How to Choose AI Priorities
Prioritize AI opportunities by strategic value, workflow readiness, risk, learning value, and the capabilities each initiative compounds.
Score the business consequence
Evaluate each opportunity against revenue, margin, customer experience, decision quality, risk, and strategic differentiation. A modest efficiency gain can matter, but it should not displace work that changes the company’s position.
Test operational readiness
Assess process clarity, data access, integration requirements, accountable ownership, and acceptable failure modes. Low readiness does not always mean no; it may reveal the foundational work that should come first.
Build a balanced portfolio
Combine near-term improvements with a small number of architecture-building initiatives. Sequence them so early work creates reusable data, controls, integrations, and operating knowledge for later work.
Common Mistakes
- Ranking only by estimated savings
- Ignoring integration and adoption effort
- Funding too many disconnected pilots
Market Signals
- The backlog is ranked by enthusiasm
- Every department labels its request urgent
- Pilots create no reusable capability