AI initiatives often stall because the team cannot connect model metrics to business value quickly enough. Leading indicators create that bridge and make it possible to steer investments before revenue or cost outcomes fully materialise.
Map the value chain. For each use case, link model-level signals (latency, accuracy, groundedness, refusal rates) to user behaviours (task completion, escalation avoidance, time saved) and then to financial levers (conversion, churn, margin, risk losses avoided). Make those links explicit in a measurement plan.
Choose indicators you can observe weekly: assistant adoption, actions executed per session, containment rates, average handling time, refund avoidance, or self-serve completion. Pair them with guardrail metrics such as incident counts, policy breaches and hallucination rates.
Instrument deeply. Add structured telemetry to prompts, tools and content sources so you can slice performance by audience, intent and data freshness. Make dashboards that product, risk and operations leaders all use, not just data teams.
Close the loop with experiments. Run A/B tests on prompt or policy variants, and couple them with cohort analysis to ensure improvements hold over time. Where controlled experiments are hard, use pre/post designs with strong operational controls.
By treating value measurement as a capability—not a report—you create a feedback system that de-risks investment, accelerates learning and keeps AI programs aligned to outcomes that matter.