Players are experimenting with generative AI to analyse unfamiliar opponents, organise life on tour and interpret performance data—but doubts over accuracy, privacy and the erosion of human judgment are dividing the locker room.
Long before a professional tennis player walks onto court, another contest has already begun. Coaches study serve locations, preferred rally patterns and performance under pressure. Analysts review video footage and statistical databases. Players discuss where an opponent is most likely to attack and how their behaviour changes at decisive moments. Generative artificial intelligence is now entering that private world of preparation.
Some of the sport’s leading players have begun using conversational AI tools to research opponents, condense information and manage the administrative demands of life on tour. Others regard the technology with suspicion, questioning whether an automated system can reliably interpret a sport shaped by instinct, emotion and rapid tactical adjustment. The result is an emerging generational divide—not simply between younger and older players, but between those willing to use AI as another source of information and those who fear that its convenience may encourage misplaced confidence.
Former US Open champion Bianca Andreescu recently turned to ChatGPT before facing an unfamiliar opponent on the lower-level International Tennis Federation circuit. With little video footage available, she asked the system to search for information and produce a detailed assessment. Andreescu said the preparation appeared to work well, although the episode also illustrated the appeal of AI most clearly: it can rapidly assemble a preliminary scouting report when traditional resources are limited.
Jessica Pegula said she had heard of another tour player using ChatGPT and similar tools to investigate future opponents. Rather than following the resulting advice unquestioningly, the player reportedly used it as a reference point—comparing its conclusions with information from coaches, video and conventional analysis. That distinction is crucial.
A generative AI system does not watch tennis or understand tactics in the way an experienced coach does. It produces answers from patterns in the information available to it, and those answers can be incomplete, outdated or confidently incorrect. A scouting report may accurately identify that a player prefers a cross-court forehand, yet fail to recognise that the pattern changes on a particular surface, against a left-handed opponent or when protecting a lead.
Naomi Osaka has publicly questioned the wisdom of relying on such systems, asking what happens when ChatGPT’s analysis is wrong. Her concern captures the central risk: an inaccurate restaurant recommendation may be inconvenient, but faulty tactical advice before an important match can influence a player’s positioning, shot selection and confidence. The adoption of generative AI is taking place alongside a more established data revolution in professional tennis.
The ATP launched its Tennis IQ analytics platform in 2023, giving players access to historical and live match data, interactive court graphics and detailed measurements of shot quality and patterns of play. Its scouting section allows players and coaches to examine opponents’ tendencies and compare individual performance with tour averages. An expanded version introduced in 2025 extended the system to about 2,000 players across the ATP Tour and Challenger Tour. It added enhanced video and scouting functions, a faster interface and the integration of physical information collected through wearable devices.
These systems differ significantly from asking a general-purpose chatbot for tactical advice. Tennis IQ is built around official match information and specialised analytical models, while conversational AI may pull from a much less controlled collection of sources.
Nevertheless, both technologies are changing expectations around preparation. Detailed performance analysis was once concentrated among elite players who could afford large travelling teams. Wider access to centralised data can narrow that information gap, giving lower-ranked competitors insights previously available mainly to the sport’s wealthiest stars. For players competing outside the major tours, that potential is particularly significant. Many travel with only one coach—or without a permanent coach at all. They may encounter unfamiliar opponents at short notice and have neither the time nor the staff to study hours of footage. An AI-generated summary can provide a starting point within seconds.
Yet information alone does not create a successful game plan. A coach must decide which tendencies are meaningful, whether a player has the skills to exploit them and how much tactical detail can be absorbed without creating confusion. Tennis remains unusually resistant to rigid instruction because players are alone once the point begins. They must recognise changes, control their emotions and make decisions without pausing to consult an analyst. A technically accurate report can still be useless if it overloads the player or encourages them to abandon their natural strengths.
The most effective use of AI may therefore be as an assistant rather than an authority. It can identify patterns, summarise large datasets and suggest questions for a coaching team to investigate. Human specialists must still test those conclusions against video, recent form, surface conditions, injury information and personal experience. Away from competition, players are using the technology in less consequential but equally revealing ways.
Emma Raducanu has described herself as a frequent ChatGPT user and even asked the system to create a personalised retrospective of her conversations, modelled on the annual summaries produced by music-streaming platforms. She said its assessment of her personality was surprisingly accurate. Other players use AI to draft emails, arrange information, seek fashion suggestions or handle the numerous small decisions created by constant international travel. A professional tennis career involves visas, accommodation, scheduling, sponsor commitments, media appearances and communication with a large support network. Tools that reduce that administrative burden can help players preserve attention for training and competition.
This everyday use may ultimately spread more quickly than AI-generated coaching. Writing a routine message or summarising travel options carries relatively limited risk. Trusting an automated system to diagnose a technical weakness, evaluate an injury or recommend a tactical strategy demands far greater confidence in the accuracy of its data and reasoning. Privacy is another unresolved issue.
Detailed performance information can reveal fatigue, injury vulnerability and strategic weaknesses. The ATP has said physical data collected through approved in-competition wearables remains confidential and accessible to players and their support teams. Those devices can track measures such as heart rate and high-intensity workload to assist performance and recovery analysis. General AI platforms may provide different protections. Players who upload private reports, medical information or proprietary scouting analysis could risk exposing sensitive material, depending on the service and its data policies.
There are broader concerns as well. Osaka and other players have questioned AI’s reliability, while some athletes have raised environmental objections associated with the electricity and water required to operate large computing systems. Others worry that constant dependence on automated answers could reduce curiosity, independent research and personal decision-making. Those concerns reflect a tension extending far beyond tennis.
Generative AI offers speed without necessarily guaranteeing truth. It can make sophisticated analysis available to more people, but it can also produce the appearance of expertise without the discipline required to verify its conclusions.
Professional sport exposes that contradiction with unusual clarity because results are immediate and public. An incorrect prediction cannot be hidden behind a presentation or revised later. The player either responds effectively on court or does not. The technology is also transforming how spectators experience the sport. Wimbledon’s 2026 digital platforms incorporated AI-powered tools that identify important match moments and continually estimate each player’s probability of victory by combining current statistics, historical information, expert input and match momentum.
As fans receive more automated interpretation, players are increasingly surrounded by models assessing their chances, analysing their decisions and converting every movement into data. That may create a sport that is better understood statistically but also more intensely scrutinised. A player’s instinctive tactical decision can be compared instantly with algorithmic expectations, while private performance patterns become valuable information for opponents, broadcasters and technology companies. The central question is therefore not whether AI will become part of professional tennis. It already has.
The more difficult question is how much authority it should receive. Players who reject the technology entirely may overlook useful information and fall behind opponents with stronger analytical preparation. Those who trust it too readily risk building strategies around flawed or decontextualised conclusions. The likely future lies between those extremes: AI gathering and organising information, coaches interpreting it, and players retaining final responsibility for what happens on court.
Tennis has always combined calculation with intuition. Generative AI may alter the balance, but it cannot remove the sport’s defining uncertainty—the requirement that one person, under pressure and without certainty, must still decide where to hit the next ball.
