Seattle NWSL Coach Uses AI to Set Game Tactics: How Your Sports Organization Can Keep Up With Your Rivals

The recent admission by a professional soccer coach that she used AI to shape her tactical decisions is a game-changer for the sports industry. Laura Harvey, head coach of the Seattle Reign FC (NWSL), is probably not the first coach to have used ChatGPT to help gain an on-field advantage, but she might be the first to have openly admitted it. What does your organization need to know about this latest development in the evolution of sports – and what should you be doing to keep up with your opponents?

Seattle Coach Leans Into AI – And Gets Results

Coach Laura Harvey just publicly disclosed on the Soccerish podcast that she used ChatGPT for on-field tactical experimentation. Specifically, she described starting by entering prompts such as “What is Seattle Reign’s identity?” but then shifted to “What formation should you play to beat NWSL teams?” The coach, a three-time NWSL coach of the year, said that AI suggested that her team should adopt a “back-five” defensive formation against two particular opponents.

Although she emphasized that AI did not provide detailed playing instructions (“it didn’t tell you how to play it … or what to do in it”), she and her coaching staff researched the suggestion, implemented the new formation, and reported an improvement in game outcomes.

The Reign had finished near the bottom of the league in 2024 but now sits in fourth place in 2025 as it heads into the final season stretch. Does that suggest the AI-recommended tactical shift may have contributed to their recent rise in the standings?

Broader AI Applications in Sport

Here’s how AI is being deployed across three key domains relevant to sports industry execs.

Game Tactics and Strategy

AI is perfect for ingesting large volumes of historical and real-time match data and then simulating multiple scenarios to support tactical decision-making (such as predicting opponent strategies, simulate formations, etc.). In fact, an academic paper from 2024 describes in detail the ways in which soccer clubs can take advantage of AI’s power.

Research shows AI models collaborating with elite soccer clubs to recommend formation tweaks (corner-kick routines, opponent counter-tactics) and outperforming conventional setups ~90% of the time in testing. Many clubs, in fact, are using “collective dynamic” models to track movement patterns and inform in-game adjustments.

Player Personnel Management and Recruitment

AI can be used to filter and identify potential talent. A recent article describes how a national sporting program and Google Cloud partnered up to analyze decades of scouting reports in an effort to surface hidden gems. Even more interesting, studies on “blind scouting” show AI can anonymize players to reduce human bias, focusing scouting attention on tactical and technical merit rather than physical attributes.

A scouting industry source claims up to 65% of professional scouts at the highest levels of English soccer believe AI will affect their role within five years. What does this mean for the industry? AI technology has the power to expand reach, accelerate filtering, and support ROI on talent investment – especially for clubs with constrained budgets. It can help transform scouting and recruitment from a “gut-feel” model to one fueled by augmented intelligence.

Health, Load Management, and Longevity

AI is increasingly being applied in training and sports science. It can combine GPS tracking, biometrics, and genomics to forecast injury risk and optimize return-to-play scenarios. Reports show that clubs are already leveraging AI to manage external load. The technology is optimized for monitoring metabolic and immune markers to keep players healthy through congested schedules.

Best Practice Tips for Executives

If you’re leading a club, league, or technology provider as we’re entering this new era, here are some recommendations to make sure you implement AI into your sporting strategy in a responsible and effective way:

Define clear use-cases and value-metrics

• Start with a narrow scope. It’s better to begin with formation simulation for your next opponent, not “let’s have AI pick our entire game plan.”
• Establish KPIs. Do you want to see change in points or goals scored in a game over a certain period of time, an improvement in substitution success rate, a reduction in injury days, a more consistently successful recruitment rate?

Embed human + machine workflows (not “machine replaces coach”)

• Ensure coaches/GMs remain central. AI should support, not replace, human judgment.
• Create a “tactical validation loop”: AI will propose an option, coach and staff will evaluate it, and then humans will adapt and personalize it before implementing it on the field. Harvey said that she and her staff did a “deep dive” on the AI suggestions to see how they would fit her team and the games themselves before deciding to implement them.
• Maintain coaching credibility and buy-in by positioning AI as an “assistant” – not the decision-maker.

Invest in data infrastructure and orchestration

• High-quality data (player tracking, opponent video, performance logs) is a prerequisite. Without clean inputs, AI suggestions are weaker.
• Connect your tactical, performance, and recruiting data systems so information can flow across all areas and create shared insights.
• Remember, using the AI-backed formation in Seattle required that the coaching staff “deep-dive” into how to play it – the data alone would not suffice.

Keep good governance, transparency, and ethics in mind

• Be explicit about limitations. AI models make predictions and aren’t perfect. Treat them as probabilistic support tools.
• Address bias and privacy issues before you integrate AI solutions into your organization. Especially in scouting or biometric applications, ensure ethical frameworks are in place.
• Protect your data. It’s unique, and has its own unique value. Take all necessary steps to protect it from your competition.

Scale innovation thoughtfully

• Pilot your AI tactics in low-risk contexts (such as minor tactical shifts, training simulations, etc.) before investing large budgets or making public claims. Have a good proof of concept before a full roll-out.
• Monitor the operational impact of any new model. Are staff actually using the AI suggestions? Has adoption changed decision-making workflows? Are players skeptical of your AI use and need to be educated about the hows and whys?
• Encourage cultural change. Success is not just about tech-deployment, but it requires a mindset of experimentation and continuous innovation.
• Stay abreast of AI developments, especially given how rapidly it is changing and improving. You need to understand and be willing to experiment with the latest AI products – because you know your competition will.

Consider your communications and branding strategy

• Public disclosures like Harvey’s use of ChatGPT are double-edged at this point in time: they signal innovation, but invite scrutiny from those who don’t understand or trust AI.
• Prepare an internal and external communications strategy. Your players and staff need to understand how AI is being used, what it doesn’t do, and how it fits into the team’s strategy. And you should consider the best way to release the information publicly so you can control the narrative.

Conclusion

To stay informed, subscribe to Fisher Phillips’ Insight System to receive the latest updates directly to your email. For specific guidance or questions, contact your Fisher Phillips attorney, the authors of this Insight, or any member of our Sports Industry Group or AI, Data, and Analytics Team.