Loughborough University

What you'll study

This interdisciplinary master's blends core management modules with technical and applied artificial intelligence topics. Typical teaching covers machine learning fundamentals, data analytics, AI for business, ethics and governance of AI, and strategic management. You will study modules such as:

• Machine Learning and Data Mining – supervised and unsupervised learning methods, model evaluation and feature engineering.
• Advanced Analytics for Management – predictive analytics, optimisation and decision support for organisational problems.
• AI in Organisations – how AI transforms business processes, value chains and customer engagement.
• Ethics, Regulation and Responsible AI – bias, transparency, accountability and regulatory frameworks for deploying AI systems.
• Project Management and Innovation – managing technical projects, change management and commercialisation of AI-enabled products.
• Research Methods and Professional Practice – quantitative and qualitative methods, data governance and research design for your dissertation or consultancy project.

The programme typically includes a substantial project or dissertation where you apply AI techniques to a management problem, working with real or simulated data. Delivery combines lectures, seminars, lab sessions, case studies and group work; assessment is usually via a mix of examinations, coursework, presentations, group reports and the final project.

Entry requirements

Applicants are normally expected to hold a recognised undergraduate degree in a related discipline such as business, management, economics, computer science, engineering, mathematics or another quantitative subject. Applicants with professional experience in technology or analytics may also be considered where evidence of quantitative ability and motivation is demonstrated.

• Typical academic requirement: an upper second-class (2:1) honours degree or international equivalent in a relevant subject, though applicants with a lower-class degree plus relevant experience may be considered.
• Quantitative skills: evidence of competence in mathematics, statistics or computing is expected; some programmes may ask for prior exposure to programming or basic data analysis.
• English language: international applicants whose first language is not English will need to meet the university's English language requirements, usually demonstrated through a recognised test or equivalent evidence.
• Supporting materials: a personal statement outlining your motivation and relevant experience, references, and in some cases a CV or portfolio may be requested.

Career prospects

Graduates go on to roles at the intersection of business and technology. Typical career paths include product manager for AI-enabled services, data science or analytics manager, AI strategy consultant, technology policy adviser, and roles in operations, marketing or risk that require applied machine learning skills. Alumni also move into consultancy firms, large corporates, startups and public-sector organisations, or continue to research and doctoral study on AI, management or ethics-related topics.

The combination of management knowledge and AI fluency prepares you to lead multidisciplinary teams, translate technical capability into business value, and take responsibility for the safe and ethical deployment of intelligent systems.

Why study at Loughborough University

Loughborough is known for a practical, industry-focused approach and strong links with employers, which benefits applied programmes combining management and technology. The university offers access to computing and analytics facilities, industry-led guest lectures, and a supportive environment for entrepreneurship and innovation. Teaching emphasises project work and real-world case studies, enabling you to develop both technical skills and leadership capabilities valued by recruiters.

Students also benefit from Loughborough's broader strengths in engineering, sports technology and design, creating opportunities for interdisciplinary collaboration, placements and networks that help bridge academic learning and professional practice.