Overview
Course overview
Understanding AI: What Leaders Need to Know is the starting point for executives who want to cut through the jargon. In clear, non-technical language you learn how modern AI actually works, where it genuinely helps and where it doesn't, the vocabulary to hold your own with technical teams and vendors, and a simple framework for deciding when and how to adopt it. No prior background required.
What you will study
Build confident foundational understanding of what AI is, how it works, and what it can and cannot do. Examine the key concepts of machine learning, neural networks, natural language processing, and computer vision in accessible, non-technical language. Learn how to distinguish between narrow AI, general AI, and the current reality of AI capabilities in practice. Develop the vocabulary and conceptual framework needed to engage credibly in AI conversations at board and executive level.
Understand generative AI, the technology behind tools such as ChatGPT, Claude, and Gemini, and its implications for business. Examine how large language models work, what they are good at, and where they commonly fail. Learn how to evaluate generative AI tools for business use and understand the risks of hallucination, bias, and over-reliance. Develop practical intuitions about where generative AI creates genuine value and where caution is warranted.
Identify the specific business opportunities that AI creates for your organisation and sector. Examine AI application areas across revenue growth, cost reduction, risk management, and customer experience improvement. Learn how to assess AI opportunity through a business value lens rather than a technology lens. Apply opportunity identification frameworks to your own organisational context, mapping where AI investment is most likely to deliver measurable and sustainable competitive advantage.
Develop a clear-eyed understanding of the risks and limitations every leader must account for when deploying AI. Examine technical limitations including data dependency, brittleness, explainability challenges, and performance degradation over time. Explore organisational risks including skills gaps, change resistance, vendor dependency, and security vulnerabilities. Learn how to ask the right questions of AI vendors and technical teams to ensure that risk is properly identified and managed.
Understand the ethical responsibilities of leaders who deploy AI in their organisations. Examine the core ethical principles of fairness, accountability, transparency, and privacy as applied to AI. Learn how regulators and governments are approaching AI governance through frameworks including the EU AI Act and UK AI principles. Develop a personal leadership position on responsible AI and understand how to build ethical considerations into your organisation's AI decision-making processes.
Translate your programme learning into a concrete personal action plan for leading AI in your own context. Examine leadership frameworks for AI strategy and reflect on the specific decisions, conversations, and initiatives you need to drive as a leader. Learn how to identify your highest-priority next steps and communicate an AI vision to your team and organisation. Develop a personalised 90-day leadership action plan with clear commitments and success measures.
Build the skills to evaluate AI technology claims, vendor proposals, and internal AI project proposals with appropriate rigour. Examine common patterns of AI overselling, technical debt, and project failure. Learn how to ask incisive questions about data quality, model performance, integration requirements, and total cost of ownership. Develop an evaluation framework for assessing AI technologies and proposals that enables confident leadership decisions without requiring deep technical expertise.
Assess your organisation's readiness to implement AI successfully and identify the preparations needed. Examine readiness dimensions including data infrastructure, talent, culture, governance, and change management capability. Learn how to conduct an honest readiness assessment and develop an improvement plan that addresses gaps systematically. Develop practical tools for evaluating implementation readiness for specific AI initiatives, enabling more accurate forecasting of complexity and resource requirements.
Build the AI capability of your immediate team and department to improve execution quality and strategic contribution. Examine team-level AI literacy requirements, learning programme design, and cultural conditions that support effective AI adoption. Learn how to identify AI champions within your team, address resistance constructively, and create an environment where experimentation is encouraged. Develop a team AI capability building plan with targeted learning activities and accountability mechanisms.
Use AI as a lens for refreshing your competitive strategy and identifying new sources of advantage. Examine how AI is reshaping the basis of competition across industries, from cost structures to customer intimacy to innovation speed. Learn how to analyse competitors' AI capabilities and anticipate disruption from AI-native entrants. Develop a competitive strategy update that incorporates AI as a strategic variable and positions your organisation to create and defend advantage in an AI-powered market.
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.
