AI Feature KPI & Outcome Tree Builder
Connect adoption, accepted task outcomes, user effort and costs to the economics of an AI product feature.
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The form starts with hypothetical examples. Replace them with your own measurements or assumptions. Inputs and exports stay in this browser.
What decision does this help answer?
What business outcome should a successful AI feature actually improve?
Calculation and methodology
Construct an outcome tree from monthly eligible users, feature adoption, tasks per adopting user, accepted-task rate, time saved per accepted task and costs. Show usage volume, accepted outcomes, work minutes saved and approximate net benefit. Export the business-metric relationships as a decision record.
Limitations and responsible interpretation
Time saved should be measured against a baseline rather than assumed; wage-equivalent time is not automatically revenue. Track guardrail metrics such as severe failures, user satisfaction, privacy incidents and review burden. The calculator is not a forecast of realized profits.
Research and supporting guidance
This planning tool complements Ninth Post's related AI product analysis. Consult the underlying technical guidance or official framework where applicable, and see our editorial methods. The tool does not independently validate submitted assumptions, current vendor prices or legal obligations.