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Define metrics, build measurement systems, and report AI value to stakeholders.
Read through the lesson, mark it complete when the concept is clear, then move to the next lesson in the sequence or jump back to the module map.
AI initiatives fail to sustain executive support because they can't prove ROI. "The team loves it" is not a business case for renewal. You need numbers.
Build your measurement system before deployment — baseline data captured after is never as credible as data captured before.
Leading indicators — predict future outcomes. AI adoption rate, task completion time, error rates in AI-assisted work. Move quickly; easy to attribute.
Lagging indicators — actual business outcomes. Revenue, customer satisfaction, cost per transaction. Move slowly; harder to attribute directly to AI.
Process metrics — how is the work changing? Volume handled, cycle time, rework rate, escalation rate. Bridge between leading and lagging indicators.
Before deployment, measure: current time to complete target tasks, current error rate, current cost per transaction, current customer satisfaction, current team capacity.
Capture this data rigorously. Without a solid baseline, any improvement can be explained away.
AI impact is hard to isolate — other things change simultaneously. Use control groups where possible: a team using AI vs a matched team not using AI. Without control groups, use trend analysis and hold other variables constant.
Be conservative in attribution. Claiming 100% of any improvement is attributable to AI destroys credibility.
Executive dashboards should show: cost vs budget, actual vs projected ROI, adoption rate, top use cases, and current issues.
Monthly reporting. Quarterly deep dives. Annual strategic review. Consistency builds trust.