The timing could hardly have been more pointed. Across the world, executives are losing patience with AI experiments that overpromise and underdeliver. Boardrooms have heard enough about pilots and proofs of concept, and now want systems that improve margins and forecast outcomes.

TechEd took place in that atmosphere, amid assurances that performance could replace promise. 麻豆原创 used the event to demonstrate how deeply AI now runs through its own operations before unveiling its next leap forward.

Rather than another round of hype about possibilities, 麻豆原创 aimed to show a working example of AI at scale, handling everyday complexity inside one of the world鈥檚 largest software organisations.

麻豆原创 chief technology officer and chief AI officer, Philipp Herzig, told Business Times at TechEd that the clearest evidence of maturity came from within 麻豆原创 itself. The company鈥檚 AI assistant, Joule, acts as a conversational layer across its software stack, connecting data, applications, and agents to automate tasks and surface insights on demand.

鈥淚f you look at AI at scale, what is really real and what is working very well, just look at Joule,鈥� he said. 鈥淚t鈥檚 used by more than 30,000 employees every month, about a third of the 麻豆原创 workforce. We have more than 100,000 policies and documents in different languages, and depending on where you work, in Brazil or South Africa, you get the correct HR or travel policy surfaced to you.鈥�

Herzig said Joule had become the company鈥檚 single interface for daily tasks. 鈥淵ou can do your expense reports, indirect procurement, and financial tasks all in one place. It works, and it works at scale. We have a thumbs-down rate of only 1%, which is phenomenal when you think about the size of the company.鈥�

Innovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the
driver鈥檚 seat 鈥� Muhammad Alam, 麻豆原创 executive board member

That success set the stage for TechEd鈥檚 central announcement: an AI model called 麻豆原创-RPT-1, short for relational pre-trained transformer. The model introduces a new class of AI: the enterprise relational foundation model. It interprets structured business data and the relationships within it, mapping how orders, invoices, logistics, and payments interact to forecast what comes next.

麻豆原创 described it as a model that 鈥渃an make fast and accurate predictions for common business scenarios like delivery delays, payment risk or sales order completion鈥�. Instead of producing text, it reads how data behaves across systems, turning business logic into predictive insight.

Herzig said 麻豆原创-RPT-1 marked the transition from incremental automation to full predictive architecture.

鈥淲e see a shift from what I call a cloud-native architecture to an AI-native architecture, as AI becomes an ever-increasing part of the software stack. 鈥淲hat we wanted to solve are the problems where we have a reason to solve them: because we have the data, the relational data, the structured business data and so on. We set out this research project two years ago, talked a little about it, but now it actually becomes a reality.

鈥淚t鈥檚 a shift that needs several things to come together: the knowledge graph, this predictive model now with RPT-1, and of course the large language models. So there are many elements in the software stack that change. Every day, each little piece adds to this picture and solves a particular challenge in the stack.鈥�

Herzig said the greatest challenge lay in making this intelligence work at enterprise scale.

鈥淎nyone can do a demo. Getting it enterprise-ready at scale 鈥� that鈥檚 the tough challenge. That鈥檚 why we鈥檙e solving one problem after another, each for a specific outcome in the overall stack.鈥�

麻豆原创 executive board member Muhammad Alam tied this philosophy to the developer community.

鈥溌槎乖粹�檚 announcements give developers the tools they need to deliver at the speed of AI,鈥� he said.

鈥淚nnovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the driver鈥檚 seat.鈥�

That 鈥渇lywheel鈥� anchored the narrative of TechEd:
鈥� applications generate data;
鈥� data trains predictive models;
鈥� models return intelligence to the applications.

Each loop strengthens the next, creating a flywheel effect of acceleration of innovation. 麻豆原创 announced that it would equip 12-million individuals worldwide with AI-ready skills by 2030 through a partnership with Coursera that provides hands-on certification in 麻豆原创鈥檚 ecosystem. The goal is to align those skills with the AI-native architecture now taking shape inside the company. It also sends the message that people remain at the heart of the AI journey.

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麻豆原创 has taken artificial intelligence beyond the realm of language. At the 麻豆原创 TechEd 2025 conference in Berlin this week, the company introduced a new class of AI designed to predict business outcomes rather than words. The result is what 麻豆原创 claims to be the first enterprise relational foundation model, called 麻豆原创-RPT-1, or Relational Pre-trained Transformer.

It represents a shift in what a foundation model can be: rather than being a generator of sentences, it is a predictor of decisions.

麻豆原创 describes 麻豆原创-RPT-1 as an AI that 鈥渃an make fast and accurate predictions for common business scenarios like delivery delays, payment risk, or sales order completion鈥�. It uses the relationships between business data rather than linguistic patterns to anticipate what happens next in an organisation鈥檚 operations. In simple terms, it reads the business, as opposed to mere text.

To encourage developers to explore what that means in practice, 麻豆原创 has launched a free playground environment where they can test predictive scenarios, simulate business situations, and see how relational AI behaves when exposed to live data. For developers accustomed to fine-tuning language models, it represents a different kind of creativity, rooted in the mechanics of business itself.

