GenAI is already reshaping how your team works and it’s easy to miss
You didn’t roll out a GenAI initiative. But you’re managing one anyway.
GitHub Copilot is quietly suggesting code. ChatGPT is drafting tests, refining requirements, and summarizing standups. GenAI didn’t ask for permission, it just showed up in your SDLC.
Now PRs are opening faster. Code is moving. Cycle time looks good.
But something feels off.
- Are reviews keeping up or getting rubber-stamped?
- Is rework quietly increasing?
- Are delivery patterns truly improving or just shifting?
The tools have changed. But your metrics likely haven’t. And that’s the risk.
Legacy metrics weren’t built for this
Most engineering leaders are still tracking:
- Commit counts, say nothing about complexity or quality
- Story points, which normalize velocity but obscure rework
- Cycle time (overall), which hides where delays or shortcuts occur
- Throughput, which can spike from shallow tickets or noisy PRs
These metrics were designed for linear pre-AI workflows, manual effort, and human-paced delivery. In an AI-assisted environment, they can create the illusion of speed, while masking real delivery risks.
You don’t need more data. You need the right signals.
Four signals to track in GenAI-powered delivery
Here’s what forward-looking engineering leaders are paying attention to and what each one can tell you about how GenAI is changing your process:
1. PR churn
How often are PRs reopened, revised, or quickly followed by hotfixes?
What it reveals:
Fast merges may feel efficient, but if GenAI-generated code isn’t well understood or reviewed, issues crop up downstream. Churn shows you where speed is costing stability.
2. Review depth
Are reviews getting more thorough — or just faster?
What to watch:
A spike in "LGTM" approvals and shortened review time might mean AI-assisted code is sliding through without critical eyes. Pair this with a drop in comments or skipped senior reviews, and you’ve got a risk zone.
3. Cycle time by stage
Not just how fast, but where the speed happens.
Why it matters:
If time-to-PR is shrinking but review or merge steps are stalling (or disappearing), you might be accelerating the wrong part of the process. Stage-specific cycle time shows where GenAI is helping — or hiding gaps.
4. WIP across teams
Are developers starting more than they can finish?
The GenAI twist:
AI makes it easier to start multiple threads of work. But it doesn’t resolve bottlenecks, clarify ownership, or manage context switching. A spike in work-in-progress can signal capacity stretch or context-switching overhead.
Engineering leadership is evolving your visibility needs to evolve too
You don’t need a new dashboard for every new tool. But you do need a way to see how work is changing and where risk is creeping in.
Because GenAI is doing more than speeding up delivery. It’s reshaping how code moves, how teams review, and how risk shows up.
You can’t manage what you can’t see. And velocity without clarity is a gamble.
See what GenAI is really doing to your delivery process
Flow helps engineering leaders track the signals that matter, from PR churn to WIP sprawl, so you can lead with clarity, not just data.
Try Flow for free and see what’s moving faster, what’s slipping through, and how to lead with clarity.
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