How a course actually feels — so you know what to expect

Easy to enter. Hard to fake. Built around the AI decisions you make.

I want you to start a course feeling welcomed, and finish each module feeling like you can actually do something with what you learned. Here's the pattern every course follows, the rhythm I'll guide you through, and the quality bar I'll hold you to.

How each module is built

Six small steps that turn theory into something you can use.

Step	What I'm doing with you	What you walk away with
1. Business question	I frame why this AI topic matters and what decision it actually supports.	A one-sentence decision question you can carry into work.
2. Core concept	I teach the essential AI idea in plain language — no overload, no jargon for jargon's sake.	A concept note you could explain to your team in 90 seconds.
3. Model or framework	I give you a reusable tool — a canvas, matrix, design pattern, or evaluation method — and walk through how to use it.	A completed design or analysis you can adapt to your own situation.
4. Case application	We apply the concept to a realistic, deliberately incomplete AI situation.	A case decision with the reasoning written out.
5. Executive artifact	I help you convert what you've learned into something you'd actually hand to a manager.	A memo, design, evaluation report, roadmap, or recommendation.
6. Reflection and transfer	I ask you to translate the module into your own company, team, or role.	An action you've committed to, and one follow-up question for next week.

How courses stay connected

Six lenses I'll keep bringing you back to.

DataWhat data does this AI need, who owns it, how fresh is it, and how is it governed?

ModelWhat model fits this task, where is it weak, and how do you know it is good enough?

ProductWhat user moment does this serve, what are the defaults, and how does the user stay in control?

Operating impactWhat changes in workflow, headcount, cost, or service when this AI ships?

GovernanceWhat reviews, controls, and accountability does the risk of this AI deserve?

RiskWhat assumptions, failure modes, and trigger points should you be watching?

Course rhythm

How a typical six-week course unfolds with you.

Week 0You take a diagnostic, set a role-specific goal, and pick a real AI problem to apply the course to.

Weeks 1–2You learn the core concepts and build your first design or analysis. Short comprehension checks keep you honest.

Weeks 3–4You work cases, get peer or self critique, integrate across data, model, product, and risk, and revise your analysis.

Week 5You build the executive artifact for the course — memo, design, evaluation report, roadmap, or decision brief.

Week 6You and I review against the rubric, you revise, set a transfer plan, and hand off to the next course or the capstone.

The quality bar

What makes your work AI-grade.

I won't grade you on memorizing definitions. I'll coach you on whether your work would hold up in a real AI design or launch discussion: conceptual accuracy, application to a real decision, quantitative or evidence-based reasoning when it matters, explicit assumptions, evaluation, and tradeoffs, and a deliverable a practitioner or leader could actually use. The default action in every module isn't to consume content — it's to decide, design, evaluate, recommend, or revise.