GitHub added a return-on-investment section to its Copilot impact dashboard, giving engineering leaders a built-in way to see quantified business impact from AI coding tool adoption, hours saved, activity trends, cost figures, rather than having to stitch that picture together manually from raw usage metrics. This lands at a moment when plenty of engineering organizations are under real pressure to justify continued or expanded AI tooling spend with something more concrete than adoption rate alone, especially as the same industry survey data showing rising AI code adoption is also showing rising code-review burden and unclear net productivity gains once verification time is accounted for. A built-in ROI view that ships with the tool itself, rather than requiring a custom internal dashboard built on top of raw usage exports, lowers the bar for engineering leaders to actually have that conversation with finance or executive stakeholders using real numbers instead of anecdote. The more interesting structural point for platform and DevEx teams is what this signals about where AI coding tool vendors expect the adoption conversation to go next: from "are developers using this" to "is this actually worth what we're paying for it," which is a harder, more skeptical question that requires genuinely honest reporting on both the time saved writing code and the time spent reviewing and fixing it. Teams evaluating or renewing AI coding tool contracts should treat a vendor-provided ROI dashboard as a useful starting data source, but pair it with their own review-time and defect-rate tracking, since a vendor's dashboard has an obvious incentive to present the numbers that favor continued adoption.