥� option, you must have a subscription to 麻豆原创 Build Code, the AI-powered cloud development environment based on 麻豆原创 Business Application Studio.

The new console helps increase developer productivity by up to 20%*, reducing the SQL learning curve and enabling efficient data retrieval with natural language.

For more details, look at the .

Document Information Extraction service: Product enhancements
General availability

Available on , the Document Information Extraction service helps businesses automate and scale their document processing use case end-to-end in more than 40 languages with easy extensions for any kind of document.

This upgrade includes three significant enhancements designed to streamline your document processing:

Instant learning: The premium edition of Document Information Extraction now features an instant learning capability. This allows for immediate improvements in accuracy as the service learns dynamically from user feedback. Just a few corrections from the user enable the system to adapt and enhance its performance, making document processing more precise than ever.

Enhanced document classification: With the new field type “list of value,” users can specify their own classification classes or categories; for example, to distinguish a supplier invoice from a payment advice. By providing a list of classification values when setting up a schema, they can tailor the service to automatically determine the type of incoming document according to their business processes. This results in more organized document handling, enabling faster and more accurate document processing.

Expanded availability on Microsoft Azure: To meet the growing global demand, the availability of the service has been extended to additional Microsoft Azure regions: Netherlands (Amsterdam) and U.S. East (Virginia). The service now incorporates:

• Extended schema description, to get the most out of generative AI for document processing
• New file types and improved performance, to get support for key Microsoft Office formats, including MS Excel and MS Word
• Schema import and export to easily share schemas across different projects or environments, enhancing consistency and saving valuable setup time

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Generative AI Hub in 麻豆原创 AI Core and 麻豆原创 AI Launchpad: Product enhancements
General availability

The generative AI hub in 麻豆原创 AI Core and 麻豆原创 AI Launchpad enables developers to access market-leading (LLMs) in a governed environment, run AI models securely and cost-effectively, and maximize value creation from generative AI use cases for 麻豆原创 applications.

Developers can save up to 60%* of their time spent onboarding and running AI models securely and realize higher value for new generative AI use cases through better grounding for increased relevance of the outcomes, resulting in faster user adoption. 

In the last quarter, we introduced two key enhancements:

• A new modular and intuitive orchestration builder helps create sophisticated, AI-driven applications by customizing pre-trained AI models for specific business use purposes through a guided process in the generative AI hub
• A simplified integration and customization process lets developers deliver innovations faster and more precisely, enabling more relevant and effective AI-driven solutions for better business outcomes

This feature also comes with a data masking and anonymization function for personal and sensitive data in prompts and LLM responses to enhance data privacy and compliance with data protection regulations.

A new helps integrate advanced AI capabilities into web applications. This user-friendly toolkit makes it easier to embed intelligent features such as chatbots and content generators, speeding up development and giving users more context-aware and relevant AI-generated content.

Finally, additional models from providers such as Google Cloud Platform, Meta, and OpenAI have been added to generative AI hub, and new data centers on Google Cloud Platform and Microsoft Azure are available in Europe and in the United States.

Get started with and .

麻豆原创 Business AI in Sales, Services, Marketing and Commerce

麻豆原创 CX AI Toolkit: Product enhancements
General availability

provides a comprehensive, configurable AI solution for e-commerce, marketing, sales, and service teams. It helps streamline content creation tasks and analyze data from across the enterprise. Many new AI-enabled tools are now integrated into the solution.

AI-supported image generation enables e-commerce teams to generate new product images or edit existing ones in the 麻豆原创 Commerce Cloud catalog. It reduces the time spent on image customization by up to 50%* and increases the number of product-specific campaigns by up to 30%*. With this feature, e-commerce teams can also boost engagement with eye-catching, on-brand visuals tailored to commerce catalogs and effortlessly remove backgrounds or place products on solid-color backdrops.

Intelligent Q&A support for service tickets and cases generates answers to business questions by finding and compiling the content of cases in 麻豆原创 Service Cloud and tickets from 麻豆原创 Cloud for Customers as sources for answers, saving time searching through thousands of records. Services teams can also integrate custom data sources and improve the answers provided by their unique company data.

