Artificial Intelligence (AI) is no longer a distant future in sport — it’s here, and it’s already shaping the way coaches prepare athletes, performance teams make decisions, and athletes refine their craft. What was once confined to laboratories, wind tunnels, or elite research facilities is now finding its way into real-world performance workflows. That shift is visible across disciplines, from the ice of speed skating to the saddle of the cyclist.

Reinventing Aerodynamics at the Olympics

As Team USA prepares for the 2026 Milan-Cortina Winter Games, one of the most striking examples of AI in action comes from speed skating and bobsled programs. U.S. speed skaters are using a bespoke AI application called Slippery Fish that creates digital “avatars” of athletes and simulates aerodynamics to calculate drag and airflow effects — essentially bringing computational wind tunnel analysis to the coach’s laptop or tablet. This allows coaches to test posture and body position tweaks rapidly and cost-effectively — what once took weeks of tunnel testing can now be evaluated in a day.

Another team, USA Bobsled-Skeleton, has partnered with advanced analytics firms to sift through massive streams of sensor data to identify refined performance patterns and athlete strengths, giving coaches deeper insights into start mechanics, coordination, and run dynamics than ever before.

What’s striking about these developments is not just technical novelty, but practical integration. These tools complement — not replace — the judgment of coaches and performance staff, enabling faster feedback loops between data, training adjustments, and on-ice or on-track execution.

With this app, it’s all just done through AI… Something that maybe took a week or two to validate… now can be done in a day.” — U.S. speed skater Emery Lehman on AI aerodynamic simulation.

Cycling: Saving Watts with AI Bike Fitting

In the world of cycling, AI is already helping riders improve and quantify gains that matter. Using AI-driven aerodynamic analysis tools — originally developed for elite programs — bike fitters can now build precise digital models of riders and optimize position for aerodynamic efficiency. One cyclist reported saving dozens of watts by using an AI-based fit application that brings computational fluid dynamics (CFD) to everyday performance work.

This type of AI application dramatically lowers the barrier to sophisticated aerodynamic modeling: instead of requiring expensive wind tunnels, riders and coaches can base decisions on AI simulations derived from simple video or photo inputs. It’s a democratization of high-end performance science — one that coaches can integrate directly into training plans for riders of all levels.

AI Everywhere: Beyond Speed and Aero

These examples are just the beginning. Across sports and continents, organizations are experimenting with AI to enhance performance, strategy, health and fan engagement:

• European cycling teams collaborate with AI platforms that estimate fitness and fatigue to help determine optimal training volumes and schedules.
• Football clubs in major European leagues use real-time AI analytics to track player movements and tactics for in-game decision support.
• AI systems are assisting in injury risk prediction and player management in basketball and other team sports, helping coaches adjust training loads before issues emerge.
• Even fan experience and broadcasting are being enhanced with generative AI platforms delivering richer data storytelling during major events.

What This Means for Coaches, Athletes & Scientists

For coaches and performance teams, AI is reshaping two critical axes of their work:

1. Speed of Insight
AI models digest and model complex data far faster than traditional analysis — meaning faster decisions and more opportunities for iteration between training sessions and competition.

2. Leveling Expertise
Tools that once required specialized facilities or teams of analysts are now accessible to a broader coaching community. That democratization accelerates innovation in grassroots as well as elite environments.

For sport scientists, these developments invite deeper collaboration between domain knowledge and machine intelligence: defining the right questions, interpreting outputs responsibly, and ensuring that technology augments human experience and expertise.

And for athletes, that means being supported by data that’s both richer and more personalized — fueling performance improvements that align with ever-rising competitive standards.

AI is transitioning from laboratory curiosity to practical performance toolkits, helping coaches fine-tune technique, optimize training load, and unlock marginal gains. What once took months of experimentation is now being evaluated in near real time — and these breakthroughs are only the beginning.

Editor’s Note: This article was compiled by ChatGPT from NBC News, OregonLive, and CyclingWeekly reporting, with additional European examples, to provide a verified overview of AI applications in sports performance.