How does Olakai measure AI coding tool productivity and ROI?
Olakai measures outcome, not activity. It computes cycle time delta for AI-assisted versus non-AI pull requests, broken out by every provider you run and split into coding time, review time and total cycle, backed by your real GitHub data. Alongside that it segments every developer into Power, Casual, New or Idle cohorts and tracks token spend by provider, team and developer. Two answers come from one platform: whether AI makes your engineers faster, and whether the cost is under control.
Every coding tool sells you adoption. None of them sell you outcome.
Cursor shows you Cursor adoption. Anthropic shows you Claude Code spend. GitHub shows you Copilot acceptance rates. None of them connect that activity to shipping velocity, or warn you when a team's token burn is trending 3x over the monthly budget. Olakai connects them all: cost and velocity, end to end, from every PR. You need two answers, whether AI makes your engineers faster and whether the cost is under control, and most tools give you only one.
Source: Olakai for VPs of Engineering
Cycle time, cohorts and budget control
Cycle time impact is reported by tool: the cycle time delta for AI-assisted versus non-AI PRs, broken out by every provider you run, covering coding time, review time and total cycle. It is the exact metric procurement asks for, backed by your real GitHub data.
Adoption coaching segments every developer in your org into Power, Casual, New or Idle cohorts, with the data you need to coach the casual users, reclaim the idle licenses, and standardize on what your power users have already chosen. Budget control tracks token spend by provider and team before the overrun.
Source: Olakai for VPs of Engineering
Why the question is being forced now
Engineering organizations are entering a moment where AI coding tool budgets are large enough to require accountability. The days of "it feels productive" as sufficient justification are ending. CFOs are starting to ask for the data, and boards are asking whether AI investments across the organization are generating returns. The organizations that will be able to answer are the ones that started measuring before the question was forced on them, because value without measurement is invisible.
Source: AI Coding Tool ROI
Also asked as
- How does Olakai track developer productivity and ROI from AI coding assistants like Cursor and Copilot?
- What metrics does Olakai use to prove the ROI of AI coding tools?