Programme Content
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
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
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
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
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
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
Understanding AI Fundamentals: Participants will be able to understand how generative AI models work including concepts like predictive AI reinforcement learning and the role of data parameters and compute. They will also gain the language to talk to AI experts and understand the limitations of the technology.
Prompt Engineering: A foundational skill taught in the course is prompt engineering which is the ability to give AI models clear and effective instructions.
Strategic Application of AI: Participants will be able to identify and evaluate business problems that are well-suited for AI. They will learn to distinguish between horizontal (general-purpose) and vertical (specialised) AI applications.
Practical Implementation: The course prepares participants to actually implement AI solutions. They will be able to navigate key decisions like "build vs. buy" choosing a base model and integrating proprietary data using methods like Retrieval-Augmented Generation (RAG) or fine-tuning.
Risk Management and Responsible Adoption: Participants will acquire the skills to identify and mitigate risks associated with AI including data leakage and the persuasion trap. They will also understand regulatory frameworks like the EU AI Act and learn how to navigate the workforce implications of automation and augmentation.
Continuous Learning and Adaptation: The course emphasises that the most successful professionals are those who commit to continuous learning and are able to reinvent themselves as the technology evolves. Participants are encouraged to actively experiment with AI tools push their limits and learn from both successes and failures.
