Online course · Tools & Training

AI Foundations

What every leader needs to know to decide on AI: what it is, where the value is, what the risks are and which decisions are yours.

  • 7 modules
  • About 3.5 hours
  • No technical background needed
  • Open to everyoneEdition October 2026
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How the course works

Learn, practice, check

The course covers the same ground as the executive workshop AI foundations for leaders. Every figure and every case comes with its source. Examples marked illustrative are made up to show a method.

01LearnShort lessons in plain language, built on real cases and independent research.
02PracticeExercises that use what you learned: rank use cases, read the EU AI Act, test a proposal against five questions.
03CheckA knowledge check closes every module, with feedback on every answer and questions to reflect on.
04KeepA library of real cases, a glossary of 32 terms and the sources behind every figure.
The course

Seven modules

The essentials when time is short: module 1, three lessons of module 2, one of module 4, module 6 and one lesson of module 7.
Module 1 · Why this matters nowOverview

Why this matters now

How fast AI arrived, how few companies earn from it yet, and why leaders need their own understanding of it.

In this module you will learn
  • How quickly generative AI spread, and why that created a governance gap
  • Where companies stand on value from AI in 2026
  • What the data says about boards and AI expertise
  • Three responsibilities that make AI literacy a leadership task
Study time 15 min Key terms Generative AI, EBIT impact, AI literacy, board oversight
Case library

Thirteen real cases

What went right and wrong when companies used AI, and the lesson each case teaches. Open a case to read it in its lesson.

  • Mata v. AviancaNew York, 2023 Nobody checked the output, and nobody took ownership when the error surfaced.Read in module 2 →
  • Deloitte Australia2025 If AI helps to write a document that carries your name, your review must catch what AI gets wrong.Read in module 2 →
  • Morgan Stanley2023 A narrow, grounded use on your own trusted content is one of the safest places to start.Read in module 3 →
  • Klarna2024 to 2025 Measure quality as well as cost, from day one.Read in module 4 →
  • Zillow Offers2021 Scale only what has proven itself, and set stop rules before you scale.Read in module 4 →
  • Samsung2023 Give staff an approved tool with clear data rules before you need a ban.Read in module 5 →
  • Amazon recruiting toolReported 2018 A model trained on past decisions repeats past patterns. Test for bias, and keep testing.Read in module 5 →
  • Dutch childcare benefits2021 An automated risk score is a decision about people. Justify every factor in it.Read in module 5 →
  • Arup2024 Assume a familiar face and voice on a call can be fake. Verify through a separate channel.Read in module 5 →
  • Moffatt v. Air Canada2024 Your AI speaks for your company. Customers may rely on what it says, and so may courts.Read in module 5 →
  • Salesforce2025 Vendor results are a starting point, not evidence. Test agents on your own work.Read in module 7 →
  • Commonwealth Bank of Australia2025 Measure demand and quality before you change roles.Read in module 7 →
  • When a model is switched off2026 Design every important workflow with a tested fallback.Read in module 7 →
Glossary

Words to know

32 terms
Agent
Generative AI that plans and takes actions in other systems to reach a goal.
Agent washing
Relabeling assistants, chatbots or automation tools as agents without real agentic capabilities.
AI Act
Regulation (EU) 2024/1689, the EU law on AI, amended by Regulation (EU) 2026/1744.
Baseline
Today’s value of the number an AI project should move, agreed with finance before work starts.
Bias
Systematic unfairness in outcomes, often learned from historical data.
Closed model
A model offered only as a service by its provider.
Context
The documents, data and conversation a model can see when it answers.
Deepfake
AI-generated audio, image or video that imitates a real person.
Deployer
Under the AI Act, an organization that uses an AI system under its own authority.
Evaluation
Systematic testing of an AI system’s output quality.
Excessive agency
An agent with more autonomy, permissions or access than its task needs.
Fallback
A tested alternative, another model or the manual process, for when an agent or model is unavailable.
Fine-tuning
Extra training of a model on specific examples.
Gateway
A layer between your applications and AI models that lets you switch models.
Generative AI
AI that creates new text, images, code or sound.
Grounding
Connecting a model to trusted sources so that it answers from them (also RAG).
Hallucination
Confident but false output from an AI model.
High risk
Under the AI Act, uses such as recruitment or credit scoring that carry strict obligations.
Human in the loop
A design in which a person reviews or approves AI output before it takes effect.
Human-agent ratio
The balance between work done by agents and by people, set by where human contact and judgment matter.
Large language model
A generative AI model trained on large amounts of text to predict the next word.
Machine learning
AI that learns patterns from examples instead of following written rules.
Observed exposure
Anthropic’s measure of how much of an occupation’s work AI covers in real use.
Open-weight model
A model whose trained weights can be downloaded and run by others.
Prompt
The instruction or question given to a model.
Prompt injection
Input that changes a model’s behavior in unintended ways; indirect when hidden in content the model reads.
Provider
Under the AI Act, an organization that develops an AI system and puts it on the market under its own name.
Shadow AI
AI used in an organization outside any approved policy.
Stop rule
A condition, agreed in advance, under which a project is stopped.
Time horizon
The length of tasks an AI agent can complete at a given success rate, in human working time.
Token
A fragment of a word; the unit in which models read, write and charge.
Transformer
The neural network design, published in 2017, on which today’s language models build.
With your team

Learn it together

The executive workshop AI foundations for leaders covers the same ground in one session, built on your own cases and your own questions.