Abstract
Artificial intelligence (AI) is frequently presented as a near-universal solution for all tasks and problems in professional sport. However, the term is applied inconsistently, often functioning as a label for considerably simpler computational methods. Following Russell and Norvig's (2021) definition of AI as encompassing reasoning, decision-making, and problem-solving, this talk argues that successful adoption depends less on acquiring advanced algorithms than on organizational readiness, governance, and collaboration. Drawing on several national AI-readiness frameworks (NIST, 2023; Ontario Government, 2023; NSW Department of Customer Service, 2024) and recent practitioner guidance for sport leaders, the talk outlines three decision points organizations should address before wide ranging AI implementation. First, clarifying the motivation of the implementation and thereby distinguish adoption driven by genuine performance or hopeful efficiency gains from adoption driven by fear of missing out. Second, defining the scope of the implementation which requires deciding whether AI should be applied organization-wide or excluded from ethically sensitive domains such as athlete health data. This also includes weighing the interpretability of "black box" models and clarifying who remains accountable for AI-informed decisions. Third, assessing the impact of the implementation, which requires benchmarking AI-supported processes against existing practice in terms of efficiency and cost, while considering potential negative effects on staff expertise or athlete–coach interaction. The talk further addresses implementation-level challenges that might hinder the use of AI tools.
To put these general ideas in perspective, practical interdisciplinary applications are presented of collaborations projects with the Dutch short track speed skating national team. The projects were both part of the last two Olympic cycles and helped the national by optimizing training scheduling and relay performance. Lessons learned on cooperative implementation are presented and contrasted with the aforementioned guidelines.
Taken together, while AI offers substantial potential for professional sport and sport scientists, organizations are encouraged to resist reactive adoption in favor of a deliberate AI action plan, supported by adequate staff literacy and strategic external partnerships. This includes a call for leaders in the sport domain to actively shape rather than merely react to AI's growth while keeping athlete welfare central to decision-making. The overarching message is to embrace AI, but with a clear plan for how and for whom.
References
National Institute of Standards and Technology. (2023, January 26). AI risk management framework. U.S. Department of Commerce. https://www.nist.gov/itl/ai-risk-management-framework
NSW Department of Customer Service. (2024). NSW artificial intelligence assessment framework. Digital NSW. https://www.digital.nsw.gov.au/policy/artificial-intelligence/nsw-artificial-intelligence-assessment-framework
Ontario Government. (2023). Ontario's trustworthy artificial intelligence (AI) framework. https://www.ontario.ca/page/ontarios-trustworthy-artificial-intelligence-ai-framework
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed., Global ed.). Pearson.
Sport Information Resource Centre. (n.d.). Beyond the buzzwords: A practical guide to AI for sport leaders. https://sirc.ca/articles/beyond-the-buzzwords-a-practical-guide-to-ai-for-sport-leaders/