Teams that ask for more AI access don’t always turn it into value. Expand first where demand is real, delivery is improving, quality is holding up, and the engineering system can absorb more AI-assisted work.
Giving all engineers the same access may seem fair. But it doesn’t mean every team will use the tools the same way or get the same return.
Larridin’s Developer Productivity Benchmarks 2026 separate tool coverage from actual use. Average organizations provide AI access to 70% to 80% of developers, but weekly active use averages only 30% to 40%. Access is necessary, but it doesn’t mean that another license will pay off.
Team conditions also matter. DORA’s 2025 research describes AI as an amplifier. It can strengthen a good engineering system, but it can also magnify existing problems. Faster code generation doesn’t improve delivery when work still gets stuck in review, testing, deployment, or rework.
That’s why expansion decisions should follow evidence, not team size, seniority, or enthusiasm.
Use the same five signals for every team. None is enough on its own.
Start with actual use, not requests.
Look at:
A team with strong, consistent use and a clear queue of engineers who need access is a better expansion candidate than a team with low use and unused coverage.
Larridin’s AI Adoption platform shows usage, cost, and adoption trends by team. That helps leaders see where access is constrained and where existing licenses are sitting idle.
Don’t confuse high activity with high ROI. Sustained use earns a team a closer look. The next four signals show whether the use is productive.
The team should be completing meaningful work faster, not just generating more code.
Compare the period before and after AI adoption using measures such as:
Raw code volume is a weak signal because AI can increase lines, commits, and pull requests without improving delivery.
Larridin’s benchmarks recommend reading adoption and AI code share alongside velocity that accounts for the complexity of the work, as well as quality and cost. A team is a stronger expansion candidate when delivery improves without another part of the system getting worse.
More licenses shouldn’t create more cleanup.
Track:
A team with rising AI contribution and stable or improving quality has stronger evidence that its review and testing practices are working. A team with rising turnover or failure rates needs investigation before expansion.
Larridin’s Developer Productivity platform tracks AI code share, code durability, delivery, and ROI across teams and repositories. That makes it easier to see whether AI-assisted work survives after merge.
AI can speed up code creation without increasing reviewer capacity.
Larridin’s PR cycle time guidance notes that the bottleneck can move from writing code to reviewing it. A team may open pull requests faster while those requests wait longer for review or require more revision.
Before adding licenses, check:
If review time is already increasing, more AI access may add to the bottleneck. The better move may be smaller pull requests, stronger tests, clearer code ownership, better routing, or dedicated review time.
The last question is what the team gets for the money.
Include seat licenses, token and API spend, premium model charges, review time, and rework. Then connect that cost to a useful outcome such as:
Larridin’s benchmarks put average annualized AI coding ROI at 2.5x to 3.5x and top-quartile ROI at 4x to 6x when total usage-based costs are included. The exact number will vary by team and work type, so the more important question is whether cost per useful outcome is improving.
A high-spend team may still be a strong expansion candidate if it produces proportionately more durable value. A low-spend team isn’t efficient if the work requires heavy review or rework.
Once the five signals are visible, sort teams into three groups.
Prioritize teams that show:
These teams have already shown that they can turn AI access into durable work.
Some teams have strong demand and promising results but one clear bottleneck. Review may be slowing, tests may be flaky, or rework may be rising.
Don’t treat that as a permanent rejection. Identify the constraint, fix it, and reassess. A targeted improvement may make the team a strong expansion candidate.
Hold off when existing seats are underused, the use case isn’t clear, or the team can’t show better delivery or quality.
More licenses won’t solve low adoption or a weak workflow. Reallocate unused access, clarify the use case, or run a smaller pilot before expanding.
Not automatically. High use shows demand, but it doesn’t show value. Compare usage with delivery, quality, review, and cost before expanding.
Start with the specific constraint. AI may help with documentation, testing, migration, or routine work, but a broad rollout can magnify weak review or delivery processes. A focused pilot is safer than giving the whole team more access at once.
Look at several normal delivery cycles. For adoption, Larridin’s benchmarks use above 50% weekly active use within 90 days as a useful target. Don’t decide from one sprint, one project, or a short spike in usage.
Combine AI tool usage with repository, pipeline, quality, and spend data. Usage shows whether the team is adopting the tools. Delivery and quality show what changed. Cost data shows whether the return justifies more access.
No. Teams work in different codebases, risk environments, and delivery cycles. Use the same five signals, but compare each team with its own baseline and with teams doing similar work.
The next AI coding license should go where the organization has the clearest evidence of demand, delivery value, durable code, available review capacity, and improving cost efficiency.
Larridin combines AI adoption, developer productivity, quality, and cost data so engineering leaders can build that shortlist from real team results.
Book a discovery call to identify the teams ready for more AI access.