Check out the for a complete overview of the innovations and get started with the .

麻豆原创 Business AI in Industries

麻豆原创 Intelligent Agriculture: Farming intelligence 鈥� create and process farming recommendations
General availability

麻豆原创 Intelligent Agriculture now enables agribusinesses to create and process recommendations on when, where, and how to conduct farming activities, hence sustainably increasing farming efficiency and helping them thrive. These recommendations can either be created through an API for data science models or created in the solution UI directly.

Read the and get started with the documentation .

麻豆原创 Business AI in Procurement

麻豆原创 Business Network: Intelligent invoice error classification
麻豆原创 Early Adopter Care

The invoice error classification feature helps suppliers process rejected invoices for payment in and reprocess invoices accurately to address initial invoice submission errors after categorizing them into appropriate rejection categories. Suppliers can expect to reduce invoice re-processing time by up to 40%* and reduce days sales outstanding impacted by rejected invoices by up to 10%*.

Get started .

麻豆原创 Concur: Chart of accounts mapping for product connectors
General availability

In Concur Expense, users can now access an AI-generated chart of accounts mapping for product connectors, such as QuickBooks or Xero, and decrease the setup time and effort of expense-type administration activities, including, for example, expense type creation and account code mapping.

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麻豆原创 Business AI in Supply Chain

麻豆原创 Product Lifecycle Management: Joule
General availability

麻豆原创 Integrated Business Planning for supply chain & 麻豆原创 Asset Performance Management: Joule
麻豆原创 Early Adopter Care

The conversational search feature within Joule is now supported in 麻豆原创 digital supply chain solutions, providing smooth and contextual access to 麻豆原创 Help Portal content and summarizing it in a clear answer to the question. Users can perform informal searches up to 95%* because they do not need to browse through a long search result list or read through several documents to find the correct information.

Watch this short demo of Joule in 麻豆原创 Integrated Business Planning for supply chain:

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麻豆原创 Field Service Management: AI-assisted activity summary
General availability

Dispatchers and field service workers can now generate summaries of activities, such as used spare parts, the duration of the activity, and the results of performed checklists, and improve the review and planning of service requests by up to 15%*. Activity summaries enhance dispatchers’ decision-making by providing contextual information based on similar past events. They also increase the first-visit resolution rate by helping dispatchers quickly identify the right technician for the job, and field service workers understand the job at hand better and find the right solutions faster.

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麻豆原创 Asset Performance Management: Visual inspection
Beta release

Already available to 麻豆原创 Digital Manufacturing Cloud customers since Q2 2024, the AI-enabled visual inspection feature supports asset owners, plant managers, and reliability engineers in monitoring asset conditions to identify critical asset health conditions, such as wear or failure modes, before unplanned downtimes with severe consequences can occur.

The feature collects images from cameras monitoring assets and AI-derived indicator values, displays them appropriately in the context of the asset history and other sensor data, and defines condition monitoring rules to create automated alerts and maintenance notifications based on images analyzed with AI. This enables various asset-intensive industries — such as oil and gas, mining, or railway and transportation — to reduce manual visual inspections by humans and transition from reactive or proactive maintenance toward prescriptive maintenance strategies.

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麻豆原创 Business AI in Sustainability

麻豆原创 Sustainability Control Tower: AI-assisted ESG report generation
General availability

ESG leads can now automate and embed the sustainability strategy into program execution, reducing the time needed to collect relevant ESG metrics manually from 30 minutes* to 30 seconds* and time to write reports by up to 80%*. Sustainability experts can build on 麻豆原创-delivered report templates that follow common ESG reporting standards or customize their own, generate automated draft reports for periodic internal or external reporting based on ESG data available in , and benefit from an enhanced accuracy of reported ESG data and quality of documentation.

Get started .

Philipp Herzig is chief AI officer of 麻豆原创.

*Disclaimer: Values mentioned in this blog post are estimates based on average industry benchmark numbers, from an 麻豆原创 analysis. Customers interested in a specific business case calculation can consult their 麻豆原创 account team.