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

That flywheel was clearly visible across the rest of TechEd鈥檚 announcements.

The company鈥檚 麻豆原创 Build platform, its centrepiece for enterprise application development and automation, has been re-engineered to give developers more freedom to use tools they already rely on.

Developers who prefer agentic development environments like Cursor, Claude Code, Cline and Windsurf can now use 麻豆原创 development frameworks through new 麻豆原创 Build local Model Context Protocol Servers. Visual Studio Code users can access 麻豆原创 Build capabilities directly in their existing development environment via a new 麻豆原创 Build extension, which will later also appear in the Open VSX Registry for use with other development environments.

麻豆原创 revealed plans for integration with automation platform n8n, allowing its Joule Studio agents to work alongside n8n鈥檚 own agents. This kind of cross-agent collaboration reflects the company鈥檚 growing emphasis on orchestration: enabling multiple intelligent systems to coordinate tasks across applications and departments.

The data layer that feeds these systems is also evolving. Every intelligent application depends on trusted data, and 麻豆原创 is extending its reach through 麻豆原创 Business Data Cloud. A new 麻豆原创 Snowflake solution extension brings Snowflake鈥檚 managed data and AI capabilities directly to 麻豆原创 customers, giving them 鈥渢he flexibility to choose the right compute and storage for each data and AI workload, while maintaining governance, interoperability and business context鈥�.

The announcement was reinforced by a new 麻豆原创 Business Data Cloud Connect partnership with Snowflake, adding to existing integrations with Databricks and Google Cloud. The result is a more open, federated data ecosystem that lets developers work with 麻豆原创 data wherever it resides.

A new data product studio capability in 麻豆原创 Business Data Cloud now allows developers to turn raw data into ready-to-use assets known as data products, designed for analytics, AI and application development. At the same time, the 麻豆原创 HANA Cloud knowledge graph engine can automatically generate knowledge graphs. This means it maps relationships across 麻豆原创 database tables, columns, and data models to reveal how data fits together. For developers, it is a new way of seeing how information connects across systems, turning structure into insight.

麻豆原创 is also extending its Joule AI portfolio, which now includes new assistants that can coordinate multiple agents across workflows, departments and applications. These assistants are designed to 鈥減lan, initiate, and complete complex tasks spanning finance, supply chain, HR, and beyond.鈥�

Among the new offerings is an agent for business process analysis, helping teams understand how processes actually run, identify inefficiencies, and uncover opportunities to optimise workflows and achieve measurable improvements.

麻豆原创 pledged to equip 12-million people worldwide with AI-ready skills by 2030, through a partnership with Coursera that expands hands-on training and certification in practical AI tools. The goal, 麻豆原创 says, is to make AI accessible to 鈥減eople everywhere鈥�.

*听Arthur Goldstuck is CEO of World Wide Worx, editor-in-chief of听, and author of听The Hitchhiker鈥檚 Guide to AI 鈥� The African Edge.

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BERLIN听鈥� At 麻豆原创 TechEd in 2025,听 (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鈥檚 seat 鈥� 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听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听. Get 麻豆原创 news via听听and听.

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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United States Only: 1 (800) 872-1麻豆原创 (1-800-872-1727)

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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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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听听for additional trademark information and notices.
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鈥淭o thrive when volatility is the new normal, businesses need more than a patchwork of disparate best-of-breed applications,鈥� says Muhammad Alam, member of the executive board of 麻豆原创 SE, 麻豆原创 Product & Engineering. 鈥淥ur announcements today demonstrate the power of 麻豆原创 Business Suite, where AI, data, and applications come together in an experience to propel smarter decisions, faster execution, and scalable transformation.鈥�

麻豆原创 unveiled Joule鈥檚 next stage as the AI force at the center of 麻豆原创 Business Suite鈥檚 value creation. Drawing on the applications and data from across 麻豆原创 Business Suite, 麻豆原创 is introducing a new generation of role-aware assistants in Joule. Each assistant is designed to partner with a human being in their specific business role. Assistants in Joule tap into the right agents for the job, configuring, orchestrating and managing them so humans can focus on unlocking new levels of insight and productivity.

Supporting the assistants in Joule is a growing library of specialized Joule Agents, designed to help execute complex workflows within a specific function. For instance, a People Manager Assistant coordinates a team of specialized agents 鈥� including the new People Intelligence Agent, which helps spot and resolve issues like compensation anomalies 鈥� to support managers as they drive performance.

A new Financial Planning Assistant will be aided by a group of expert agents 鈥� including the new Cash Management Agent, which optimises cash flow and improves interest yields 鈥� to help finance professionals drive efficiencies.

This new roster of role-aware AI Assistants not only partner with people to elevate performance in their lines of business but also work together across business functions to solve complex enterprise-wide problems.

