{"id":4862,"date":"2026-07-08T11:23:22","date_gmt":"2026-07-08T15:23:22","guid":{"rendered":"https:\/\/workai.tv\/news\/2026\/07\/ai-engineering\/add-review-cycles-and-time-to-adoption-phases-in-the-usage-api\/"},"modified":"2026-07-08T11:23:22","modified_gmt":"2026-07-08T15:23:22","slug":"add-review-cycles-and-time-to-adoption-phases-in-the-usage-api","status":"publish","type":"post","link":"https:\/\/workai.tv\/news\/2026\/07\/ai-engineering\/add-review-cycles-and-time-to-adoption-phases-in-the-usage-api\/","title":{"rendered":"Add review cycles and time to adoption phases in the usage API"},"content":{"rendered":"<h2>Share with your CTO<\/h2>\n<p>GitHub is sharpening the ROI case for Copilot by expanding its <a href=\"https:\/\/github.blog\/changelog\/2026-07-07-add-review-cycles-and-time-to-adoption-phases-in-the-usage-api\/\" target=\"_blank\" rel=\"noopener nofollow\">usage metrics API with two new code-review velocity fields<\/a>: median time from pull request creation to first review, and median number of review cycles before merge. Both metrics are segmented by AI adoption phase, meaning enterprises can now directly compare how teams with heavy Copilot usage perform in code review against teams that barely touch it. Available in 1-day and 28-day enterprise and organization reports.<\/p>\n<h2>What this means for your business<\/h2>\n<p>The move from &#8220;are developers using Copilot?&#8221; to &#8220;is Copilot changing how code flows through the pipeline?&#8221; is the maturity jump most engineering organizations haven&#8217;t made yet. Review latency and cycle count are the right places to look. A developer who writes faster but submits code that requires four back-and-forth review cycles has not actually accelerated delivery. These metrics close that gap.<\/p>\n<p>The segmentation by adoption phase is what makes this analytically useful rather than decorative. GitHub&#8217;s adoption cohorts bucket developers by depth of Copilot usage, so CTOs can now run a concrete comparison: teams in the highest adoption phase, do their pull requests sit waiting for review for 90 minutes or 240 minutes? Do they merge after two review cycles or five? That&#8217;s the kind of before-and-after framing a board-level productivity conversation requires, and most enterprises have been building it by hand in Looker or Tableau until now.<\/p>\n<p>The tradeoff is real: this data is scoped to merged pull requests only, which means abandoned or rejected PRs disappear from the analysis entirely. If Copilot-heavy teams are closing out low-quality PRs at higher rates before merge, the metrics will look better than the underlying reality. Any serious interpretation requires pairing this API data with PR abandonment rates from a separate query.<\/p>\n<h2>Concept deep-dive: AI adoption phase cohorts<\/h2>\n<p>GitHub&#8217;s adoption phase cohorts classify individual developers by how intensively they use Copilot, then group all their pull requests into that bucket for reporting. Think of it like customer segmentation applied to your own engineering org. The segmentation exists because aggregate averages mask everything: a team averaging 60% Copilot acceptance rate might contain ten power users and forty non-users. Cohorts let you isolate the signal. The business connection is that ROI calculations for seat licenses finally have a denominator that reflects actual behavior, not just provisioned access.<\/p>\n<p><em>Based on reporting from <a href=\"https:\/\/github.blog\/changelog\/2026-07-07-add-review-cycles-and-time-to-adoption-phases-in-the-usage-api\/\" target=\"_blank\" rel=\"noopener nofollow\">Add review cycles and time to adoption phases in the usage API<\/a>, originally published 2026-07-08 00:53:00.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Share with your CTO GitHub is sharpening the ROI case for Copilot by expanding its usage metrics API with two new code-review velocity fields: median time from pull request creation to first review, and median number of review cycles before merge. Both metrics are segmented by AI adoption phase, meaning enterprises can now directly compare [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":4863,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[145],"tags":[],"tmauthors":[],"class_list":["post-4862","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai-engineering"],"_links":{"self":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/4862","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/comments?post=4862"}],"version-history":[{"count":0,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/4862\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media\/4863"}],"wp:attachment":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media?parent=4862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/categories?post=4862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tags?post=4862"},{"taxonomy":"tmauthors","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tmauthors?post=4862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}