麻豆原创 TechEd is our premier event for our core developer audience to explore the latest 麻豆原创 innovations and gain hands-on technical experience. This year, we have expanded the program into a continuous learning experience with both virtual and in-person opportunities.

麻豆原创 TechEd Virtual

Start your learning journey by registering for , taking place October 8-9, 2024. At the free two-day event, attendees will be the first to hear the latest 麻豆原创 technology announcements and gain technical expertise through a wide range of engaging sessions led by 麻豆原创 and partner experts.

We will offer over 100 sessions, most of them filmed live from 麻豆原创 AppHaus Heidelberg and served up in a number of different formats to provide a range of learning opportunities.

To jumpstart your learning journey, you can also join on September 23, 2024, featuring fun contests and content aligned to the 麻豆原创 TechEd Virtual event.  

麻豆原创 TechEd on Tour and Other In-Person Events

If you want to take your learning journey to the next level, join in person with .

麻豆原创 is offering four separate tour stops around the globe, organized in collaboration with 麻豆原创 user groups. Each stop will offer hands-on learning opportunities, deep-dive lecture sessions, and meet-the-experts all presented by 麻豆原创 experts, partners, and many customers.  

We are also bringing back the Developer Garage, a great opportunity for attendees to learn by doing.

The 鈥淔our on Tour鈥� stops include:

• , taking place November 12-14, 2024, in West Palm Beach, Florida, U.S.
• , taking place November 19-20, 2024, in Melbourne, Australia 
• , taking place December 1-3, 2024, in Birmingham, UK 
• , being held in April 2025 in Wiesbaden, Germany 

In addition, the 麻豆原创insider EMEA 2024 event will provide another in-person opportunity, featuring lecture sessions related to 麻豆原创 TechEd along with hands-on and deep-dive learning experiences with peers and 麻豆原创 experts.

• is taking place November 12-14, 2024, in Copenhagen, Denmark

Please today or make plans to attend one of the in-person stops.

JG Chirapurath is chief marketing officer for 麻豆原创 Business Technology Platform.

This year, we鈥檙e offering three unique and convenient event experiences so customers and partners can build skills and meet 1:1 with 麻豆原创 experts.

麻豆原创 TechEd Virtual

Held October 8-9, 2024, the free two-day virtual event will deliver 麻豆原创 technology news and announcements via global keynotes, technical and developer enablement, and engaging sessions of live and pre-recorded content for everyone across the globe. 聽

麻豆原创 TechEd On Tour

Taking place on multiple dates throughout the year, the single-day in-person events hosted across the globe by 麻豆原创 experts and the 麻豆原创 Community will deliver deep learning, networking, and hands-on engagement. Locations will range in North America, Europe, the Middle East and Africa (EMEA), Latin America and the Caribbean (LAC), and Asian-Pacific and Japan (APJ) regions.聽聽

ASUG Tech Connect, in partnership with 麻豆原创 TechEd, West Palm Beach, Florida

From November 12-14, 2024, the major three-day event stopover on the 麻豆原创 TechEd program tour will deliver visionary keynotes, technical education and enablement, expert-led sessions, and a full-scale show floor community experience with networking, best practices, and hands-on engagement.聽聽

I can鈥檛 wait to see everyone at an 麻豆原创 TechEd event in 2024! 

JG Chirapurath is chief marketing and solutions officer for 麻豆原创 BTP.

麻豆原创 has been working on AI for more than eight years now. Regardless, the past year was special as it has shown a huge leap forward in AI evolution toward generative AI. Today, more than 24,000 of our 麻豆原创 customers are using 麻豆原创 Business AI solutions in over 130 use cases. And we are aiming to provide even more businesses the opportunity to benefit from our unique data, industry, and process know-how as we work to become THE leading business AI company.

At the heart of that vision is 麻豆原创 Business Technology Platform (麻豆原创 BTP), THE platform of choice and key enabler for infusing 麻豆原创 and partner applications with business AI.

