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Organizations should run fewer initiatives than they can imagine. Keep candidate ownership and status visible in Tasks, and use Analytics as evidence only after a pilot has produced real usage. Every active initiative consumes business ownership, user attention, IT and security cooperation, testing capacity, and change-management effort.

Four stages for selecting and governing AI initiatives

Score With Evidence

Use a simple 1–5 score, but require a short evidence note for every value.

CriterionQuestionHigh-priority signal
Business valueWill it improve an important outcome?Clear, measurable impact
User demandDo target users actively need it?Named users and workflow pull
FeasibilityAre Data, access, connections, and owners available?Realistic first version
AdoptionWill it fit the way people work?Clear users, training, and support
Strategic fitDoes it support an agreed priority?Sponsor and roadmap alignment
ComplexityIs the expected value proportionate to effort?Small bounded pilot
RiskAre consequences and controls understood?Manageable, reviewable impact

Do not let a high total compensate for an unacceptable risk, missing owner, or unavailable data. Treat these as gates.

Choose a Portfolio

Maintain three states:

  • Active: funded, owned, and within current delivery capacity.
  • Next: sufficiently defined but waiting for capacity or a dependency.
  • Explore: useful idea that needs discovery before it can be ranked.

Limit active work until each initiative has time for realistic testing and feedback. Starting ten pilots and finishing none provides less evidence than completing two well-governed pilots.

Make the Decision Visible

For each candidate, record the score, evidence, blocking gates, decision owner, status, and next review date. Revisit prioritization when business conditions, source availability, policy, or delivery capacity changes.

Approved candidates move into the Initiative Lifecycle and Governance.