What to keep, what to replace, what to build, with a clear-eyed view of what actually works. A framework for making sensible decisions about your data landscape in the age of AI.
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Why the vast majority of AI pilots fail, and the uncomfortable question nobody asks.
The five-level hierarchy, why you can't skip levels, and why ML should come before LLMs.
Hallucinations, drift, and operational risk. What it actually takes to deploy AI safely.
Why "ask in English, get an answer" is brutally hard, and how to make it work anyway.
Proportionate controls, EU AI Act compliance, and governance that enables rather than blocks.
Why "let's wait for better AI" is a strategic error, and the compounding cost of delay.