gettyAI coding tools are helping development teams write and ship software faster, but faster output doesn’t automatically translate into better outcomes. When code is generated at a higher volume, hidden quality, security and maintenance issues can also move through the pipeline more quickly.That’s why teams may need to rethink which metrics best reflect sustainable progress. Here, members of Forbes Technology Council share the KPIs development teams should monitor more closely as AI coding tools reshape how software is built, reviewed and released.Mean Time To Detect DefectsAI coding tools expedite delivery but don’t guarantee judgment. They confidently generate plausible code that passes review but contains subtle logic flaws. As release velocity increases, defects compound faster than teams can notice. Mean time to detect defects measures how quickly you catch what slipped through. When AI writes the code, human oversight needs sharper instrumentation, not less. - Kshitij Mahant, Cisco Systems Inc.Defect Escape RateDefect escape rate is a KPI you should pay attention to. AI tools generate code faster, but speed means nothing if more bugs reach production. When this metric rises, it signals that review discipline is slipping. Faster delivery with degraded quality just accelerates technical debt and erodes user trust. - Steven Singer, Julius Silvert, Inc.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?Dependency Governance CoverageThe KPI I’d watch is the gap between dependencies entering your environment and dependencies under active governance. Most dev teams track velocity. What isn’t measured is how much is entered that no human deliberately selected. AI tools suggest packages, developers accept them and they’re in production before anyone has evaluated their provenance. Your artifact manager is the control point. If it isn’t tracking what came in and whether it was reviewed, that’s the gap to close. - Abby Kearns, ActiveStateCode-To-Review RatioWatch the ratio of code written to code reviewed. AI accelerates generation but review bandwidth stays fixed: Teams start rubber-stamping PRs, and that’s where quality quietly collapses. A good proxy: When something breaks, how long does it take to find the owner? If nobody can confidently claim a module, you’re shipping faster into a black box. - Joe Zein, Soal LabsChange Failure RateChange failure rate is the DORA metric most likely to degrade as AI coding tools quietly speed up delivery. AI compresses the writing step, not the understanding step. If deployment frequency and CFR rise together, that’s real acceleration. - Anna Meadows, CodeROIMean Time To RemediateMean time to remediate is the most overlooked KPI for development teams. The faster the code is produced, the faster vulnerabilities can accumulate. The ability to detect and fix security issues quickly matters more than ever. If MTTR isn’t shrinking alongside delivery speed, teams are simply building up risk at scale. - Andrea Scaduto, SecureFlagReview Throughput Per Reviewer-HourThe KPI to watch is review throughput per reviewer-hour. AI tripled the rate at which code gets written; human comprehension didn’t scale to match. Teams shipping double the PRs while reviewers rubber-stamp them are accumulating a debt that surfaces months later as incidents and orphaned code. Shrink PR size to comprehension capacity, not generation capacity. - Lev Yatsemyrskyi, Qube Research & TechnologiesHuman Effort Before MergeAs a dev team, you are always concerned with some standard KPIs, no matter the technology, including velocity, quality coverage and deployment frequency. But probably the most important in the age of AI coding tools is something I would call “human effort before merge.” The efficiency of AI coding tools should be measured by how much time a human engineer needs to spend on that piece of code—giving context, prompting, reviewing, testing—before it is merged to master or production. - Ram Dhiwakar Seetharaman, SyneraRollback RateAs AI coding tools accelerate delivery, development teams should closely track deployment speed alongside rollbacks and production issues. Higher productivity is valuable, but it must not come at the expense of quality or create more technical debt. - Daniela Binatti, PismoLead TimeThe KPI to watch is lead time, meaning how quickly an idea turns into something running in production. AI can help teams generate much more code, but if deployment and release engineering cannot keep up, that becomes the bottleneck. At that point, productivity gains in coding do not translate into real business value because the work still cannot reach production fast enough. - Yuri Gubin, DataArtTime To ConfidenceAs AI coding tools increase development speed, I would pay closer attention to time to confidence. The bottleneck is no longer generating code. The bottleneck is knowing whether the generated code is actually correct, secure, maintainable and aligned with business intent. The goal is to reach justified confidence as fast as possible. - Dmytro Filatov, Aimech Technologies Corp. (DeepXhub.com)Cycle Time And ThroughputWhen AI coding tools enter the picture, it’s easy for teams to get hyper-focused on raw code-generation speed or the sheer volume of code being pumped out. But the critical KPI dev teams need to watch is cycle time and throughput, specifically measured through a lens of alignment and supervised validation. Teams must pay close attention to alignment, quality parameters and architectural standards rather than just execution speed. - Fabio Caversan, Stefanini GroupAutomated Test Coverage RateAutomating test coverage rate would provide peace of mind. Achieving 100% regression and acceptance tests is challenging, but if you succeed, you can be more confident that the speed of delivery is positively impacting real productivity. - Hovhannes Avoyan, PicsartVulnerability Introduction RateAs AI accelerates code output, the one KPI dev teams must obsess over is vulnerability introduction rate per deployment. Faster delivery means flaws are embedded at machine speed. AI-generated code is expanding attack surfaces faster than teams can patch. Speed without security visibility isn’t progress; it’s technical debt with a threat actor waiting at the other end. - Kumar Ritesh, CYFIRMACode Deletion RateStop obsessing over velocity and prioritize code deletion rate. As AI tools flood repos with boilerplate, the real bottleneck is maintenance. Teams must ruthlessly delete unnecessary AI-generated bloat to keep the codebase cognitively manageable and reduce long-term technical debt. - Ivan Tankoyeu, AI Superior GmbHCode IntegrityAI coding tools are winning the speed race yet quietly losing the quality one. The KPI that matters now isn’t delivery velocity but code integrity—how much of what AI generates survives without creating downstream debt. Speed without governance is just faster entropy. - Sunil Dolwani, ZS Associates India Private LimitedComprehension LatencyTrack comprehension latency: how long it takes a nonauthor to understand and safely modify code. AI makes producing code cheaper, but it can also flood teams with logic no one fully owns. The teams that win won’t just ship faster; they’ll keep their codebase explainable at speed. - Aajinkya Singh, DAEROHuman-In-The-Loop Review RateHuman-in-the-loop is a KPI to pay attention to. While AI coding can provide 80% of the solution, the critical 20% must be reviewed by a human expert; note the term “expert.” AI is a tool, not an answer. It should be leveraged, not relied upon strictly to deliver all results. There is drift, “dirty” knowledge graphs and more. Many issues will arise, and an HITL is a critical and necessary step in the AI coding process. - Rob Buller, Cyberhill Partners, LLCDependency Trust RateThe KPI I’d watch most closely is dependency trust rate: the percentage of open-source and AI-suggested dependencies that meet your organization’s security and quality standards before they enter a build. AI is accelerating code creation, but it’s also accelerating dependency selection. The teams that move fastest will be the ones making the fewest risky decisions at machine speed. - Brian Fox, Sonatype, Inc.AI Token UsageA KPI to track is tokenomics. Why? AI pricing is shifting to usage-based models. Every input and output token costs money. Lazy prompts or needless iterations can multiply expenses fivefold to tenfold. Tracking token benchmarks (for example, production output) enforces a FinOps mindset, ensuring developers write concise queries, iterate smartly, are trained adequately and rightsize AI model selection for the task. - Rex Lee, Canadian Tire Corporation