PRIORITIZATION

How to prioritize AI use cases

A long list of AI ideas is not a roadmap. Rank each candidate against the same dimensions and force explicit tradeoffs.

Direct answerDecision frameworkFree tool

Use one scoring frame for every idea

Consistency makes tradeoffs visible.

Value1–5How much recurring effort or outcome can change?
Fit1–5Does the task suit AI capabilities?
Readiness1–5Are data and systems accessible?
Risk1–5How costly is a wrong result?
Effort1–5How hard is integration and change?

Separate quick wins from strategic bets

ProfileMeaningAction
High fit / low effortQuick winPilot now
High value / high effortStrategic betValidate assumptions first
Low fit / high valueProcess problemRedesign before AI
Low value / low effortDistractionUsually deprioritize

Do not hide risk inside one total score

Keep risk visible as its own dimension. A use case can score highly on economic value while still requiring explicit approval gates, logging or limited scope.

Build a sequence, not a ranking

Some opportunities become easier after another is implemented. For example, clean CRM capture can enable stronger account-health summaries later. Map dependencies before finalizing the roadmap.

Frequently asked questions

Should ROI be the biggest weighting?

No. ROI without readiness or control can produce a bad first project. Economic value matters, but it should not erase feasibility or risk.

How do you compare revenue-generating and cost-saving use cases?

Express each in a common economic range, but keep confidence separate. Revenue estimates are often more uncertain than measured labor effort.

How often should priorities be revisited?

Revisit after major process, system or policy changes and after each pilot, because evidence from one implementation can change the feasibility of others.

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