Course overview
AI Strategy for Executives is a non-technical programme for leaders who own the AI decision but not the code. You work through identifying high-value use cases, sizing the investment and expected return, setting the guardrails that keep adoption safe, and sequencing projects into a realistic roadmap. You leave able to brief a board, challenge a vendor and lead an AI initiative with genuine confidence.
Who it is for
Working professionals, managers, founders, team leaders and ambitious learners seeking practical development.
It is a non-technical course for executives and business leaders who own AI decisions. No coding, data science or technical background is needed.
AI strategy topics
Develop a compelling AI vision that aligns with corporate strategy and inspires organisational commitment. Examine how leading organisations frame their AI ambitions and communicate them to diverse stakeholders. Learn how to assess your industry's AI trajectory, identify white spaces, and articulate where AI can create distinctive value. Apply a structured visioning process to define your organisation's AI north star with clear principles, priorities, and a measurable long-term destination.
Build a disciplined portfolio of AI use cases that balances short-term value capture with strategic capability building. Examine use-case identification, feasibility assessment, and prioritisation frameworks including impact-effort matrices and value-risk analysis. Learn how leading organisations manage their AI pipeline from ideation through pilot to scale. Develop a structured use-case portfolio review process that directs AI investment to the highest-value opportunities.
Design the organisational structure, processes, and ways of working that enable effective AI strategy execution. Examine centralised, federated, and hub-and-spoke AI operating models and the trade-offs between them. Learn how to define team structures, decision rights, and collaboration protocols for AI initiatives. Develop a target operating model that integrates AI capability with existing business functions while enabling rapid experimentation and scaling.
Build robust frameworks for justifying AI investment and measuring return. Examine total cost of ownership modelling, value driver identification, and benefit realisation planning for AI initiatives. Learn how to quantify value in areas such as customer experience, risk reduction, and capability building. Develop a financial model and investment governance process that enables confident decision-making about AI portfolio allocation at board level.
Establish a governance structure that provides appropriate oversight of AI strategy without stifling innovation. Examine AI governance frameworks from leading organisations, including board-level AI committees, executive AI councils, and operational review processes. Learn how to balance speed and control at different stages of the AI lifecycle. Develop a governance model with clear escalation paths, decision rights, and accountability mechanisms for your organisation.
Translate your AI vision into an actionable multi-year roadmap with clear milestones, owners, and dependencies. Examine roadmap planning methodologies for complex, uncertain technology programmes. Learn how to sequence initiatives to build capabilities progressively, manage dependencies, and maintain organisational momentum. Develop a living roadmap document that communicates strategic direction clearly while remaining adaptable as technology and business conditions evolve.
Apply change management principles to the unique challenges of AI strategy implementation. Examine how AI-specific concerns including job displacement anxiety, algorithmic distrust, and skills uncertainty require tailored change approaches. Learn how to build coalitions of support, design effective communication campaigns, and address resistance constructively. Develop a change management plan for your AI strategy that builds genuine engagement rather than superficial compliance.
Build and maintain alignment among the diverse stakeholders whose support is essential for AI strategy success. Examine stakeholder mapping, influence analysis, and engagement planning for AI initiatives. Learn how to manage conflicting priorities between technology, business, legal, HR, and board stakeholders. Develop a stakeholder engagement strategy that creates shared understanding, addresses legitimate concerns, and sustains commitment through the challenges of strategic transformation.
Develop the human and technical capabilities your organisation needs to execute its AI strategy effectively. Examine capability gap analysis, build-versus-buy-versus-partner decisions, and learning strategy design for AI skills. Learn how to develop AI literacy across leadership, create specialist career pathways, and build partnerships with technology providers and academic institutions. Develop a capability building plan that supports both immediate execution and long-term organisational learning.
Design the metrics and tracking mechanisms that provide meaningful insight into AI strategy progress and impact. Examine leading and lagging performance indicators, balanced scorecard approaches for AI, and executive dashboard design. Learn how to distinguish output metrics from outcome metrics and align measurement with strategic objectives. Develop a performance tracking framework with clear targets, reporting cadences, and review processes that keeps AI strategy accountable.
Business use-case evaluation
You learn to identify AI use cases, assess their feasibility and prioritise them using frameworks such as impact-effort matrices and value-risk analysis, balancing short-term value with longer-term capability building.
Governance and risk
You design governance that gives appropriate oversight without stifling innovation, looking at board-level AI committees, executive AI councils and operational review processes, and how to balance speed and control at different stages of adoption.
Roadmap and ROI exercise
You build a business case using total cost of ownership and value drivers, then turn your priorities into a roadmap with milestones, owners and dependencies.
The course's roadmap module builds a plan for your own organisation. A typical outline has three stages:
- First 90 days: agree two or three business problems worth solving, check data readiness, set governance rules and record a baseline for each measure.
- Months 3 to 9: run bounded pilots with named owners, compare results against the baseline including cost and risk, and stop what does not work.
- Months 9 to 18: scale the pilots that proved their value, build the skills and operating model to support them, and review the portfolio with the board at agreed gates.
The timings are an example, not a promise; your roadmap sets its own stages, costs and review points. No coding or technical background is needed.
A worked first-year business case shows the discipline involved: £20,000 of expected benefit against £8,000 of implementation and £4,000 of annual running cost gives a first-year ROI of 66.7% only if the benefit is realised and the costs are complete; at half the benefit, the same case loses money. The full example, with risks and pilot stop conditions, is in the AI use-case and business-case pack.
Delivery
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.
| Live Online | Campus (London) | |
|---|---|---|
| Fee | £492 | £1,583 |
| Where you study | Live online, taught in UK time. Joining from India, the UAE or China? Admissions send the timetable converted to your timezone | Malet Street, Bloomsbury, London WC1E 7HX |
| Duration | 3 to 6 weeks | |
| Suits | Executives, managers, founders and team leaders who own AI decisions but not the code | |
Fees and dates
Fees are £492 for the Live Online route and £1,583 for the Campus route in London, charged in pounds sterling. The course runs over 3 to 6 weeks; admissions confirm the next dates when you enquire. Ask admissions for the session times and the study expected between sessions, so you can plan the course around your work.
Guides for leaders building an AI strategy
Practical reading that pairs well with the course:
- How to build an AI strategy for your company
- AI strategy document template: what to include
- How to measure AI ROI and strategy outcomes
- How to present an AI strategy to your board
For the whole decision in order, read the AI strategy subject guide: entry, workload, cost, the choices to compare and the questions to ask. When you are ready, ask admissions a question.
