Tennis Strategist's Data Methods Draw Interest From Corporate Decision-Makers
Craig O'Shannessy spent years analyzing Grand Slam matches and advising professional tennis players on when to rush the net. Now his methodology—using granular performance data to challenge conventional wisdom—is catching the attention of an unexpected audience: finance executives looking for frameworks to evaluate AI-driven business decisions.
O'Shannessy, a tennis strategist and analyst for multiple Grand Slam tournaments, appeared on Wharton's "Moneyball" podcast on February 11 to discuss how data analytics are reshaping tennis strategy at the highest levels. The conversation, hosted by Wharton professors Cade Massey, Eric Bradlow, and Shane Jensen, centered on a question that translates directly to corporate finance: how do you know when the data is telling you to abandon what's always worked?
The parallel isn't subtle. Tennis players, like CFOs, operate in environments where decades of accumulated wisdom—"stay at the baseline," "don't over-invest in unproven technology"—can calcify into doctrine. O'Shannessy's work, which includes contributions to The New York Times and strategic advising at major tournaments, focuses on identifying underused tactics that data suggests should work but that players resist adopting. His signature example: serve-and-volley play, a high-risk approach that analytics indicate is underutilized relative to its success rate.
The finance application is straightforward. Corporate finance teams are currently drowning in vendor pitches about AI tools that promise to automate reconciliation, accelerate close processes, or predict cash flow with unprecedented accuracy. The challenge isn't access to technology—it's determining which innovations represent genuine strategic advantages versus expensive distractions. O'Shannessy's framework offers a template: isolate the specific situations where unconventional approaches outperform, then test whether organizational resistance is rational or merely habitual.
What makes O'Shannessy's methodology particularly relevant to finance leaders is his emphasis on "coachability"—the willingness to absorb contradictory evidence and adjust behavior accordingly. In tennis, this means convincing a player to change stroke mechanics or court positioning based on match data. In corporate finance, it means persuading a controller to trust an AI-flagged anomaly over their own pattern recognition, or convincing a CFO to restructure a planning process that's worked for fifteen years.
The Wharton podcast, which runs over an hour, explores how O'Shannessy translates raw performance statistics into actionable strategic recommendations—a process that mirrors how finance teams must convert AI model outputs into actual business decisions. The discussion touches on continuous learning, the gap between what data recommends and what practitioners actually do, and how to build organizational buy-in for data-driven changes.
For CFOs evaluating AI investments in 2026, the tennis analogy offers useful guardrails. O'Shannessy isn't advocating blind faith in analytics—he's demonstrating how to identify specific, measurable situations where data contradicts intuition, then building the case for change. The question isn't whether AI will transform finance functions; it's whether finance leaders can develop the coachability to recognize when the transformation is real versus when it's just another vendor demo that looks better on the screen than in production.
The podcast is available through Wharton's Knowledge platform and standard podcast distributors.
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