AI Enters the Race at the Winter Olympic Games

Artificial intelligence (AI) is also entering the race at the 2026 Winter Olympics in Milano Cortina, Italy, which runs through Feb. 22, bringing a layer of machine intelligence to an event long defined as a showcase of human performance.

From athlete training and broadcast production to fan engagement and judging, AI systems are increasingly embedded in how the games are prepared for, experienced and evaluated.

Training Edge for Olympic Athletes

For elite athletes, micro-second gains often separate podium finishes from near misses. That reality is driving adoption of an AI-powered performance analysis tool developed through a collaboration between Google Cloud and U.S. Ski & Snowboard. The system uses computer vision and large language models to convert ordinary video footage into detailed biomechanical insights, allowing coaches and athletes to analyze rotations, takeoff angles, airtime and landings without specialized motion-capture equipment.

The technology has already influenced Olympic preparation. Halfpipe snowboarder Maddie Mastro, known for her signature “crippler” trick, used the AI system to identify subtle flaws in arm positioning during landings that were not apparent through traditional video review. By adjusting technique based on AI feedback, athletes can make data-backed refinements earlier in training cycles, reducing reliance on trial and error as competition approaches.

“Our collaboration with U.S. Ski & Snowboard is the blueprint for a global shift in how humans move, train, and recover, moving beyond historical data to provide athletes with near real-time, prescriptive coaching,” said Oliver Parker, vice president, global generative AI, Google Cloud, in a statement.

Beyond individual athletes, the tool signals a broader shift in how national teams approach training. Cloud-based AI allows performance analysis to happen faster, more consistently and at lower cost, potentially narrowing gaps between well-funded programs and those with fewer resources.

AI Powering the Olympic Digital Experience

AI at the 2026 Olympics isn’t limited to athlete performance. The technology has also been embedded into how the games are experienced by spectators, officials and broadcasters.

Alibaba Cloud has announced a suite of AI tools built on its Qwen large language model that will be integrated into the digital ecosystem of the Milano Cortina Games. These “Olympic AI Assistants” will be deployed on the International Olympic Committee’s (IOC) global platforms to provide multilingual conversational support for fans seeking schedules, results and event information in real time via chat interfaces.

Beyond fan engagement, the same AI models will also support internal Olympic operations. On secure IOC portals, AI assistants will help National Olympic Committees access documents and guidelines through natural language queries, a first for LLMs in the Olympics’ digital infrastructure.

Alibaba’s AI initiatives also include enhanced broadcast technology. AI-powered replay systems promise faster, more insightful visual breakdowns of events across multiple sports, enabling broadcasters to deliver compelling, near-live replays that isolate athletes from complex backgrounds like snow or ice. This could help viewers better appreciate technique and speed in disciplines like freestyle skiing, figure skating and ice hockey.

From Subjective Calls to Objective Data

For sports where subjective evaluation plays a large role—such as skiing, snowboarding and figure skating—the IOC is exploring AI systems to support judges with more consistent, data-backed assessments.

According to The Conversation, AI systems being explored for Olympic judging focus on breaking down complex athletic movements into measurable components, offering officials a data-driven reference point in events where scoring has historically depended on human interpretation.

For example, AI systems can measure body angles, rotation speeds and airtime with precision that exceeds the human eye, flagging potential scoring issues or technical details that might otherwise be missed. These tools to augment human decision-making by offering unbiased metrics that reduce error and promote fairness in competitive outcomes.

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