The Wi-Fi password at the SAP TechEd conference in Berlin last week encapsulated the new mission of the global leader in enterprise resource-planning software: GetReal2025. The slogan captured the quest that SAP unveiled at TechEd: to bring AI into everyday business reality.
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.
SAP used the event, marketed under the theme “Where ideas get real”, 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, SAP aimed to show a working example of AI at scale, handling everyday complexity inside one of the world’s largest software organisations.
SAP chief technology officer and chief AI officer Philipp Herzig told Gadget at TechEd that the clearest evidence of maturity came from within SAP itself. The company’s 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.
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“If you look at AI at scale, what is really real and what is working very well, just look at Joule,” he said. “It’s used by more than 30,000 employees every month, about a third of the SAP 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.”
Herzig said Joule had become the company’s single interface for daily tasks.
“You 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 one percent, which is phenomenal when you think about the size of the company.”
That success set the stage for TechEd’s central announcement: an AI model called SAP-RPT-1, short for Relational Pre-trained Transformer. The model introduces a new class of artificial intelligence: 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.
SAP described it as a model that “can make fast and accurate predictions for common business scenarios like delivery delays, payment risk, or sales order completion”. Instead of producing text, it reads how data behaves across systems, turning business logic into predictive insight.
Herzig said SAP-RPT-1 marked the transition from incremental automation to full predictive architecture.
“We 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. What 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.
“It’s 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.”
Herzig said the greatest challenge lay in making this intelligence work at enterprise scale.
“Anyone can do a demo. Getting it enterprise-ready at scale – that’s the tough challenge. That’s why we’re solving one problem after another, each for a specific outcome in the overall stack.”
SAP executive board member Muhammad Alam tied this philosophy to the developer community.
“SAP’s announcements give developers the tools they need to deliver at the speed of AI,” he said. “Innovations across SAP’s unique flywheel of applications, data and AI put developers in the driver’s seat.”
That “flywheel” anchored the narrative of TechEd. Applications generate data; data trains predictive models; models return intelligence to the applications. Each loop strengthens the next, creating a flywheel effect of acceleration of innovation.
The company’s Build platform, its hub for enterprise application development and automation, has been expanded to give developers greater autonomy. New Model Context Protocol Servers, which allow development tools to connect directly to SAP’s AI models and data frameworks, link SAP’s environment with agentic platforms such as Cursor, Claude Code, Cline and Windsurf. A new SAP Build extension brings these capabilities directly into Visual Studio Code, giving developers access to SAP’s automation tools inside the coding environment most of them already use.
SAP also announced a partnership with n8n, an open-source workflow automation platform, linking Joule Studio agents and n8n agents to coordinate activity across applications. The collaboration turns the concept of orchestration into practice, allowing multiple intelligent systems to work together.
At the data layer, the SAP Business Data Cloud acts as the backbone of this ecosystem. A new Snowflake solution extension merges Snowflake’s managed AI and data services with SAP’s environment, allowing enterprises to balance compute and storage with governance and context. The Business Data Cloud Connect programme already includes market leaders like Databricks and Google Cloud, creating a unified foundation for business data across systems.
Herzig said this combination of systems defined SAP’s new reality.
“Over the last two years we’ve been working on making this happen at scale. Everybody can build a little app or proof of concept in quick time. The challenge is doing it for thousands of employees, across countries, in multiple business processes. For a human experience like we have with Joule, it already works really well.”
As this predictive stack takes shape, the centre of software gravity shifts toward developers and analysts who can frame the question and judge the outcome. TechEd’s message was that enterprise AI earns its keep through people who can turn models into decisions, and decisions into results.
That focus on human capability runs through SAP’s wider plan. The company announced it would equip 12-million individuals worldwide with AI-ready skills by 2030, through a partnership with Coursera that provides hands-on certification in SAP’s 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.
For all the architecture and orchestration on show, the message that carried through TechEd was that progress depends on people who understand both the data and the decisions it drives.
Herzig told Gadget that this defined what “real” now meant for enterprise AI.
“This is the stage where it becomes about outcomes,” he said. “Every system, every process, every model has to prove its value through what it delivers.”