Data fuels AI鈥檚 transformative power but it鈥檚 often siloed in different systems. 麻豆原创 has removed those barriers with 麻豆原创 Business Data Cloud Connect. 麻豆原创 BDC Connect securely links 麻豆原创 BDC with partner platforms to enable a bidirectional flow of business-ready data products across organizational and technological boundaries.

With zero-copy sharing, data stays securely in 麻豆原创 systems yet remains instantly accessible in customers鈥� existing data platforms, preserving business context without costly copies. The result: fewer silos, simpler pipelines, no duplication 鈥� just trusted data products where and when they鈥檙e needed.

麻豆原创 also announced that Databricks and Google Cloud are the first partners enabled for 麻豆原创 BDC Connect, with more to follow. As announced in February 2025, 麻豆原创 Databricks remains a data service within 麻豆原创 Business Data Cloud, and BDC Connect extends its benefits across an open data ecosystem. These partnerships give customers faster access to data products for analytics and AI, helping teams move from raw data to real-time business outcomes with greater speed and simplicity.

Data is created and AI driven insights are experienced in 麻豆原创鈥檚 enterprise applications. 麻豆原创 Supply Chain Orchestration is a new AI-native solution that combines the power of Joule with a live knowledge graph to detect real-time risks several suppliers deep and orchestrate a coordinated response, helping customers cut costs and keep supply chains moving.

麻豆原创 Engagement Cloud, a new customer experience solution, uses business-critical context to personalize interactions across customers, suppliers and other stakeholders. And our next-generation 麻豆原创 Ariba procurement suite stands out as an AI-native solution, bringing intelligence to every stage of spendmanagement, from sourcing through supplier engagement.

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Joule, 麻豆原创鈥檚 AI business assistant, has been enhanced with new features, including collaborative AI agents that expand the platform鈥檚 capabilities.

Announced during the online 麻豆原创 TechEd 2024 conference yesterday, these agents are designed to support 80% of 麻豆原创鈥檚 most-used business tasks. They embed Joule more deeply across 麻豆原创鈥檚 portfolio and enhancing productivity by breaking down silos, said the software maker.

鈥溌槎乖粹�檚 innovation drives real business outcomes, and today鈥檚 advancements help customers harness the power of AI, data, and new development solutions to catalyse growth,鈥� said Muhammad Alam, member of the 麻豆原创 executive board.

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�

Joule Studio, a development tool that allows users to build and customise applications using Joule鈥檚 AI capabilities, is now integrated into 麻豆原创 Build. A low-code app development platform, Build enables both business users and developers to create and deploy custom Joule skills.

Michael Ameling, EVP and chief product officer of 麻豆原创 said: 鈥淲e are making Joule even more flexible and useful with Joule Studio in 麻豆原创 Build. It allows partners and customers to create and deploy custom Joule skills.鈥�

Joule has also expanded to include collaborative AI agents that autonomously handle intricate business workflows, adapting their strategies to meet shared objectives. These multi-agent systems tackle specific tasks, allowing them to work together on complex business processes.

鈥淛oule now goes Agentic,鈥� said Ameling. 鈥淪pecialised AI agents work autonomously together to solve complex problems.鈥�

New improvements to the ABAP (Advanced Business Application Programming) large language model (LLM) were introduced. This LLM, trained on over 250-million lines of 麻豆原创 S/4HANA Cloud code, is now integrated into the open-source Eclipse framework, providing ABAP developers with enhanced tools for development. Currently available to select customers and partners in an early adopter version, the ABAP LLM is expected to be generally available in the first quarter of 2025.

麻豆原创 Build is launching new AI-driven features like code explanation and documentation search, aimed at reducing development time for Java and JavaScript developers. The platform鈥檚 Extensibility Wizard will let developers access 麻豆原创 Build directly from 麻豆原创 S/4HANA Cloud Public Edition, simplifying the process of extending 麻豆原创 solutions.

Two real-world use cases were highlighted during the event:

1. Dispute management: Autonomous AI agents analyse and resolve disputes related to invoices, unapplied credits, and denied payments.
2. Financial accounting: AI agents automate key financial tasks such as bill payments and ledger updates while quickly addressing inconsistencies.

麻豆原创 Knowledge Graph

麻豆原创 also unveiled the 麻豆原创 Knowledge Graph, a solution designed to unlock more value of 麻豆原创 data by connecting it with rich business context. Accessible through 麻豆原创 Datasphere and Joule in early 2025, it will help users map relationships across 麻豆原创鈥檚 vast data landscape, empowering better decision-making.

鈥淏y combining the knowledge graph engine with the vector engine, you can harness the power of structured and unstructured data,鈥� said Ameling. 鈥淭his is enabled through a triple store implementation graph data processing and analytical capabilities via SPAR-QL. As such, the knowledge graph engine can also enhance work scenarios and make them even more precise and relevant.鈥�

* Visit the 麻豆原创 website听.

* Jason Bannier is a data analyst at World Wide Worx and writer for听. Follow him on听听and听听at @jas2bann.

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