AI Fundamentals for Leaders and Decision Makers

A free YouTube course for everyone — from curious beginners to executives handed an AI initiative and told to make it work. 43 lectures across 6 modules — no math, no coding bootcamp, no breathless predictions.

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Lectures

Modules

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Course Curriculum

Click any lecture to play. Or watch the whole course on the YouTube playlist.

Introduction: Introduction

2 lectures

Introduction · Lecture 1

Who This Course Is For

This course is designed for leaders and decision makers who need to understand AI as a strategic capability, not a technical curiosity. Whether you're a CEO mapping AI into your long-term plan, a department head exploring process transformation, a board member needing to ask the right questions, or someone newly tasked with an AI initiative — this is your starting point. You won't find deep mathematics or coding here. Instead, you'll gain the concepts, frameworks, hands-on skills, and strategic thinking to lead AI initiatives with confidence.

Introduction · Lecture 2

What You Will Learn

Get a clear roadmap of what's ahead. This lecture walks you through the six core modules — from understanding the AI landscape and building a strategic business case, through hands-on tool usage and risk management, to creating your AI strategy and measuring implementation success. By the end of the course, you'll speak the language of AI fluently, use AI tools effectively, manage risks responsibly, build a tailored AI strategy, and leave with a concrete 90-day action plan ready for execution.

Module 1: The AI Landscape

7 lectures

Module 1 · Lecture 1

What is Artificial Intelligence?

Cut through the hype and understand what AI actually is — and isn't. This lecture establishes a practical working definition: AI is computer systems performing tasks that typically require human intelligence. You'll learn why "artificial intelligence" has always been a marketing term, discover that the underlying mathematics dates back decades, and gain six critical questions to ask whenever evaluating any AI proposal. This reframing — from AI as magic to AI as specific capabilities — is the foundation for strategic thinking.

Module 1 · Lecture 2

A Short History of AI

Understand why this moment feels different by tracing AI's journey from the 1956 Dartmouth workshop through repeated cycles of hype and disappointment. Three factors finally converged to make old algorithms work at scale: computational power, abundant data, and cloud accessibility. This history explains why healthy skepticism is warranted, why current capabilities are genuinely real, and how to frame your strategic window as organisations build AI capabilities now.

Module 1 · Lecture 3

Machine Learning Explained

Grasp the engine that powers most modern AI. Machine learning flips traditional programming: instead of writing rules, you provide examples and let the system discover patterns. This lecture explains the fundamental shift, illustrates it with practical examples like fraud detection and demand forecasting, and covers the three essential requirements for any ML initiative: quality data, clear objectives, and ongoing maintenance. You'll understand why most AI projects fail — and what they actually need to succeed.

Module 1 · Lecture 4

Supervised vs. Unsupervised Learning

Learn the two fundamental approaches to machine learning and when to use each. Supervised learning predicts specific outcomes when you have labelled examples — ideal for classification and prediction tasks. Unsupervised learning discovers hidden patterns without predefined categories — perfect for customer segmentation and anomaly detection. You'll gain a practical framework for matching the right approach to your business problem, plus a brief introduction to reinforcement learning.

Module 1 · Lecture 5

Natural Language Processing and Large Language Models

Understand the technology behind ChatGPT, Claude, and similar tools. This lecture explains how machines process human language, from basic text analysis to the transformer architecture that enabled today's large language models. You'll learn what these systems can and cannot do, why they sometimes produce confident-sounding nonsense, and how to think about their capabilities realistically — neither dismissing them nor overestimating what they can deliver.

Module 1 · Lecture 6

Predictive vs. Generative AI

Distinguish between AI that predicts and AI that creates. Predictive AI analyses patterns to forecast outcomes — customer churn, demand, fraud risk. Generative AI produces new content — text, images, code, designs. This lecture clarifies the distinct business applications, investment requirements, and risk profiles of each approach, helping you identify which type addresses your specific challenges and where each delivers the most value.

Module 1 · Lecture 7

Agentic AI

Explore the emerging frontier where AI systems take autonomous actions to achieve goals. Agentic AI goes beyond single responses to plan, execute, and adapt across multiple steps — booking travel, managing workflows, or conducting research independently. This lecture examines current capabilities, practical applications, significant risks around autonomy and accountability, and how to think about this evolving technology as a leader making investment decisions.

Module 2: The Strategic Business Case

5 lectures

Module 2 · Lecture 1

AI Use Cases by Function

Survey practical AI applications across core business functions. This lecture maps specific use cases in HR, Finance, Marketing, Operations, and Customer Service. You'll identify patterns that make use cases successful and understand how to balance proven applications with strategic experiments.

