SAP is working with Bundesliga club TSG Hoffenheim to test how generative AI can be embedded directly into operational systems, using scouting and video analysis as real-world use cases inside SAP Sports One. The initiative focuses on applying AI to unstructured data and time-intensive workflows, with the aim of improving decision speed, consistency, and analytical depth in high-performance environments.
The announcement is significant for two reasons – it builds on SAP’s work in sport, and it shows SAP testing narrow, domain-specific capabilities inside an established operational workflow.
This is less about consumer-style generative AI experimentation and more about embedding AI into operational systems where speed, context, and expert judgment matter.
Joule-Powered Scouting Reduces Manual Analysis Effort
One of the new capabilities under evaluation is AI-generated scouting summaries. This use case is designed around SAP Joule, which can be used to ask natural-language questions and return AI-generated responses based on large volumes of unstructured scouting text.
According to SAP, the goal is to condense extensive scouting reports into more concise overviews that highlight recurring strengths, weaknesses, and performance tendencies for a given player. With this, scouts could ask questions such as, “How good is player X at creating chances?”, “Summarize the most relevant observations from the scouting reports of player X from 2026,” or “What are the strengths and weaknesses for Player X?”
Analysis
What This Means for ERP Insiders
AI will be judged on workflow impact, not features. The Hoffenheim use case shows AI applied to reduce time in specific tasks such as report synthesis and video search. Enterprise buyers should evaluate AI based on measurable workflow improvement, not model capability alone.
Natural-Language Video Search Transforms Match Analysis
Video analysis remains one of the most resource-intensive workflows in professional football. The second capability under test is a scene-finder concept for video analysis, which SAP said is being explored together with AWS Professional Services.
This capability explores how natural language input can support analysts in identifying relevant match situations more efficiently.
Instead of sifting through video material, analysts can describe the scenario they are looking for: “Show counterattacks after ball recovery that resulted in a shot on target.” Within seconds, the system can retrieve matching clips.
For clubs like TSG Hoffenheim, this capability has great potential to reduce time spent on manual video search, support more targeted tactical preparation, and improve collaboration between analysts and coaching staff as the solution continues to mature.
Analysis
What This Means for ERP Insiders
Unstructured data is becoming a primary enterprise input. Scouting reports and video mirror enterprise documents, emails, and logs. SAP’s approach signals how organizations will need to operationalize AI on unstructured data to unlock meaningful business value.
How SAP Applies AI Across Football, Esports, and Hockey
SAP has been working with sports organizations across football, esports, and hockey to improve team performance, fan engagement, and business operations. These projects show how SAP is applying data, cloud platforms, and AI to different parts of the sports business.
- FC Bayern Munich is using SAP BTP to connect more than 50 SAP and non-SAP systems into one data environment for fan engagement and stadium operations. The club has also moved to SAP Cloud ERP Private to support a clean core approach and create a foundation for future AI and predictive scenarios.
- Team Liquid is using SAP Joule on SAP BTP to turn esports data into real-time insights for players, coaches, and analysts. The project also expands natural-language access to strategy information, making it easier for teams to work with complex gameplay data.
- SAP and the NHL have built a long-running partnership around hockey operations and league data. That work includes the SAP-NHL Front Office App for iPad, as well as tools that support roster planning, salary cap management, game analysis, and coaching decisions.
Across these examples, the pattern is consistent: connect fragmented data, make it easier to use, and deliver insight inside the workflow where decisions happen.
Analysis
What This Means for ERP Insiders
Domain-specific AI will outperform generic deployments. SAP is testing tightly scoped, industry-specific use cases rather than broad AI rollouts. This suggests enterprises will see faster ROI by prioritizing targeted, process-level AI adoption over horizontal experimentation.
