Show notes
Sports organizations have more data than ever. The real advantage comes from knowing which problem to solve, which data matters, and whether people will trust the answer enough to change how they work.Rohan Nagi, VP of Strategy and Analytics at Sponsor United, explains how sports analytics is moving beyond basic reporting into AI supported decision making. He discusses how teams and brands can combine quantitative and qualitative information to evaluate athletes, identify sponsorship opportunities, understand audiences, and make better business decisions.The technology is only part of the challenge. Coaches, athletes, executives, and business teams may be asked to abandon routines and instincts that have worked for years. Successful AI adoption requires clear problems, organized data, executive direction, and tools that fit real workflows.Practical Takeaways• Start with the person and the problem, not the AI tool.• Identify the information people already use and the data gaps limiting their decisions.• Build adoption around practical individual workflows before expanding across departments.• Connect daily use cases to a clear executive vision and broader business goals.Approximate Episode Highlights00:55 What Sponsor United does across sports, entertainment, brands, and sponsorships02:50 How sports moved from intuition toward data informed decision making05:35 Where traditional analytics ends and more advanced AI applications begin08:55 How teams can combine performance, medical, and personality data when evaluating players12:20 Why changing an athlete’s routine can be harder than collecting the data18:00 Why an AI strategy must begin with a clearly defined problemBest Line“The tools are just meant to help solve a problem.”Follow The Tech Trek for more conversations on AI, data, engineering, product, and technical leadership.