Module 2 · Lecture 2

Competitive Dynamics: What Are Your Competitors Doing?

AI strategy doesn't happen in isolation. This lecture examines adoption patterns across industries and helps you assess your competitive landscape, understand where AI is becoming table stakes versus where it offers differentiation.

Module 2 · Lecture 3

Cost-Benefit Analysis Frameworks

Move beyond vendor hype to rigorous evaluation. This lecture provides frameworks for mapping all costs, quantifying benefits across efficiency, revenue, and risk reduction, and building defensible business cases.

Module 2 · Lecture 4

Case Studies: Cautionary Tales

Learn from documented AI failures to avoid repeating them. This lecture examines real cases including Amazon's biased recruiting tool, Air Canada's chatbot lawsuit, and IBM Watson's struggles at MD Anderson.

Module 2 · Lecture 5

Identifying High-Value Opportunities in Your Organization

Turn frameworks into action for your specific context. This lecture provides a systematic approach to scanning your organisation for AI opportunities, evaluating them against criteria that predict success, and prioritising based on value, feasibility, and strategic fit.

Module 3: Hands-On: Using AI Tools Effectively

9 lectures

Module 3 · Lecture 1

Getting Organizational Approval (IT, Legal, Compliance)

Before experimenting with AI tools, get proper governance in place. This lecture walks you through why approval matters, which stakeholders to involve, what questions each will ask, and how to frame conversations productively.

Module 3 · Lecture 2

The AI Tool Landscape and How to Choose

Navigate the crowded AI marketplace with clarity. This lecture surveys the major platforms — ChatGPT, Claude, Gemini, and Copilot — examining their strengths, limitations, and ideal use cases.

Module 3 · Lecture 3

Setting Up Your AI Accounts

Get hands-on with step-by-step guidance for creating accounts on the major AI platforms. This practical lecture walks you through the sign-up process for ChatGPT, Claude, Gemini, and Copilot.

Module 3 · Lecture 4

Prompt Engineering: Persona, Context, Goal

Master the foundational skill of communicating effectively with AI. This lecture introduces the PCG framework: Persona, Context, and Goal. Through examples and practice, you'll learn to craft prompts that consistently produce useful, relevant results.

Module 3 · Lecture 5

Prompt Engineering: Examples and Iteration

Advance your prompting skills with techniques that dramatically improve output quality. This lecture covers using examples (few-shot prompting), iterative refinement, and strategies for complex multi-step tasks.

Module 3 · Lecture 6

Critical Thinking: Hallucinations and How to Catch Them

AI tools can produce confident-sounding nonsense. This lecture explains why hallucinations happen, teaches you to recognise warning signs, and provides verification strategies to catch errors before they cause problems.

Module 3 · Lecture 7

Using AI for Data Exploration

Discover how to have a conversation with your data. This lecture shows you how to upload spreadsheets and ask questions in plain language — descriptive analysis, comparative analysis, pattern identification, and anomaly detection.

Module 3 · Lecture 8

From Analysis to Boardroom Presentation

Transform AI-generated analysis into executive-ready communications. This lecture covers using AI to create compelling visualisations, structure findings for different audiences, and prepare presentations that drive decisions.

Module 3 · Lecture 9

Workshop: Solve a Real Business Problem with AI

Put everything together in a guided hands-on exercise. This workshop walks you through a complete workflow: defining a business problem, crafting prompts, evaluating and iterating on the response, verifying outputs, and polishing for final use.

Module 4: Risk, Security, Ethics, and Legal

5 lectures

Module 4 · Lecture 1

Data Privacy and Security Risks

Understand the real risks of AI adoption and how to manage them. This lecture covers data exposure through prompts, training data concerns, data retention and storage, security vulnerabilities, and shadow AI.

Module 4 · Lecture 2

Legal Considerations: IP, Liability, Contracts

Navigate the evolving legal landscape around AI. This lecture addresses intellectual property questions, examines liability implications, and identifies critical contract provisions to negotiate with AI vendors.

Module 4 · Lecture 3

Regulatory Landscape: EU AI Act and Industry-Specific Requirements

Understand the rules governments are putting in place. This lecture focuses on the EU AI Act — the world's first comprehensive AI regulation — explaining its risk-based framework and compliance timeline.

Module 4 · Lecture 4

Ethical Frameworks for Responsible AI Use

Move beyond compliance to genuine responsibility. This lecture introduces the FATE framework — Fairness, Accountability, Transparency, and Ethics — and provides practical guidance for evaluating AI systems.

