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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Athlete performance enhancement

Introduction

The sporting industry has always been a hotspot for innovations and advancements, and it is no surprise that in the recent years there has been an increasing integration of machine learning (ML) and artificial intelligence (AI). These technologies are being applied in order to increase the performance of athletes, create important information and revolutionize the very notion of sports. From setting up individualised training schedules to minimizing on injuries to even enhancing the process of talent identification and development, artificial intelligence and machine learning have completely changed the sports business model.

Challenges

There are, however, a number of obstacles that can limit the use of AI and ML in the large-scale improvement of athletes’ performance. This includes the issue of data and privacy since it is difficult to collect enough quality data for the AI models without infringing on the athletes’ privacy. In addition, there is a possibility of technology dependence wherein important decisions are made with the help of technology and without the use of human judgment. The performance of ML models is also sensitive to the data used to train the models, where biased or unrepresentative data can lead to biased or wrong results. Finally, the integration of AI solutions entails costs, expertise, and changes that can be prohibitive to many sports organizations.

AI Solutions

These are the challenges that AI and ML are helping to solve in some very interesting ways. There are many technologies such as player tracking systems and wearable devices that gather a lot of data of athletes while at the same time ensuring that the athletes’ privacy is not infringed upon. State-of-the-art AI models are being created to give valuable information regardless of missing or contaminated information. This is why AI is combined with human intelligence instead of replacing it. For instance, in talent identification, AI can play a role in finding potential athletes but the final say rests with the human scouts. Currently, many sports teams and organizations are also implementing their own AI strategies and collaborating with tech companies to address the technological and funding issues.

Benefits

There are a lot of benefits of using AI and ML in the athletes’ performance management. They make it possible to create individualized training schedules and diet plans that can lead to a significant performance increase and overall improvement in the athlete’s health. This is also another major benefit of data analytics in sports, injury prevention since AI can help in determining the likelihood of injury by analyzing certain patterns. It can also improve on game plans since it can analyze the opponent’s strategy. In talent identification, AI can extend the search and pinpoint those who could be considered as having potential. Besides, AI can improve the fan engagement by offering key statistics and analysis in the course of the game.

Return on Investment

The ROI of AI in sports is quite impressive. In this way, AI can enhance the performance of an athlete, which in turn will enhance the performance of a team and may result in more wins and victories thus increasing revenues from ticket sales, merchandise, and sponsorships among others. Injury prevention can also reduce health care costs and loss of productivity by millions. In talent identification, AI can assist in identifying potential athletes at a much cheaper rate. Additionally, the improved fan experience may also result to increased viewer participation and commitment, which may increase revenue from broadcast rights and advertisements.

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