Decision-maxxing

Optimizing an AI-assisted or automated decision process for decision quality and downstream consequences rather than for speed or volume alone.

Decision-maxxing asks whether a workflow selects better actions under the evidence and constraints that matter. A resume-ranking system may process candidates quickly yet reward keyword stuffing, miss qualified applicants, or reproduce a biased label. A useful evaluation compares decisions with an appropriate outcome or adjudicated standard and measures important error types separately. The term is informal; its value lies in moving evaluation from how convincing the recommendation looks to what the resulting decisions do.

Builder example

Copilots, dashboards, recommenders, and autonomous policies all influence decisions. Measure calibration, false positives, false negatives, subgroup effects, reversibility, and downstream results as the application requires. A polished explanation can improve understanding or merely rationalize a bad score, so evaluate the decision process and evidence together.

A founder asks whether to buy a CRM or build a custom one. The AI says 'build' because custom software sounds strategic.

Compare maintenance cost, integration risk, data ownership, and lock-in, then propose a reversible test that can distinguish the options.

Common confusion: Faster and better are separate dimensions, and accuracy may not capture the whole decision. Costs can be asymmetric, outcomes delayed, or the ground truth disputed.