Every conversation with a VP of Engineering about AI training eventually arrives at the same question: "How do we measure ROI?" And almost every answer I have seen in the market is some version of lying with numbers.
What you cannot measure (honestly)
You cannot directly measure "developer productivity gain from AI tools." The concept is not well-defined enough to measure. Anyone who claims a clean "X% productivity gain" is either running a controlled academic study or making it up.
What you CAN measure
Instead of chasing a single productivity number, we track four proxy metrics: tool adoption rate, internal support ticket reduction, new-engineer onboarding time, and self-service resolution rate.
Putting it together
No single metric tells the story. But when you present leadership with a dashboard showing adoption went from 30% to 78%, internal support questions dropped 55%, new-hire onboarding shortened by 4 days, and self-service resolution saves 500+ interruptions per week — that is a credible, defensible story.
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