Overview
Course overview
Many organisations have already experimented with AI. Far fewer have answered the harder leadership questions: which use cases deserve investment, where people must remain accountable, how agents should access data and tools safely, and what operating model moves AI beyond isolated pilots. This 16-week live executive certificate is designed to close that gap. It treats AI transformation as a connected business system involving strategy, data, workflows, people, governance and measurable value — not a collection of tools.
Across four phases you will build an accurate leadership-level understanding of generative and agentic AI, apply it across the business, design and test a working no-code agent, and translate technical possibility into an operating model, governance framework and board-ready roadmap. The central promise is simple: move from scattered AI activity to a controlled transformation plan your organisation can act on.
What you will leave with
Every phase contributes to a practical leadership deliverable rather than passive content:
- An AI opportunity portfolio — realistic generative and agentic use cases prioritised by value, feasibility, risk and strategic fit.
- A working no-code AI agent — designed, built and tested to complete a defined business workflow with approved tools, data and human checkpoints.
- An AI readiness and data assessment — an honest view of the information, infrastructure, skills and controls responsible implementation requires.
- A governance and risk framework — ownership, escalation, human oversight, privacy, security and responsible-use controls.
- A 12–24 month transformation roadmap — pilots, investment, capability-building and governance sequenced into a board-ready plan.
- An executive certificate from LSBUK — documenting successful completion of the programme and capstone.
What you will study
Phase 1 — AI Foundations for Leaders. Understand the AI landscape and where generative and agentic AI fit within it. Distinguish traditional, generative and agentic AI, separate real capability from exaggerated claims, and see where organisations are creating value today. Apply a three-horizon view of what is ready now, what is emerging and what remains uncertain, and learn the leadership questions to ask before approving any AI initiative. Practical output: an AI terminology and opportunity map for your organisation.
Understand how generative models work well enough to evaluate their use and limitations. Cover how large language models generate responses, tokens, context windows and model memory, why hallucinations occur and how to reduce their impact, and text, image, audio and video generation. Examine reliability, verification and the boundaries of model judgement. Practical output: an AI limitations and verification checklist.
Improve the two areas leaders can influence immediately: information quality and task design. Conduct a practical data-readiness review, structure prompts around role, objective, context, constraints, examples and output format, and build repeatable prompt workflows rather than isolated prompts. Decide what should and should not be sent to an AI system when handling confidential, personal and commercially sensitive information. Practical output: a data-readiness audit and reusable prompt workflow.
Phase 2 — Generative AI Across the Business. Apply generative AI to revenue-facing work without compromising quality or brand control. Explore marketing and content production, sales research, enablement and personalisation, and customer support and service workflows. Define brand voice, approval and quality assurance and set the human-in-the-loop boundary. Practical output: one growth use-case design with quality controls.
Identify practical efficiency and knowledge opportunities across internal functions: finance analysis, reporting and forecasting support; operations, procurement and process documentation; HR, recruitment, onboarding and learning; and meetings, email, drafting, research and knowledge access. Focus on redesigning work rather than simply adding another tool. Practical output: a current-state and AI-enabled workflow comparison.
Use AI to improve the structure and speed of analysis while retaining executive accountability. Cover market and competitor research, scenario planning and strategic options, synthesising large volumes of information, and assumption testing and red-team questioning. Clarify where AI can support judgement and where it must not replace it. Practical output: an AI-assisted decision brief with assumptions and verification notes.
Make better tool, vendor and platform decisions. Evaluate the changing AI-tool landscape, apply a build, buy or configure decision framework, and understand licensing, usage costs and hidden operational spend. Assess security, data residency, access control and vendor risk alongside integration and enterprise-readiness criteria. Practical output: a vendor evaluation scorecard.
Phase 3 — Agentic AI: From Assistance to Execution. Understand what makes a system agentic and how controlled autonomy changes risk and value. Cover goals, planning, tools, memory and action; the difference between chatbots, copilots and agents; single-agent and multi-agent patterns; and autonomy, permissions and accountability. Examine the failure modes introduced by tool access and long workflows. Practical output: an agent opportunity and risk map.
Design and build a working agent for a real business task. Select a suitable workflow, break it into triggers, decisions, actions and checks, connect approved tools and information sources, and define prompts, permissions and escalation points. Includes a live build-and-test lab. Practical output: a working no-code agent prototype.
Understand how agents coordinate, hand off work and operate within larger systems. Cover agent orchestration and task hand-offs, integrating agents with existing workflows, human approval gates and exception handling, and monitoring, logs, guardrails and fail-safe design. Decide where not to automate. Practical output: a controlled multi-step agent workflow design.
Build a credible business case for agentic AI. Identify high-value use cases by function and measure time, cost, quality, revenue, risk and experience against realistic baselines. Separate quick wins from strategic investments and learn the common pilot and scaling failures to avoid. Practical output: a prioritised use-case portfolio and business-case outline.
Phase 4 — Leading AI Transformation. Connect AI investment to organisational strategy and execution. Cover strategic priorities and AI investment choices, centralised, federated and hybrid operating models, ownership across business, technology, data, legal and risk teams, and partner, infrastructure and capability decisions. Sequence a realistic 12–24 month roadmap. Practical output: a draft AI operating model and roadmap.
Establish practical controls for responsible AI use. Translate responsible-AI principles into operating controls, and cover data protection, privacy, intellectual property and copyright; bias, fairness, transparency and explainability; risk classification, approval and monitoring; and the evolving EU, UK and international regulatory environment. Practical output: an AI governance and risk framework. Teaching is educational and does not replace legal advice.
Help people adopt AI safely and productively. Address the human side of AI change, building literacy and role-specific capability, resistance, uncertainty and trust, and incentives, workflow redesign and manager accountability. Learn to measure adoption, behaviour and performance. Practical output: an AI adoption and capability plan.
Weeks 15–16. Integrate the programme into a board-ready transformation plan. Define the strategic problem and desired outcomes, prioritise generative and agentic use cases, and present the operating model, governance and adoption approach. Build the investment logic, milestones and success measures, receive peer review and expert feedback, and present and defend the final blueprint. Final output: a complete AI transformation blueprint for your organisation.
Who is this for?
This programme is designed for professionals responsible for decisions, performance, transformation or organisational risk — C-suite executives and directors, business owners and founders, functional leaders across operations, finance, HR, marketing, sales and customer experience, strategy, transformation and change leaders, product and programme leaders, and consultants advising on AI adoption. You do not need programming experience, a technical or data-science degree, or prior experience building AI systems. It is not an engineering or coding bootcamp, a list of prompt templates, or a fully self-paced course.
Learning outcome
By the end of the programme, you will understand generative and agentic AI at a leadership level, have built and tested a working no-code agent, and be able to evaluate value, risk and investment with confidence. You will leave with a usable set of deliverables — an opportunity portfolio, readiness assessment, governance framework and a 12–24 month roadmap — brought together in a board-ready AI transformation blueprint for your own organisation.
Assessment and delivery style
The core programme is fully taught through live online cohort sessions supported by selected London campus immersion days. Learning is interactive and applied: live teaching, practical workshops and labs, applied work on your own organisation or a realistic business context, senior peer learning and expert feedback. Assessment is based on practical module outputs and the final capstone, which is presented and reviewed for strategic logic, feasibility, governance, value and clarity. Plan for approximately 5–7 hours per week.
