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Product ai fit

Skill jpoindexter/product-management-skills/skills/product-ai-fit

Decide whether a product problem should use AI and define the appropriate human-AI workflow. Use when a team starts with a model or agent idea, must compare AI with deterministic software or process change, needs an AI use-case brief, or must assess data, uncertainty, value, feasibility, cost, and fallback fit.From its SKILL.md

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npx -y skills add jpoindexter/product-management-skills --skill product-ai-fit

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SKILL.md

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Product AI Fit

Start with a user decision or workflow that benefits from interpretation, generation, prediction, ranking, or adaptive behavior. Recommend AI only when it creates enough value to justify uncertainty and operating cost.

Inputs

  • User, job, workflow, and current alternative
  • Required output or decision
  • Tolerance for error, latency, variability, and explanation
  • Available data, feedback, and permissions
  • Expected frequency, value, and cost
  • Human expertise and escalation capacity

Workflow

  1. Define the user outcome and the part of the workflow that is currently constrained.
  2. Compare four approaches: no change, process or policy change, deterministic software, and AI-assisted or AI-automated behavior.
  3. Identify the AI capability required: classify, predict, retrieve, rank, generate, perceive, or plan.
  4. Determine whether examples, labels, context, feedback, and evaluation data exist or can be obtained responsibly.
  5. Analyze the cost and consequence of wrong, delayed, inconsistent, or manipulated outputs.
  6. Choose the human role: author, reviewer, approver, exception handler, or recipient.
  7. Define confidence handling, fallback, explanation, correction, and user control.
  8. Estimate value per successful task, cost per task, latency budget, and operational burden.
  9. Recommend AI, deterministic software, hybrid workflow, or no build, with the smallest validating prototype.

Output contract

Return workflow and problem, alternatives, AI capability, value mechanism, data requirements, error-cost analysis, human role, fallback, economics, recommendation, prototype, and decision rule.

Quality gate

  • The problem exists without mentioning AI.
  • AI provides a specific advantage over deterministic alternatives.
  • Uncertainty is visible in the user experience and operating model.
  • Evaluation and feedback data are plausible.
  • Cost, latency, privacy, and failure recovery are included.

Avoid

  • Choosing a model before defining the task
  • Using AI where rules are adequate and safer
  • Assuming natural interaction means reliable behavior
  • Automating high-impact decisions without meaningful oversight
  • Ignoring the cost of review and exceptions

Source grounding

Operational synthesis informed by AI use-case, workflow, uncertainty, and lifecycle concepts in Building AI-Powered Products and production trade-offs in Designing Machine Learning Systems.

What ships with it: 1 file

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