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
- 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
- Demo vs AI wrapper vs real AI solution
- APIs, consumer AI and data privacy issues
- The anatomy of an AI API call
- What’s MCP, and what’s the difference with an API?
- Do you need to use open-weight models?
- How much does inference infrastructure actually cost?
- How to assess AI outputs objectively?
- On the risk of AI going rogue
Part 3. How to think of Org Design in a post-AI era?
- HITL, HOTL and HOOTL: how to think of the human role in AI workflows?
- If you can manage people, can you really manage AI agents?
- Should your AI org design just be a replica of your org chart?
- What does an AI engineer actually do?
- Do you need a CAIO?
The below index is for reference only and may change by the time of publication
Part 4. ROI, risks and rewards of AI
- Should you build it, buy it, or configure something you already have?
- How to think about AI ROI (financials AND non financials)
- Variables your CFO should know when calculating AI ROI
- AI improves fast. Should you just wait and see?
- Cybersecurity and AI