Generative AI is increasingly finding practical applications, moving from theoretical novelty to a tool used in high-stakes professional environments. A striking example recently emerged not from a boardroom or development lab, but from the world of professional sports. Seattle Reign FC head coach Laura Harvey successfully used ChatGPT to help formulate game-winning tactics, providing a compelling case study for any professional whose job depends on strategy and verifiable outcomes.
According to a report from The Seattle Times, Coach Harvey, looking for an edge after a difficult season in the National Women’s Soccer League, turned to the AI model “on a whim.” She posed a general query: “What formation should you play to beat NWSL teams?”
The model’s response was surprisingly specific, listing formations for every team in the league. For two specific opponents, it recommended a “back-five (defense).” This was a strategy Harvey herself had only used in limited, end-of-game scenarios.
The crucial next step was not blind implementation. Instead, the AI’s output served as a starting point for human expert analysis. As Harvey noted on the Soccerish podcast, she and her staff “researched it.” They “did a deep dive on it and thought about how we could play it.”
The team adopted the AI-suggested, human-vetted strategy. “We went for it, and we liked it. It worked. We won the game,” Harvey stated. The question is whether this is a victory for AI.
A Tool for Brainstorming, Not a Replacement for Expertise
The admission of using a public, frontier AI platform for strategy has drawn some discussion from sports fans across the spectrum of AI familiarity. Some observers might view turning to ChatGPT as outsourcing a prime duty of a head coach: a defensive formation. This perspective, however, may misinterpret the tool’s function in this case. The AI was not treated as an infallible authority but as a brainstorming partner.
Coach Harvey and her staff performed the necessary legwork to validate the idea and bring it to fruition. The AI’s suggestion was the catalyst for research, not a directive that bypassed human expertise. It sounds like the heavy lifting was performed by the coaches.
Some defenders of the practice labeled this use case as “analytics”; however, it probably falls short of how that term is typically used in professional sports, which often involves proprietary data sets and complex statistical modeling. The AI’s output was likely based on general, publicly available soccer strategy information. The true “analytics” likely happened when the coaching staff took that suggestion, researched its viability against specific opponents, and integrated it into their own system.
The result for the Reign has been a more “fluid” and “unpredictable” team, which is now fourth in the league and heading to the playoffs—a significant turnaround from their 13th-place finish last year.
Analysis for the Modern Professional
This story serves as a powerful analogy for the opportunities and risks facing professionals, managers, and specialists across all industries.
The soccer coach’s challenge—outmaneuvering competitors—is not unlike the challenges of drafting compelling client proposals, protecting sensitive data, or developing new strategic initiatives.
Benefits
Breaking Cognitive Ruts: The AI suggested a strategy that the experienced coach had not seriously considered for traditional play. For employees, AI could similarly propose alternative marketing angles, identify non-obvious market segments, or brainstorm new features for a product that human experts might initially overlook.
Strategic Ideation: The tool provided a list of options, allowing the human experts to select the most promising one for further research. This mirrors using AI to generate a first draft of a project plan or a list of potential vendors, which professionals can then refine.
Competitive Advantage: The Reign reportedly became “the hardest team to prepare for” because of their new unpredictability. In business strategy, leveraging AI for new perspectives could lead to stronger, more resilient operational plans or marketing campaigns that competitors find difficult to anticipate.
Challenges and Risks
Confidentiality: This remains the paramount concern for any professional. Coach Harvey’s query was general. An employee, however, might be tempted to input confidential company information, sensitive customer data, or internal strategy notes to get a more specific output. Doing so on a public-facing model could breach company policy, violate data privacy laws, and feed proprietary information into the model’s external training set.
Data Integrity and Hallucinations: The AI’s advice to Harvey was likely based on general soccer blogs and public data. It “spurt out” formations. This lack of data transparency is a serious risk. An AI tool could “hallucinate” market data, misstate key facts for a report, or invent sources. The output is not guaranteed to be accurate. A rationality filter needs to be applied before nay action.
The Verification Burden: The AI’s suggestion did not save Harvey and her staff work; it created a new research task. They had to “do a deep dive” to verify the tactic’s viability. For an analyst, an AI-drafted report does not replace the analyst. It creates a document that must be rigorously fact-checked, verified, and edited by a qualified human who must take ultimate professional responsibility for the final product.
Managing Client and Stakeholder Perceptions
This same spectrum of reactions seen in sports fans often applies to clients, stakeholders, and … bosses. Some may be all aboard the “AI hype train,” ready to hand over the coaching reigns and expecting instant, flawless results.
Others might fundamentally distrust service providers who implement AI in workflows—whether for brainstorming, preliminary drafting, or polishing—even if the results demonstrate a significant boost in quality or efficiency.
Clear communication is necessary. Professionals must manage expectations by conveying what AI is and what it is not. AI is not magic, and it comes with verifiable risks. When governed by human expertise, however, it can be a key tool for efficiency and creative problem-solving.
A Case Study for Prudent Adoption
The Seattle Reign story demonstrates a compelling model for using generative AI: as a brainstorming partner, not an oracle. The AI’s role was to provide a novel suggestion. The humans’ role was to use their expertise to validate, adapt, and implement that suggestion, taking full ownership of the successful outcome.
Ultimately, the path forward for professionals is not to rush into adopting AI for every task, nor is it to ignore its potential. The most prudent approach involves thoughtful experimentation to find specific, high-value use cases within one’s own workflow to achieve one’s goals.
Like the Reign’s coaching staff, professionals should collaborate with colleagues to act as a critical check on AI-generated ideas. Coach Harvey did not reveal any “bad” ideas that the LLM might have output, but imagine if the model recommended pulling the goalie in the first period to add another offensive player. Hopefully someone would have raised a yellow card.
Learn to recognize “AI slop” and avoid being the link in the chain that hands over bad AI output. Remember, there was a day when the best Yahoo! search results might not appear until page 3 or GPS would randomly recommend a u-turn on the interstate. Sanity checks are vital with tech.
Success in AI will be found not in replacing human judgment, but in augmenting it, with professionals always taking final ownership of the process and the outcome. A willingness to safely experiment with the tool is half the battle.
Coach Harvey’s AI curiosity has paid off so far.
Disclaimer: This is provided for informational purposes only and does not constitute legal or financial advice. To the extent there are any opinions in this article, they are the author’s alone and do not represent the beliefs of his firm or clients. The strategies expressed are purely speculation based on publicly available information. The information expressed is subject to change at any time and should be checked for completeness, accuracy and current applicability. For advice, consult a suitably licensed attorney and/or patent professional.