Enterprises increasingly mandate the use of AI coding tools and announce ambitious productivity targets, yet there is little longitudinal evidence on whether such productivity targets have been reached, how, and at what cost. We present a quantitative case study of a documented enterprise "2x" mandate: in June 2025, the CTO of a mid-sized, AI-forward software company set the goal of doubling merged pull requests (PRs) per engineer with AI. Using a panel of 802 developers and 196,212 PRs (January 2024 - April 2026), we estimate the effect of AI adoption by comparing each developer's PR throughput after they started using AI tools with their own throughput before. We find that the company eventually reached the target, but only in the tenth month and not in the way the announcement implied: PR throughput barely moved at the mandate date, rose by about 12% when each developer started using AI, and then kept growing as developers accumulated use. The target was not met merely by working longer or splitting work: extra working days account for at most one-fifth of the throughput increase, and the added PRs became larger and skewed toward features and bug fixes rather than routine changes. The gain is similar across seniority levels and larger in repositories created since 2022. The cost of AI adoption fell on code review, where load per reviewer doubled and automated review overtook human review in coverage within months of the mandate. We interpret these results as strong evidence that the adoption and sustained usage of AI raises PR throughput in an enterprise setting, with two caveats: throughput is only a proxy for productivity, and the exact size of the effect remains uncertain due to a lack of truly randomized assignment in our setting. Our results have implications for organizations under AI transformations and researchers studying the impact of AI on software development productivity.

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