#10 Certification Matters
As AI moves into decisions that matter, auditors will ask whether it behaves the same way every time. The future belongs to narrow models you can certify.
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#9 Narrow Beats Broad
Business value doesn't need general intelligence. A narrow, specialist model is cheaper, easier to check, and it doesn't wander off task. Specialists win.
#8 A Tool, Not An Employee
Automate tasks, not jobs. AI is very good at repeatable tasks and very bad at whole jobs — augment your people instead of trying to replace them.
#7 Your Spreadsheet Is A Training Set
Any CSV with an outcome column is training data. Export it, pick the column you want to predict, and get predictions back on new rows.
#6 Is Your Model Any Good?
Five validation checks, starting with the most important: how did it score on data it never saw during training? That's the difference between memorising and understanding.
#5 The Cheapest AI Project
The cheapest useful AI project needs one label column and about a week. A narrow, well-defined prediction is a small problem wearing a scary name.
#4 Five Questions For Any AI Vendor
The five purchasing questions that separate a real model from a thin wrapper. A real answer is specific; a wrapper gets vague fast.
#3 When To Use An LLM
Draft with an LLM, never decide with one. It hallucinates confidently and can't know what it's missing, so use it to write — not to choose.
#2 Churn, End to End
An eight-step walkthrough of building a real churn model from your own data — a project with a beginning and an end, not an open-ended initiative.
#1 AI Isn't Magic
AI is statistical prediction, not magic. If the answer you want is already a column in your data, you fit a model to it rather than prompt for it.