Team Liquid is extending its long-standing partnership with SAP by adopting Joule, SAP’s AI copilot, to transform how it processes and operationalizes esports data, the company said. The move builds on the organization’s existing SAP Business Technology Platform (SAP BTP) foundation and reflects a broader shift toward AI-driven decision-making in high-performance environments.

The global esports organization, which competes across multiple game titles and generates vast volumes of gameplay data, is using Joule to simplify how insights are accessed and applied. SAP said the initiative is designed to help Team Liquid move from manual analysis toward real-time, AI-assisted decision-making. Thom Valks, partnerships manager at Team Liquid, said, “We’re the biggest esports organization in the world. Gaming is a huge industry, a billion-dollar industry nowadays. And esports is at the tip of the pyramid. People come to watch with thousands in stadiums like normal sports. And we are the best team in the world at it.”

From manual analysis to AI-assisted insights

Team Liquid processes extremely high volumes of gameplay data, with analysts previously relying on spreadsheets and manual review to extract insights. “We were doing everything in Excel and manually combing through the data, which turned out to be really not doable,” Valks explained. According to the company, this approach made it difficult to identify relevant patterns and respond quickly to in-game or opponent-specific scenarios. To address this, Team Liquid initially implemented SAP BTP in 2018, integrating directly with game publishers’ APIs for titles such as League of Legends and Dota. This enabled the creation of centralized dashboards and improved data accessibility across the organization. However, as data volumes continued to grow, the need shifted from data aggregation to rapid interpretation. Joule is now being used to query large datasets using natural language, allowing users to ask specific performance or strategy-related questions and receive immediate, contextual responses. For example, users can query historical gameplay data to identify optimal strategies against specific opponents over defined time periods. SAP said this reduces the time required to generate insights and enables more targeted decision-making during match preparation.

Expanding access beyond analysts

A key change with Joule is the shift from analyst-driven workflows to broader, role-based access to data insights. Previously, Team Liquid relied on multiple analysts per game to process and interpret data. With AI-assisted querying, insights can now be accessed directly by players, coaches, and other stakeholders. “Joule has taken the data that we have in our database and you can now ask it: ‘Find me the best hero to play against this team over the last six months.’ It will turn out an answer that actually makes sense. That’s revolutionary for us,” Valks said. This expands the use of data beyond centralized analytics teams and embeds decision support more directly into operational workflows. SAP said the goal is to allow non-technical users to interact with complex datasets without requiring specialized tools or skills. As a result, the organization expects to reduce reliance on manual processes while enabling faster iteration on gameplay strategies and performance analysis.

Positioning AI as a competitive differentiator

Team Liquid views Joule as a way to further operationalize its data strategy and create a competitive edge in esports. By enabling faster access to relevant insights, the organization aims to improve both individual and team performance. SAP said the next phase of the initiative involves expanding access to Joule across the organization, with the objective of embedding AI-driven insights into everyday workflows. This includes not only gameplay analysis but also broader operational and strategic decision-making. The development reflects a wider trend in which organizations are moving from data availability to data usability, particularly in environments where speed and precision directly impact outcomes.

What This Means for SAPinsiders

AI is shifting enterprise data from reporting to real-time decision support. Organizations must move beyond dashboards to enable instant, context-aware insights that can directly influence operational outcomes. Decision-making is moving closer to the point of execution. Enterprises need platforms that deliver insights to frontline users—whether in operations, finance, or supply chain—rather than relying solely on centralized analytics teams. Scaling AI requires both access and control. As more users interact with data through AI, strong governance, data quality, and workflow integration become critical to ensure consistent, reliable decisions at scale.