Dr.Walter KurzMBA, M.Sc.

Understand what AI, machine learning, and generative AI really are, in plain language and with no technical background.

• Defining Artificial Intelligence
• What Generative AI Is
• Common Myths About AI
• + 3 more

Apply AI to your correspondence, routine tasks, and the everyday tools you already use to get real work done faster.

• Drafting Business Correspondence
• Breaking a Task into Model-Sized Steps
• Combining AI with Everyday Tools

Draft text, generate your first image, and refine both to a professional standard with AI tools you can start today.

• Editing for Clarity and Concision
• Your First Generated Image
• Writing an Image Prompt

See what happens inside a chat assistant: how tokens, prediction, and training combine to make it sound so fluent.

• How Models Read Text as Tokens
• How Next-Token Prediction Builds Sentences
• The Context Window

Recognise fabricated answers and invented sources, and build the everyday habit of checking before you trust a reply.

• What a Hallucination Is
• Fabricated Sources and Citations
• The Verification Mindset

Understand the ethics and privacy limits of AI, and learn to recognise synthetic media so you can use it responsibly.

• What AI Ethics Covers
• Privacy by Design for AI Use
• Recognising Synthetic Media

Run a productive first session with an assistant and learn to write clear prompts that get you the result you want.

• Running an Assistant Session
• The Four Parts of a Prompt
• Reading a Reply Critically

How AI creates and destroys economic value in an organisation that competes, and how to read a proposal in those terms: where value lands, what makes an advantage hold, what it costs to keep rather than to build, and what a mistake costs.

• Name the business problem first
• AI changes tasks before it changes jobs
• Cost, revenue, risk: where AI value lands
• + 11 more

Building the apparatus that decides and evidences in the officer's absence: the mandate and its limits, the inventory, decision rights and evidence standards, the incident and harm path, refusal, and the management system as a whole.

• What a real mandate consists of
• What stays with other people
• Where you sit and who you can reach
• + 37 more

A defined AI implementation carried from concept to something that runs, with practitioners alongside, together with the governance, evaluation and legal artefacts the earlier parts taught. The integration point of the programme.

The English the role actually has to survive: a supplier negotiation, an audit conversation, a regulator's question, a board's scepticism.

Presenting AI work to decision-makers and expert panels, negotiating, handling hostile questions, and holding a refusal under pressure.

Choosing where the organisation competes with AI and what it declines, how AI changes what it sells and on what economics, and how to commit to a direction before the capability is proven.

• A strategy is what you decline
• From ambition to an AI thesis
• What this does to the industry, not only to you
• + 12 more

Running AI as a standing function rather than a series of projects: portfolio and funding cadence, benefit realisation, supplier control, adoption, and knowing when operating evidence has overturned the strategy.

• Set the operating calendar
• Intake and triage
• Run the portfolio review
• + 12 more

How these systems work at the level where a non-engineer can reason about them, where their error comes from, and why they are attackable at all. Produces somebody who can ask the question that exposes a weak answer.

• The words people use in the room
• What learning from data actually means
• Where the data came from, and what it leaves out
• + 24 more

What an AI system consists of end to end, what working means for a given use and who sets that floor, how evaluation and human oversight are designed, and how to accept or refuse what a supplier delivers.

• From a use case to a system boundary
• The parts of an AI system, end to end
• What "good enough" means for this use
• + 26 more

What a sourcing choice commits an organisation to: where the system runs, where models come from, how cost behaves as use grows, what lock-in actually consists of, and what a real exit requires.

• The five things that actually differ between offers
• Where the system runs, and what that decides
• Whose ground it sits on
• + 22 more

Placing a system inside the regulation that governs it: classification, the role the organisation occupies, the obligations that follow, and the point where it stops being the officer's own call. Taught as a method, since the applicable framework set varies by jurisdiction and sector.

• The fact pattern you will keep reusing
• What AI regulation is trying to do
• Is this even an AI system in the legal sense
• + 21 more

Lawful use of data in AI systems, the rights of the people in it, what transparency is owed, and the judgements that remain once the law is satisfied.

• Map the data in the system
• Is there personal data here at all
• Lawful basis for using data this way
• + 23 more

Where liability lands, what a supplier contract does and does not give you, intellectual property in both directions, employment consequences, and the documentation that answers a question two years later.

• Where harm becomes liability
• Your own exposure as the officer who signed
• What a standard supplier contract does not give you
• + 21 more