The Four Dimensions of AI Opportunity Prioritisation
Business value
Quantify the expected impact of each opportunity in business terms: cost reduced, revenue generated, time saved (translated to cost equivalent), error rate reduced (translated to remediation cost). Use conservative estimates based on measured current-state baselines, not optimistic projections. Opportunities where the business value cannot be quantified should be deprioritised until it can be — "strategic" is not a substitute for measurable impact.
Technical feasibility
Assess the technical complexity and risk of each opportunity: data readiness (is the required data accessible, clean, and sufficient volume?), AI capability fit (does current AI capability reliably solve this type of problem?), integration complexity (how many systems need to connect, and how accessible are they?), and team capability (can the team build and maintain this with available skills or manageable upskilling?).
Strategic sequence
Some opportunities unlock others. A document processing pipeline built for opportunity A also supports opportunities B, C, and D. A knowledge base built for customer support also supports sales enablement. Strategic sequencing means choosing early projects that build reusable infrastructure rather than isolated one-offs — each project compounds the return on the foundational work.
Risk and reversibility
Customer-facing AI deployments have higher risk than internal tools. Regulated process automation has higher compliance risk than unregulated. Irreversible data transformations have higher risk than reversible ones. High-risk opportunities should require higher expected value to justify inclusion in the near-term roadmap.