My keynote guest speaker Michael Nilles, chief digital and information officer (CDIO) of Henkel, said it well: 鈥淕enerative AI disrupts economy and society like the internet, but at lightspeed. Look at the last 10 months only — I have never discovered an exponential technology like that. 麻豆原创 BTP is our choice at Henkel for three years already; it鈥檚 our innovation platform. Together with 麻豆原创, we innovate at large scale, now also in the generative AI space.鈥�

To this end, we have been driving significant progress in the past months, from 麻豆原创 Business AI capabilities across our portfolio to the launch of our generative AI co-pilot Joule. The are another testament to the innovation 麻豆原创 provides.

Build In, Not Bolt On

We believe that business AI capabilities need to be directly embedded into applications and extensions. Designed with security, governance, and trust in mind, the is our new one-stop shop for developers to do exactly that. It provides ready-to-use AI services, AI runtimes, lifecycle management, as well as tooling for generative AI capabilities and business-data connectivity.

As part of the AI Foundation, our generative AI hub provides instant access to the most powerful large language models (LLM), such as Azure OpenAI Service and Falcon-40b. On the other side, it offers grounding capabilities based on enterprise data to ensure context, for example, by leveraging our new 麻豆原创 HANA Cloud vector engine that combines LLMs with relevant organizational data.

All Developers Will Be AI Developers

Today鈥檚 dynamic technology and business landscape means every developer needs to become an AI developer. For coders using Java and Javascript, our new solution increases developer productivity by offering a simplified experience with a unified lobby as an entry point, and generative AI capabilities. 麻豆原创 Build Code also takes care of identity management, audit logging, secure access, and more.

Does this sound too good to be true? Check out to see for yourself how 麻豆原创 Build Code helped to create a full-blown application in just five minutes on stage!

R鈥淎I鈥漵e the Ecosystem

More and more businesses are seeking to benefit from AI. No single company can unlock generative AI鈥檚 potential alone. A vibrant ecosystem of partners is required in order to meet growing customer expectations.

麻豆原创 is partnering with many key players that offer solutions connected to 麻豆原创 BTP. Today, partners support 90% of 麻豆原创-based implementations and offer 2,000 solutions on our store — more than 400 of these include AI capabilities. Moreover, our partner solutions also enable developers to create AI-based apps on 麻豆原创 BTP. One example is our partnership with DataRobot, announced earlier this year. We now brought its AI models to 麻豆原创 AI Core as well as its predictive and generative AI capabilities to 麻豆原创 Build.

Apart from the partner context, we also invest in AI research. The recently announced membership at the Stanford Institute for Human-Centered AI is an important step to reinforce our commitment to build ethical and trustworthy AI.

Going Forward, Looking Beyond

The future holds a lot. On our way to become THE leading business AI company we will continue to integrate AI into businesses鈥� everyday processes.

For 2024 and beyond, we are exploring multiple use cases, such as AI-supported business simulations, AI-generated business correspondence replies and follow-up actions, composable business processes, or automated generation of code. We will also support fine-tuned LLMs to make them more suitable for business AI.

This 麻豆原创 TechEd has only been a first milestone — there’s a lot more to come! Don鈥檛 miss out on your journey to 麻豆原创 BTP, THE choice for anything technology-related in an 麻豆原创 context.

Juergen Mueller is CTO and a member of the Executive Board of 麻豆原创 SE.

Last week at , we announced new 麻豆原创 BTP innovations that help organizations and developers bring their ideas to life.

Thank you to everyone who attended the event virtually and in person at Bangalore. For those who couldn鈥檛 attend live, I recommend reading the , checking out the full list of , and watching the , delivered by 麻豆原创 CTO Juergen Mueller.

Here are some of the key announcements we made.

Turning Every Developer into an AI Developer

Developers are essential to the modern world we live in, driving innovation and transformation forward across every industry. To help improve their productivity, we announced 麻豆原创 Build Code, a generative AI-powered application development solution, built on 麻豆原创 BTP and optimized for Java and JavaScript development. It delivers a turn-key environment for coding, testing, integrations, and application lifecycle management.

