Logo
Munich Personal RePEc Archive

The use cases for AI in Australian sport

Bratanova, Alexandra and Evans, David B and Irons, Jessica (2026): The use cases for AI in Australian sport. Published in: Industry report

[thumbnail of MPRA_paper_128742.pdf]
Preview
PDF
MPRA_paper_128742.pdf

Download (1MB) | Preview

Abstract

This paper examines the emerging applications of artificial intelligence (AI) across the Australian sports sector, focusing on how the technology is reshaping athlete performance, operations, fan engagement and inclusion. Drawing on international evidence, Australian case studies and stakeholder insights, the paper identifies a set of use cases that illustrate both current applications and near-term opportunities. In high-performance contexts, AI is being applied to enhance athlete performance through automated data capture and real-time analytics, support personalised training and coaching, and predict injury and health risks. Additional uses include improving officiating accuracy, expanding talent identification pathways, strengthening integrity through anti-doping and match-fixing detection, and monitoring online abuse directed at athletes. Across sporting organisations, AI is supporting operational efficiency by automating administrative tasks, improving scheduling and communication, enhancing access to information, and strengthening volunteer recruitment and retention. Data-driven tools are also being used to optimise facility utilisation, support sustainability goals and assist with compliance processes. In fan engagement, AI enables more accessible and cost-effective broadcasting, generates highlights and personalised content, and delivers real-time insights to audiences. It also supports participation pathways and enhances marketing and sponsorship activities. Finally, AI is contributing to greater inclusion and accessibility by reducing language barriers, improving experiences for people with disability, and increasing the visibility and participation of under-represented groups. Together, these use cases demonstrate the breadth of AI’s potential to transform the sports ecosystem, while highlighting the need for careful and context-aware implementation.

Atom RSS 1.0 RSS 2.0

Contact us: mpra@ub.uni-muenchen.de

This repository has been built using EPrints software.

MPRA is a RePEc service hosted by Logo of the University Library LMU Munich.