Enterprise AI Measurement Guide
Agent Effectiveness
App Adoption
How do we compare the cost-efficiency of every AI coding agent we're running, across adoption, sessions, spend, and outcomes, in a single view?
What it shows
The App Adoption table shows each AI coding app's adoption rate, weekly session volume, spend, and, critically, Cost per Outcome. It is the side-by-side comparison that makes tool effectiveness decisions data-driven: two apps at identical adoption rates may have a 3x gap in Cost per Outcome, making the adoption rate an unreliable basis for tool selection.
Why it matters
Tool proliferation in AI coding is driving a consolidation reckoning: organizations that deployed multiple coding agents during the experimentation phase are now being asked to justify each one. The App Adoption table is the evidence layer for that conversation. An app with 91% adoption and $29.62 Cost per Outcome versus an app with 91% adoption and $10.97 Cost per Outcome is a straightforward consolidation candidate, but only if both numbers exist in the same view. Without Cost per Outcome, the decision gets made on adoption rate alone, which cannot distinguish value from volume.
The Larridin angle
Larridin's App Adoption table is the only per-app view that combines adoption, sessions, spend, and outcomes in a single table, making the tool-by-tool ROI comparison that vendor dashboards structurally cannot provide, since each vendor shows only their own data.