麻豆原创 Build Code has embedded generative AI capabilities with Joule to enable code generation to streamline application development. It also enables fusion development so developers can build rapidly, integrate seamlessly, and collaborate effectively with other developers and business experts. 麻豆原创 Build Code complements , our low-code solution that provides a unified environment to create and enhance enterprise applications, automate processes, and design business sites with drag-and-drop simplicity. .

Generative AI for Everyone

More than ever before, companies are seeking to unleash the power of AI in their applications and processes. We are thrilled to unveil , a one-stop shop for developers to build and extend applications powered by AI and generative AI on 麻豆原创 BTP. With AI Foundation, developers gain access to everything they need to quickly embed business-ready AI into their applications, leveraging the same powerful technology 麻豆原创 development teams use today. This includes AI services, generative AI management, AI workload management, access to business data and context, and lifecycle management based on 麻豆原创 AI Core, 麻豆原创 AI Launchpad, and 麻豆原创 HANA Cloud. .

Later this quarter, AI Foundation will include a new generative AI hub capability in 麻豆原创 AI Core for accessing a broad range of large language models (LLMs) from third-party providers. With the hub, developers will be able to use tooling for prompt engineering and experimentation to accelerate generative AI application development.

AI-Powered Business Data

麻豆原创 believes every data professional should have seamless and scalable access to mission-critical business data. To this end, we鈥檝e announced new innovations that empower developers to build intelligent data applications with 麻豆原创 HANA Cloud but also brings new capabilities that not only enhance the with 麻豆原创 Datasphere.

Good data is the foundation of good AI, and 麻豆原创 HANA Cloud continues to pioneer innovation in the data space by adding new vector database features to its multi-model offerings at no additional cost. Vector datastores manage unstructured data — such as text, images, or audio — to provide long-term memory and better context to AI models. This makes it easy to find and retrieve similar objects quickly. For example, users can search for suppliers based on the language in their contracts to examine payment history and trace individual orders. These powerful new vector database features enhance interactions between LLMs and an organization鈥檚 mission-critical data. The innovation puts 麻豆原创 developers at the forefront of delivering radically new levels of data insight within a secure, private framework that uses industry-specific customer data to reduce hallucinations.

New semantic onboarding capabilities within enable customers to deliver business-ready data products at an accelerated pace. With the powerful semantic layer, organizations can build data products that maintain business context and application logic and forego the time-consuming tasks of extracting business data, creating data pipelines and rebuilding data context that was lost. .

Enterprise Automation

Integrating, analyzing, and automating end-to-end business processes is critical in today鈥檚 digital landscape. To help customers stay ahead of the competition, 麻豆原创 offers , one solution that combines the value of , , and . At 麻豆原创 TechEd, we previewed new generative AI capabilities to simplify creating processes and integrations and to recommend pre-built content. Deeper integrations across the three technologies helps companies easily mine non-麻豆原创 processes to better analyze, integrate, automate, and monitor business processes across their environment.聽.

See you at 麻豆原创 TechEd in 2024!

JG Chirapurath is chief marketing and solutions officer for 麻豆原创 BTP and Artificial Intelligence at 麻豆原创.

In an interview, Mueller shares 麻豆原创鈥檚 amazing generative AI journey with partners and customers that鈥檚 supercharging generative AI development and how companies operate.

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Q: What are the top three things that developers, IT professionals, and business leaders need to know about the at 麻豆原创 TechEd 2023?

A: First, we want every 麻豆原创 ecosystem developer to become an AI developer and we are rolling out new tools, leveraging generative AI, to help them build and deploy intelligent data applications at scale.

Second, we are simplifying interactions between large language models (LLMs), enabling developers to implement intelligent data for generative AI applications. By expanding 麻豆原创 HANA Cloud database capabilities with a vector engine, developers can combine the generic LLMs and gain the context they need with company-specific real-time data.聽

Third, is the choice for infusing 麻豆原创 and partner applications with AI. By embedding generative AI into our solutions and giving developers, partners, and customers access to all our technology and learnings through 麻豆原创 BTP, 麻豆原创 is the go-to partner for business AI and generative AI-powered enterprise applications. We already have 22,000 live 麻豆原创 BTP customers in the cloud.

