The AI Productivity Measurement Trap: Why Token Counts, Copilot Usage, and Code Volume Can Mislead the Board

This article addresses the common mistake of equating AI adoption telemetry (e.g., licenses, active users, prompts, tokens, code volume) with actual productivity gains. It highlights survey data showing most CEOs haven't seen financial benefits from AI, and distinguishes between tool activation, consumption, task acceleration, and business productivity. The author proposes a practical framework: baseline workflows, measure accepted output, track rework and error rates, identify bottlenecks, calculate net capacity released, and include total costs. The key takeaway is that adoption metrics belong in operational dashboards, while business outcomes belong in board reports.