PhD Studentship: Operationalising Artificial Intelligence in Elite Sport Organisations: Readiness, Governance and Integration Pathways

Manchester Metropolitan University Manchester, United Kingdom

About the position

This PhD tests a new explanation: readiness for AI depends not only on money, staff and data, but on an organisation's relationships with technology suppliers, regulators and peers. Working across football, cricket and basketball, including women's professional sport, you will investigate how organisations move from experimenting with AI to embedding it, and help create a practical diagnostic tool for the sector.

You will gain advanced skills in case study research, interviewing and social network analysis while working with decision makers in sport, and you will be encouraged to shape the research design.

You will design and lead a comparative multiple case study of how clubs adopt and govern technology. The fieldwork combines semi-structured interviews with senior decision makers and social network analysis of the relationships between clubs, technology providers, leagues and governing bodies. You will receive training in case study design, qualitative interviewing and analysis, and network methods, including data collection, measurement and modelling. The research will examine whether current governance arrangements for technology reduce or widen resource and performance inequalities between clubs, and the results will feed directly into policy discussions with partners in the sport.

Objectives

  • Explore how data quality and interoperability, undermined by conflicting standards across vendors, leagues and governing bodies, constrain AI deployment.
  • Explore and understand what organisational and technical factors shape integration of AI systems with legacy and outsourced infrastructures, and the trade-offs imposed.
  • Explore how ESOs reconcile short-term operational gains with long-term capability building, and how technical debt accumulates through AI procurement.
  • Analyse how governance, regulation and position within the technology ecosystem shape unequal organisational capacity to operationalise AI.

Funding

This studentship is open to home and international applicants. Home and international tuition fees will be covered for the duration of the 3-year award.

The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £21,805 for the academic year 2026/27.

Candidate requirements

  • Experience working/studying in, or with sports organisations, governing bodies or sport technology providers.
  • An understanding of AI regulation.

How to apply

If you have any questions, please contact the proposed Principal Supervisor, Dr. Tom Mitchell (Tom.Mitchell@mmu.ac.uk).

To apply you will need to complete the online application form for a full-time PhD in Operations & Technology. Please note that you are not required to submit a research proposal through the online application portal.

Please complete the Doctoral Project Applicant Form, and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.

Please upload these documents in the supporting documents section of the University's Admissions Portal or email the requested documents to pgradmissions@mmu.ac.uk.

Expected start date: October 2027.

Please quote the reference: FoBL-TM-2027-28-AI Governance and Integration

This PhD project is one of six independent projects open for applications, with only three funded studentships available. All applications will go through an initial review stage conducted by the project supervisors. Candidates selected from this stage will be invited to a second-round interview, where final funding decisions will be made based on performance across all projects.

This listing was collected from a public source and is reproduced here for information only. Always confirm the details on the original posting before applying.
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