Lastly, our ecosystem is critical for all our efforts to provide our customers with beneficial innovations for their digital transformation. We have 2,100 partners on 麻豆原创 BTP, and there are 2,000 innovative partner solutions on the today. More than 360 of these solutions offer AI capabilities that drive business value.

Can you give us a snapshot of the major announcements this year?

Each of these advancements reflects 麻豆原创鈥檚 commitment to become the No. 1 generative AI enterprise application ecosystem together with our partners and supported by our platform, 麻豆原创 BTP.

• 麻豆原创 Build Code offers AI-powered productivity tools for developers. It鈥檚 an exciting evolution of the 麻豆原创 BTP low-code solution that we launched last year. Our unified lobby of tools enables enhanced collaboration between citizen developers, professional developers, and IT departments. Developers will be able to create new applications or extensions for 麻豆原创 solutions using our generative AI assistant Joule to generate code, create data models, and test data for applications.
• New vector capabilities for 麻豆原创 HANA Cloud allow developers to enrich business data with unstructured data including text, images, and audio. This is a unique offering from 麻豆原创, as customers will be able to combine the intelligence of LLMs with their business-specific data to provide accurate, relevant, and insightful responses. is the database management foundation of 麻豆原创 BTP.
• AI Foundation on 麻豆原创 BTP is our one-stop-shop for developers to build business-ready AI applications and extensions on 麻豆原创 BTP. From ready-to-use AI services to AI runtimes and lifecycle management, we鈥檙e providing the tools developers need to manage enterprise-grade generative AI with security, governance, and trust. As part of AI Foundation we will also include a generative AI hub in 麻豆原创 AI Core to provide developers with instant access to a broad range of LLMs from different providers.
• Learning is foundational to 麻豆原创鈥檚 ambitious goal to upskill 2 million professionals by 2025. Our newly announced role-based certification for ABAP Cloud back-end developers will drive expert skillset growth for agile cloud business transformation. Moreover, we鈥檝e supplemented the with a new , which explains how to build and implement business applications on 麻豆原创 BTP.

How do 麻豆原创 solutions help developers, data scientists, business users, and other professionals power their personal success and organization鈥檚 growth?

麻豆原创 is already at the forefront of generative AI advancements. Over 24,000 cloud customers are actively using more than 130 AI scenarios built on 麻豆原创 BTP. We have the business data, processes, and customer relationships, and we believe that most business AI capabilities will be consumed in an embedded way.

麻豆原创鈥檚 unique access to business data and our industry depth — together with our foundation in ethics, data privacy, compliance, and security — mean that we鈥檙e able to deliver unparalleled automation and seamless human-machine collaboration capabilities to our customers.

Most important, 麻豆原创 Business AI is relevant, reliable, and responsible. In our trusted environment, people are creating relevant AI use cases in the context of their organization鈥檚 real-world business processes and embedded into their daily 麻豆原创 systems. These use cases are reliable because they鈥檙e based on an understanding of both the business data and what it means within the business process.

Finally, our AI is responsible as it is built on leading ethics and data privacy standards.

How are customers and partners generating business results from 麻豆原创 BTP generative AI capabilities?

Many of our partners are pioneering multiple generative AI use cases with our support.

麻豆原创 partner and customer HCLTech is using AI technology to simplify daily tasks like recruiting in 麻豆原创 SuccessFactors software for speed and efficiency in finding the best job candidates. They鈥檙e developing a catalog of generative AI-infused processes across industries and line-of-business use cases. India鈥檚 Mahindra & Mahindra, which has automated more than 250 business processes in just three months using 麻豆原创 Build Process Automation, has piloted two generative AI use cases.

In Brazil, social enterprise startup SDW brings clean water to over 20,000 people. The company used 麻豆原创 Build Apps to develop an app that speeds up field worker collaboration and analyzes accurate data to help measure the impact of its services against KPIs, proving ROI that boosts investments for growth. Henkel is co-innovating with 麻豆原创 on its first large-scale generative AI use case, combining natural language processing, simulation, and planning to make decisions faster.

