AI Use Case Prioritization
AI use case prioritization is the structured process of ranking candidate AI initiatives — typically across value (revenue, cost, risk reduction), feasibility (data, technology, talent), risk (regulatory, reputational), and strategic fit — to concentrate investment on the small set of bets that will move the P&L.
Full definition
A working method: (1) inventory 30-100 candidate use cases via business-unit interviews; (2) score each on 4-6 weighted dimensions; (3) plot on a value-vs-feasibility 2x2; (4) commit funded teams only to top-quadrant cases; (5) re-score quarterly. The single most common mistake is pursuing many small "experiments" simultaneously — a portfolio of 30 unfunded pilots beats one funded production deployment in headlines and loses to it in P&L. AIDOLS runs prioritization workshops as part of /ai-strategy-consulting/.
Why it matters
McKinsey's 2024 State of AI found that the gap between AI leaders and laggards is now mostly explained by use-case selection discipline, not technology choice. Picking the right 5 cases beats executing the wrong 30.
Example
A 12-week prioritization across 47 candidate use cases for a manufacturer concentrates 80% of next-year AI budget on three: predictive maintenance, contract analysis, and field-service copilot — projected $42M annualized impact vs. $4M from the prior scattered portfolio.
Related terms
- AI StrategyAn AI strategy is a written, board-level plan for how an organization will use AI to create competitive advantage — naming the business goals, prioritized use cases, required capabilities, governance posture, partner choices, and a 12-36 month investment plan.
- AI ROIAI ROI is the financial return generated by an AI investment relative to its total cost — including build, inference, MLOps, governance, and change-management cost — and the metric that ultimately determines whether an AI program survives the next budget cycle.
- AI ReadinessAI readiness is an organization's practical capacity to deploy and operate AI safely and economically — measured across data foundations, technology stack, talent, governance, and operating model — and the prerequisite to any large AI investment paying off.
- AI Operating ModelAn AI operating model defines how AI capability is structured across an enterprise — centralized, federated, hub-and-spoke, or platform-plus-product — and how decisions, talent, data, platforms, and accountability flow between the center and business units.
Source & further reading
Primary source: McKinsey — "The State of AI 2024" (2024).
Citation policy: this entry is part of the AIDOLS AI Implementation Glossary and may be quoted for research, journalism, and education with attribution to aidolsgroup.com/fr/glossary/ai-use-case-prioritization/.