Module 4 · Lecture 5

Building Internal AI Policies and Governance

Create the governance infrastructure that enables responsible AI adoption. This lecture provides frameworks for building AI policies across four pillars: policies, processes, people, and technology.

Module 5: Building Your AI Strategy: Planning for Implementation

8 lectures

Module 5 · Lecture 1

Assessing Organizational AI Readiness

Before building strategy, understand where you're starting from. This lecture introduces five dimensions of AI readiness: strategic clarity, data readiness, technical infrastructure, talent and skills, and organizational culture.

Module 5 · Lecture 2

Data Readiness: The Foundation Most Overlook

Data readiness deserves its own lecture because it's where most organisations are least prepared. This lecture examines what data readiness actually means — accessibility, quality, governance, and infrastructure.

Module 5 · Lecture 3

Build vs. Buy vs. Partner Decisions

Make strategic choices about how to acquire AI capabilities. This lecture examines three approaches: building in-house, buying off-the-shelf, and partnering with experts.

Module 5 · Lecture 4

Vendor and Tool Evaluation Criteria

When you decide to buy, evaluate options systematically. This lecture provides six categories of evaluation criteria: functional fit, technical requirements, vendor viability, data practices, total cost of ownership, and support.

Module 5 · Lecture 5

Structuring AI Capabilities: Teams, Roles, Reporting

Organise for AI success. This lecture examines different structural models — centralised centres of excellence, distributed embedded teams, and hybrid approaches — with guidance on when each works best.

Module 5 · Lecture 6

Change Management: Getting Your People on Board

Technology changes are fundamentally people changes. This lecture addresses resistance, communication strategies, training approaches, and cultural shifts required for AI adoption.

Module 5 · Lecture 7

Creating Your AI Roadmap

Translate strategy into a sequenced plan. This lecture guides you through roadmap development: defining strategic horizons, sequencing initiatives, building in decision points, and creating accountability mechanisms.

Module 5 · Lecture 8

Workshop: Draft Your 90-Day AI Action Plan

Turn learning into immediate action. This workshop guides you through creating a concrete 90-day plan: selecting your first initiative, defining success criteria, identifying resources, establishing milestones, and anticipating obstacles.

Module 6: Implementation and Measuring Success

4 lectures

Module 6 · Lecture 1

Designing Effective Pilot Projects

Pilots are how you learn fast with bounded risk. This lecture covers the four purposes of pilots and seven essential design elements: clear scope, success metrics, duration, participant selection, comparison baseline, support structure, and learning capture.

Module 6 · Lecture 2

Defining KPIs and Measuring ROI

"What's the ROI?" is the question every executive asks — and one of the hardest to answer well. This lecture provides frameworks for categorising value and selecting meaningful KPIs.

Module 6 · Lecture 3

Iteration, Evaluation, and Scaling

After pilots, you face a decision: scale, iterate, or stop. This lecture provides frameworks for making that call, guidance on scaling successfully, and principles for continuous improvement.

Module 6 · Lecture 4

Sustaining Momentum: From Pilot to Culture

Moving from successful pilots to an AI-enabled organisation requires sustained effort. This lecture examines what AI maturity looks like and strategies for building momentum across your organisation.

Closing: Summary and Next Steps

3 lectures

Closing · Lecture 1

Key Takeaways

Consolidate the most important ideas from the entire course. This lecture distils seven key takeaways — one from each major section — that will serve as touchstones for your AI leadership journey.

Closing · Lecture 2

Resources for Continued Learning

This course is a beginning, not an end. This lecture points you to resources for staying current as AI evolves: recommended information sources, communities of practice, and skill development pathways.

Closing · Lecture 3

Where to Get Help: Next Steps and Support Options

You don't have to navigate AI implementation alone. This lecture outlines support options available to you and how to connect with peer networks of leaders on similar journeys.

What You'll Learn

Course Highlights

The AI Landscape

Understand the current state of AI technology, key players, and how different AI systems work — from machine learning to generative AI.

Strategic Business Case

Learn to evaluate AI opportunities, build compelling business cases, and identify where AI can create the most value in your organization.

Hands-On AI Tools

Get practical experience using AI tools effectively. Learn prompting techniques, workflow integration, and productivity optimization.

Risk & Ethics

Navigate the complex landscape of AI security, privacy, legal compliance, and ethical considerations that every leader must understand.

AI Strategy

Develop a comprehensive AI strategy for your organization, including roadmaps, resource planning, and change management.

Implementation

Learn best practices for AI implementation, measuring success, and scaling AI initiatives across your organization.

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