Written by: Wiktor Stefański, Head of People & Operations, Digital Colliers
Your team just added AI coding assistants. Velocity doubled in three weeks. Sprint points are up. But the code review queue looks roughly the same size it did before. That should worry you.
If output doubles and review load does not, someone stopped reviewing. The code still ships. It just ships without the safety net you had before AI arrived.
The math that does not add up
When a team adopts AI assistants, common sense says review load should climb. More code means more surface area to check. But the pattern we keep seeing is the opposite.
Research tracking experienced developers after they adopted AI coding tools found they reviewed 6.5% more code than before. Their own original code output dropped 19%. The AI filled the gap. But 6.5% more review work is nowhere near enough to cover a doubling of total output. The remainder went through with lighter scrutiny or none at all.
That gap is where the problems hide. More than 15% of commits from every major AI coding assistant introduce at least one issue. GitHub Copilot sits at 17.4%. Gemini is worse at 29.1%. If your review process caught those before, you need more review capacity now. If it stayed flat, those issues are in production.
What happens when review capacity lags output
The effects compound fast. Unresolved technical debt from AI-generated code climbed from a few hundred surviving issues in early 2025 to over 100,000 by February 2026 in one large-scale study. That is not a rounding error. That is a maintenance crisis brewing in the backlog.
Most teams see the lag six to nine months later. Tests start flaking. Refactors take twice as long because the code structure is inconsistent. Security audits surface patterns no human would write. By then you are firefighting instead of shipping features.
The cost shows up in the aggregate numbers too. 42% of companies abandoned most of their AI initiatives in 2025, up from 17% the year before. More than 80% of AI projects fail outright. Failure rates for AI projects run roughly twice as high as conventional IT work. A large part of that gap comes from teams who shipped fast early and paid the price later when the hidden debt came due.
Why smart teams miss this signal
Review work is invisible to most velocity dashboards. Management sees more commits, shorter cycle times, and fewer blocked stories. All green. The fact that Sarah is now spending 30% of her week in review instead of 15% does not show up in the sprint report.
The other reason teams miss it is that AI assistance feels like pure upside at first. Developers like the tools. Productivity seems higher. Nobody wants to be the person slowing down the good news with process concerns. So review capacity does not get budgeted as a line item when you roll out AI tooling.
But review is not overhead. It is your quality gate. If that gate gets thinner while the volume passing through it doubles, you are not moving faster. You are just deferring the cost.
The metrics that actually tell the story
Track review hours per pull request or commit. If that number drops after you adopt AI, your quality floor dropped with it. Track issues caught in review per engineer per week. If that stays flat while output climbs, you are missing things. Track time from commit to review completion. If that grows, you have a capacity bottleneck.
None of these are complicated. You probably already log most of the data. The shift is treating review load as a first-class metric instead of something that happens in the background.
The pattern we see among teams who make AI assistance work long-term is simple. They budget review capacity when they budget AI tooling. If AI is going to double output, they hire or reallocate so review capacity can keep pace. They make the invisible work visible before the problem compounds.
The alternative is shipping fast now and cleaning up the mess later. Some teams take that bet. Most regret it.