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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