KPIs for AI Initiatives

Choosing the right metrics for AI projects is harder than it sounds, because the obvious technical metrics often don't tell you what you actually need to know. Model accuracy is important, but a model that's 95% accurate and never gets used is worth less than one that's 85% accurate and fully adopted by your team. Good AI KPIs operate at multiple levels: technical performance (accuracy, latency, reliability), operational impact (time saved, throughput increased, errors reduced), business outcomes (revenue generated, costs avoided, customer satisfaction improved), and adoption metrics (how many people are actually using it, and how often). The trap many organisations fall into is measuring what's easy rather than what matters, or tracking too many metrics without a clear hierarchy. Start with the business outcome you're trying to achieve and work backwards to identify the leading indicators that tell you whether you're on track. Review your KPIs regularly, because what matters often shifts as an AI initiative moves from pilot to production. And be wary of vanity metrics that make dashboards look impressive but don't connect to decisions anyone actually needs to make.