Can you preview what developers should expect from 麻豆原创鈥檚 generative AI vision as we head into 2024?

We are building AI capabilities, including our new generative AI copilot Joule, into our cloud-based enterprise applications. This includes 麻豆原创鈥檚 just ask feature in , available in Q1 2024. We鈥檒l also continue expanding access to LLMs in the generative AI hub. Watch for more generative AI use cases that will be released in the first half of 2024.

麻豆原创 is on a mission to boost developer productivity. Last year at 麻豆原创 TechEd we released , a unified low-code environment that enables people with any skill level to create web and mobile apps, automate processes, and design business sites through intuitive drag-and-drop simplicity.

Today, we are taking a giant step forward to empower developers. We are excited to announce , an application development solution that draws on the power of 麻豆原创鈥檚 new generative AI copilot Joule by embedding AI-based code generation to create data models, app logic, and test scripts.

A true game-changer for all 麻豆原创 developers, 麻豆原创 Build Code offers a turn-key environment for coding, testing, integrating, and managing Java and JavaScipt application life cycles by bringing together key design and runtime services and tools in a single offering.

麻豆原创 Build Code includes pre-built integrations, APIs, and connectors to 麻豆原创 and non-麻豆原创 systems, guided templates, and built-in 麻豆原创 developer best practices to accelerate development. 麻豆原创 Build Code enables fusion development, so developers can innovate faster by collaborating with other developers and business experts.

Let鈥檚 look at what developers can accomplish with 麻豆原创 Build Code.

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麻豆原创 Build Code will enable rapid development and has all the features we require to build a full stack solution powered with generative AI, including pre-configured runtime and design time capabilities, enabling shorter lead time to market.

Nilesh Mhatre, CIO Americas, IMEA and Global Technology Center, Henkel

Supercharge Development with Generative AI

Envision this: Developers rapidly build business applications using AI-based code generation from natural language descriptions. 麻豆原创 Build Code empowers 麻豆原创 developers to streamline Java and JavaScript development with built-in 麻豆原创 guidance and best practices.

• Use code generation with Joule to generate application logic, data models and sample data conforming to app extensions, and test scripts
• Leverage guided experiences to quickly build 麻豆原创 applications and extensions
• Simplify onboarding with automated environment setup and one-click provisioning and streamline application lifecycle management for developers

Tailored for 麻豆原创 Development

With 麻豆原创 Build Code, 麻豆原创 developers become productive by seamlessly connecting heterogeneous landscapes of applications, data, and processes across all 麻豆原创 and non-麻豆原创 assets.

• Extend 麻豆原创 S/4HANA as well as other 麻豆原创 and non-麻豆原创 systems easily, leveraging integrations, APIs, events, and business services via the service center
• Safeguard assets with proven security from 麻豆原创 BTP for authentication, authorization, and data protection
• Access prebuilt templates to build and personalize full stack, UI, and mobile extensions

Enhanced Fusion Development

Collaboration is key for development projects and 麻豆原创 Build Code stands out for its fusion development capabilities, enabling developers and business experts to work together. 麻豆原创 Build Code also has built-in governance that unites stakeholders to drive better business outcomes securely.

• Enable fusion team development to solve problems faster by sharing all or specific application components, such as user experience (UX), business logic, and processes
• Facilitate developer choice for application and extension development, with flexibility to use 麻豆原创 Build for low-code, 麻豆原创 Build Code for Java/JavaScript, and ABAP environment
• Ensure unified governance and simplified application lifecycle management centrally in a secure manner

Learn more about 麻豆原创 Build Code

Developers everywhere must become more productive and collaborate effectively to achieve meaningful business outcomes. With generative AI-based application development, 麻豆原创 Build Code simplifies the developer experience and provides the capabilities and power to drive the next wave of developer ingenuity.

• Visit for more details
• Watch our on what’s new and what’s next with 麻豆原创 Build Code and 麻豆原创 Build low-code solutions
• Dig deep into 麻豆原创 Build Code: read

JG Chirapurath is chief marketing and solutions officer for 麻豆原创 BTP and Artificial Intelligence at 麻豆原创.