A good coach rarely gives every player the same advice. One athlete might need to improve their technique, another may be working on consistency, while someone else simply needs the confidence to trust what they’re already doing.
Understanding these differences has always been an important part of coaching. What’s changing is the information available to support it.
Advances in artificial intelligence (AI) are making it possible to analyse individual performance in greater detail, helping players recognise patterns, track their progress and receive guidance that’s more relevant to their own development.
From tennis and golf to cricket, personalised coaching is taking on a new form, with technology beginning to play a greater role in how athletes train and improve.
From General Advice to Individual Guidance
Personalised coaching has always been about understanding the individual. A good coach recognises a player’s strengths, identifies areas for improvement and adjusts training as they progress.
What technology adds is the ability to collect and interpret detailed performance information between sessions.
In tennis, for example, two players might struggle with their backhand for entirely different reasons. One may have difficulty maintaining consistency during longer rallies, while another might perform well from the baseline but struggle when moving forward.
Recognising these differences allows coaching to become more focused.
AI-powered analysis can support this process by identifying patterns across matches and training sessions, helping players understand which aspects of their performance deserve attention.
Rather than working through a fixed programme, athletes could increasingly follow training plans that respond to their progress and changing needs.
Personalisation Is Already Taking Shape
Some of the clearest examples can be found in tennis and golf, where video analysis has become an established part of coaching.
In tennis, SwingVision uses AI to analyse recorded matches and practice sessions, identifying shot types and providing information about ball placement, speed and performance patterns.
Players can review their matches and identify areas that need attention, using information from their own game rather than relying entirely on general advice.
Golf technology is developing along similar lines, although the focus is often more technical.
Sportsbox AI uses three-dimensional swing analysis to examine body movements and provide detailed performance information.
The company has also introduced SAMI, an AI assistant designed to help golfers understand their swing data and identify areas for improvement.
Rather than simply presenting measurements, SAMI can respond to questions about an individual’s technique and provide guidance based on their recorded movements.
These approaches demonstrate two different aspects of personalised coaching. SwingVision helps players recognise patterns in match performance, while Sportsbox focuses on understanding an individual’s movement and technique.
Both reflect a shift towards making performance information more relevant to the person using it.
Knowing When Feedback Is Most Useful
One of the more interesting questions in coaching is how much feedback an athlete actually needs.
While detailed analysis can reveal useful information, receiving instructions after every movement isn’t necessarily the best way to learn.
A 2025 meta-analysis published in Behavioral Sciences examined 29 studies involving 1,147 participants, investigating the effects of allowing learners to control when they received feedback.
Researchers found that participants who could choose when to receive feedback generally performed better when retaining and transferring newly learned motor skills, although the results varied across studies.
The findings weren’t specific to AI, but they highlight the importance of giving athletes some control over their learning.
For personalised coaching technology, this raises an important consideration. A system may be capable of analysing every movement, but deciding when to offer guidance could be just as important as the information itself.
Some players may prefer regular feedback when learning a new technique, while others might benefit from practising independently before reviewing their progress.
Technology that can accommodate these differences could make coaching feel more natural and less intrusive.
Coaching That Develops with the Player
Personalisation also becomes more valuable when performance can be followed over time.
An athlete’s needs change as their abilities improve. A beginner might initially focus on coordination and consistency, while a more experienced player could concentrate on decision-making or refining specific techniques.
A useful coaching system should be able to recognise that progression.
Rather than repeatedly highlighting the same movements, technology could help identify when a player has developed sufficient consistency to move on to another aspect of their game.
This approach is also emerging in cricket.
Kabuni Sports is developing a similar approach to personalised coaching. Its founder, Nimesh Patel, has spoken about the importance of using technology to recognise a player’s strengths, rather than simply identifying weaknesses.
The aim is to help young cricketers understand what they’re already doing well, while providing individual guidance on where they can improve.
Kabuni’s contracted Super Coach network includes Sourav Ganguly, AB de Villiers, Shane Watson, Shreyas Iyer and Harry Brook.
Kabuni can create AI voice and likeness versions of its Super Coaches, combining personalised performance feedback with familiar coaching guidance.
For developing players, this could offer a more engaging way to understand their technique while receiving advice relevant to their own progress.
Its effectiveness will ultimately depend on how accurately the technology interprets performance and adapts its guidance to individual needs.
Understanding the Individual Beyond the Data
Despite these developments, personalisation isn’t simply about collecting more information.
Athletes bring different motivations, experiences and expectations to training. Someone returning after a long break may have different priorities from a competitive junior preparing for an important tournament.
Physical differences also matter. There isn’t one ideal technique that suits every player, and trying to reproduce a particular movement without considering the individual can sometimes be counterproductive.
This makes context important when interpreting performance information.
A change in technique may reflect fatigue, growing confidence or a deliberate adjustment rather than a problem that needs correcting.
Technology can help recognise patterns, but useful coaching still depends on understanding what those patterns mean for the person involved.
The Next Step in Personalised Coaching
As AI continues to develop, personalised coaching could become increasingly responsive to individual goals, abilities and progress.
The most useful systems may be those that recognise when a player needs detailed guidance, when they are ready for a new challenge and when they would benefit from practising without interruption.
For athletes, this could mean spending less time working through advice that doesn’t apply to them and more time concentrating on the skills that genuinely need attention.
Ultimately, the future of personalised coaching isn’t necessarily about receiving more information. It’s about making better use of the information available, giving every player the right guidance at the right time.
