Guides, a shared vocabulary, and a newsletter on data, AI, and decision-making, free to use, whether or not you've read the book.
Capture the evidence, reasoning, assumptions, and uncertainty behind an important decision before you know the outcome, so you can learn from the decision later.
Download PDFA practical guide to writing prompts that surface sharper analysis and better-framed evidence, built for leaders, not engineers.
Download PDFA short self-assessment for identifying where AI genuinely improves your team's decisions, and where it just speeds up the same old problem.
Coming soonTerms from the book, and from the broader world of AIand decision-making, defined in plain language.
The discipline of thinking clearly about how decisions actually get made in organizations, and building the systems that help good evidence turn into good outcomes.
A five-step framework, Context, Leverage, Evidence, Alignment, Resolution, for preparing, presenting, and closing the loop on any consequential decision.
The widening distance between how easy AI has made it to produce evidence and how hard it still is to know what to do with it. As evidence gets cheaper, judgment gets more valuable, not less.
The tendency to over-trust a recommendation simply because it came from a model or algorithm, even when a person would ask harder questions of a human making the same claim.
An approach to AI systems that keeps a person actively reviewing and approving key outputs, rather than letting the system act on its own, especially for high-stakes decisions.
The practice of crafting clear, well-structured instructions for an AI system so it returns sharper, more useful, and more decision-ready output.
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