The Field Guide to AI for Non-Tech CEOs explains the concepts that actually matter to understand what’s going on in an AI meeting, evaluating vendors, allocating capital towards AI and asking better questions.
No jargon and no hype, only the straight truth from someone who actually built and deployed real-world AI solutions.
Click on the links to go directly to the relevant topic.
Part 1. Demystifying AI and busting AI hype
- Do you need AI or merely automation?
- What the hell is a token?
- Model weights and the concept of machine intelligence
- “AI is a prediction machine.” Yes, but there’s more.
- Demystifying “context.”
- Do you really need to “train AI on your data”?
Part 2. How AI systems actually work
- What actually happens when you send a prompt to an AI API?
- Calling an AI API is trivial. So what makes an AI solution difficult?
- Do you need an agent at all? Standard AI vs agentic AI.
- If you can manage people, can you manage AI agents?
- HITL, HOTL and HOOTL: how to think of the human role in AI workflows?
- How to assess AI outputs objectively?
Part 3. The eternal question: Build or buy?
- Should you build it, buy it, or configure something you already have?
- What does an AI engineer actually do?
- Your Head of IT doesn’t seem to “get AI.” Is that actually a problem?
- Questions to ask an AI vendor when you don’t want the sales pitch.
- Are you using expensive AI to deal with cheap clerical work?
Part 4. ROI, risks and rewards of AI
- How to think about AI ROI
- Variables your CFO should know when calculating AI ROI
- Are you leaking proprietary data when using AI?
- What should be inside every serious AI system besides the AI?
- AI Transformation is really org and process redesign
- AI improves fast. Should you just wait?