AI is moving from experimentation to execution. The decisions that determine whether an organisation gets durable value from AI are increasingly leadership decisions, where to invest, how to govern, which use cases to pursue, and how to bring teams along through the change. This programme equips business leaders and managers with the practical knowledge needed to drive AI adoption within their own functions. It is not an awareness session and it is not a technical course. The focus is on applying AI including Generative AI and emerging Agentic capabilities, to real business problems, improving day-to-day decisions, and leading AI initiatives in close collaboration with technical teams. The programme is deliberately tool-agnostic. Participants learn how to evaluate and select among leading LLMs — Claude, ChatGPT, Microsoft Copilot, GitHub Copilot, Codex, Gemini and others — rather than committing to a single platform. AI challenges, risks, and Responsible AI are treated as core leadership content, not as an appendix. Each participant leaves with a shortlist of AI opportunities for their function, a draft business case, and a 30-60-90 day adoption plan.
WHO SHOULD ATTEND
- Business leaders and C-suite executives
- Delivery heads and programme managers
- Senior managers and managers across business functions
- Non-technical stakeholders sponsoring or driving AI initiatives
- Functional leaders in operations, sales, finance, HR, marketing, and customer-facing roles
Cohort size: 15–25 participants for optimal discussion depth.
PRE – REQUISITES
- 8–15+ years of leadership or managerial experience
- A conceptual understanding of what AI is and is not capable of — no coding required
- Familiarity with how decisions get made and how work gets done in their function
- Openness to using AI tools in day-to-day work — encouraged to bring one or two real problems from their function
- Access to at least one general-purpose LLM (Claude, ChatGPT, Copilot, or Gemini) during the session
KEY OUTCOMES
- Frame AI as a business decision — distinguishing where it creates value from where it adds cost or risk
- Speak confidently about Predictive AI, Generative AI, RAG, and Agentic AI at a leadership level
- Compare leading LLM tools and select the right tool for the right task
- Identify and prioritise AI opportunities using a value-vs-feasibility lens
- Recognise and plan for major AI risks — hallucination, bias, data privacy, IP exposure, security
- Apply structured decision-making frameworks (OODA, Cynefin, human-in-the-loop) to AI-assisted
decisions - Draft an investment-ready business case for a pilot AI initiative with measurable success metrics
- Lead AI adoption using change frameworks (Kotter, ADKAR) and a 30-60-90 day plan
