Overview
Course overview
A non-technical AI programme helping leaders understand strategy, governance, risk, workforce change and responsible adoption without needing to code. The course is built around practical learning, professional confidence and clear progression. Learners are encouraged to apply ideas to realistic business situations and leave with a stronger ability to communicate decisions effectively.
What you will study
Establish the conceptual foundation every senior leader needs to lead in an AI-powered world. Examine the history, capabilities, and limitations of AI technologies including machine learning, deep learning, and generative AI. Learn how AI differs from conventional software and why this matters for executive decision-making. Explore case studies of AI leadership successes and failures, developing a clear-eyed understanding of AI's transformative potential and the organisational challenges of realising it.
Develop a structured approach to AI strategy and build a portfolio of high-value AI use cases. Examine strategic frameworks for assessing AI opportunity across the value chain, from customer experience to operations to finance. Learn how to evaluate use-case feasibility, prioritise based on strategic fit and ROI potential, and build an AI pipeline that balances quick wins with longer-term capability building. Apply strategy tools to define your own organisation's AI direction.
Build the data governance and ethical frameworks that responsible AI deployment requires. Examine data quality, data ownership, and governance structures alongside ethical principles of fairness, transparency, and accountability in AI. Learn how GDPR, UK data protection law, and emerging AI-specific regulation affect data strategy. Develop governance policies and ethical review processes that enable trustworthy AI while supporting innovation and commercial growth.
Identify and manage the full spectrum of risks associated with deploying AI in enterprise environments. Examine model risk, cybersecurity threats, regulatory compliance requirements, and reputational exposure from AI failures. Learn how to build AI risk registers, design control frameworks, and establish incident response protocols. Develop an AI risk management approach that integrates with enterprise risk management and gives the board appropriate assurance about AI-related exposures.
Lead the human side of AI adoption across your organisation with confidence and empathy. Examine how AI affects roles, skills requirements, and employee experience across different business functions. Learn reskilling programme design, change management approaches for AI-related role changes, and strategies for maintaining employee trust during transformation. Develop a workforce transformation plan that addresses both the practical skills agenda and the cultural dimensions of AI adoption.
Apply learning across the programme to develop a comprehensive AI roadmap for your organisation or a defined business unit. Examine roadmap design methodologies that sequence AI initiatives across a three-to-five-year horizon. Learn how to present AI roadmaps to boards and executive committees and secure strategic investment. Develop your own AI roadmap as the programme's major assessed deliverable, receiving faculty and peer feedback throughout the development process.
Translate strategic AI ambitions into operationally credible implementation plans. Examine project governance, resource planning, vendor selection, and build-versus-buy decisions for AI initiatives. Learn agile implementation approaches appropriate for AI development, including pilot design, stage gate criteria, and scaling methodology. Develop detailed implementation plans for priority AI initiatives that include milestones, resource requirements, risk management actions, and success measurement.
Lead the organisational transformation required for AI strategy to succeed beyond isolated pilots. Examine change management theory and practice applied specifically to AI adoption, addressing resistance, building momentum, and embedding new ways of working. Learn how to design change programmes that shift culture and behaviours sustainably. Apply change management frameworks to develop an actionable plan for embedding AI in your own organisation's operating model.
Evaluate your organisation's current AI capabilities against the requirements of your strategic ambitions. Examine AI capability maturity frameworks covering technology infrastructure, data assets, talent, governance, and culture. Learn how to design and conduct capability gap assessments and translate findings into actionable development priorities. Develop a capability assessment report for your own organisation, with a prioritised roadmap of capability investments required to execute your AI strategy.
Analyse your organisation's competitive position in a world where AI is reshaping industry dynamics. Examine how AI is disrupting competitive landscapes across sectors and how first-movers, fast followers, and laggards are differentiated. Learn competitive intelligence approaches for tracking AI developments among rivals. Develop a competitive positioning analysis for your own organisation, identifying where AI investment will have the greatest strategic impact on market position.
Who is this for?
Working professionals, managers, founders, team leaders and ambitious learners seeking practical development.
Learning outcome
By the end of the programme, learners should have a clearer professional framework, stronger confidence and a practical action plan that can be applied in study, work or organisational decision-making.
Assessment and delivery style
Teaching is designed to be interactive, applied and professionally relevant. Activities may include case discussion, guided exercises, workplace examples, short presentations, reflective planning and tutor-led feedback.
