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Measuring Real Productivity with AI Guide

"AI made us 55% faster." That sentence reaches leadership meetings every day, almost always with no measurement behind it: it comes from a vendor's slide, an internal survey, or a calculation of hours saved that nobody checked. This guide teaches you what to measure and what not to believe before claiming how much your team's productivity changed with AI: the DORA delivery metrics and its 2025 AI Capabilities Model, the SPACE, DevEx and Core 4 frameworks, why speed is never reported without stability, why perception is not evidence, how to design a before and after measurement with a comparison group, which metrics turn into traps, and how to report it to leadership without overselling. You learn it as Head of Engineering at **Vela Viajes**, a LATAM travel booking platform with 70 engineers in 9 squads. The board asked the team to prove that the AI licenses are worth what they cost. The vendor promises 55%, the survey says 85% of the team is faster, and the CFO has already calculated that the licenses return 5.4 times their cost. Module by module you build an answer that holds up to questions: a measurement charter, a baseline, a balanced metric set, a speed and stability ledger, a design with a comparison group, an audit of trap metrics, a findings report, and a one-page summary for the board. There is no code to write and no statistics to know. The math is done in tables and by hand, and the studies (DORA, METR, controlled experiments, industry surveys) come with their source and date.