Module 1: Foundations of AI
Understand the evolution from predictive AI and machine learning to generative AI
Explore how large language models learn and generate content through data, training and next-token prediction
Build the vocabulary needed to engage confidently with technical specialists and evaluate AI proposals
Discover emerging capabilities across text, images, voice, video, code, reasoning and research
Recognise key limitations, including hallucinations, bias and the uneven boundaries of model performance
Examine the implications of data privacy, copyright and the rapidly evolving future of generative AI
Module 2: Horizontal Applications and Personal Productivity
Identify horizontal AI applications that can enhance everyday work across roles and functions
Examine research evidence on how generative AI affects productivity, quality, learning and job satisfaction
Apply prompt engineering in a realistic business scenario, using customer feedback and data to extract insights and recommend action
Compare outputs from different generative AI models and evaluate their clarity, depth, nuance and limitations
Assess your organisation’s readiness for generative AI and identify practical steps to encourage safe experimentation, knowledge sharing and adoption at scale
Module 3: Vertical and Agentic Use Cases
Distinguish general-purpose AI tools from vertical systems designed for specialised, high-value workflows
Identify a vertical AI opportunity in your organisation and define the problem, specialised data requirements and anticipated benefits
Evaluate whether to buy, build or customise an AI solution based on strategic value, capabilities, cost, time-to-value and control
Use practical decision tools to consider base-model selection, proprietary-data integration and appropriate accuracy, compliance and moderation guardrails
Explore how AI agents plan, use tools, take actions and collaborate to complete multi-step workflows
Assess organisational readiness and map a suitable agentic workflow through guided activities and practical sessions
Module 4: Strategy, Value and Opportunity Mapping
Distinguish off-the-shelf AI tools that improve the industry baseline from custom applications that can create competitive advantage
Identify opportunities for predictive AI within the frequent, repeatable decisions embedded in organisational workflows
Recognise opportunities for generative AI across text, images, audio, video, code and other forms of business content
Apply the Precision–Persuasion Framework and recognise when convincing AI-generated output requires additional validation and human judgement
Use a structured canvas to define an AI use case, its audience, inputs, risks, human oversight requirements, business impact and pilot potential
Score and prioritise the opportunity, creating an evidence-based idea that can be developed further in the Capstone
Module 5: Risk, Regulation and Responsible Adoption
Examine major AI risks, including misuse, bias, discrimination, workforce disruption and loss of human oversight
Understand the competing perspectives shaping the global debate between AI safety and accelerated innovation
Compare emerging regulatory approaches across major jurisdictions and understand the EU AI Act’s risk-based framework
Identify the governance, documentation and accountability practices required for responsible organisational adoption
Analyse work at the task level to determine where AI is likely to automate activity and where it can augment human capability
Evaluate how AI can support cost reduction, growth or differentiation, and consider how leaders can prepare their organisations and people for change
Module 6: Capstone: Design a Real-World AI Proposal
Select a high-impact horizontal, vertical or agentic AI opportunity, building on ideas and feedback developed throughout the programme
Identify the stakeholders whose support is essential and tailor the proposal to their priorities
Define the business problem, proposed solution, intended users, strategic value and decision required
Assess implementation requirements and risks, including data, technology, governance, regulation and workforce implications
Establish practical success measures and recommend a realistic pilot and immediate next steps
Produce a customised stakeholder briefing, presentation or video pitch, with guidance and support available should you wish to develop an optional prototype
