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How to ThinkIn the Age of AI
Principlev1PR-2154

Select pilot contexts that are representative and moderately

Select pilot contexts that are representative and moderately challenging rather than ideal or enthusiastic, to generate generalizable learning.

Why This Is a Principle

This principle derives from understanding of calibration (Domain-Specific Calibration Development) and reference class forecasting (Reference class forecasting (using base rates from similar…). It prescribes pilot selection criteria based on what enables accurate prediction of scaled results.

Source Lessons

Pilot programs as system experiments

Test systemic changes on a small scale before rolling them out broadly. A pilot program is a bounded experiment — a deliberate test of the proposed system change in a contained context where the change can be observed, measured, and refined without risking the entire organization. Pilots serve three functions: they generate evidence (does the change produce the intended outcome?), they reveal unintended consequences (what side effects emerge in practice?), and they build organizational confidence (the change has been tested and it works). System changes deployed without piloting are organizational gambles — large bets on untested designs.

Connections

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