--- title: "Measuring for AI success and quality improvement" url: https://daily.dev/posts/measuring-for-ai-success-and-quality-improvement-vo2t7mcwf source_url: https://lisacrispin.com/2026/05/21/measuring-for-ai-success-and-quality-improvement type: article source: "Agile Testing" published: 2026-05-21T15:28:51.057Z updated: 2026-05-21T15:29:11.804Z tags: ["testing", "dora"] reading_time: 4 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Measuring for AI success and quality improvement **[Agile Testing](https://daily.dev/sources/lisacrispin)** · 4 min read · 0 upvotes · 0 comments ## Summary A quality engineering perspective on measuring AI adoption success using the DORA AI Capabilities Model. The approach advocates for small, iterative experiments with targeted metrics rather than chasing vanity metrics like code coverage or bug counts. The DORA AI Capabilities Model Report identifies seven key capabilities and provides guidance on prioritization, measurement, and common obstacles. The post also references DORA's ROI of AI-Assisted Software Development report to justify the upfront investment needed before teams cross the 'J-curve of AI value realization'. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#testing](https://daily.dev/tags/testing), [#dora](https://daily.dev/tags/dora) [View this post on daily.dev](https://daily.dev/posts/measuring-for-ai-success-and-quality-improvement-vo2t7mcwf)