A Larridin resource
The Enterprise AI Measurement Guide
A practical reference for how enterprises define, calculate, and benchmark the metrics that prove AI is actually working: adoption, fluency, governance, spend, and engineering output.
This guide exists because most “AI adoption” reporting stops at login counts. It’s built for AI leads, PMO, HR/People analytics, and engineering leadership who need a shared, defensible definition for every number that shows up in a board deck or a QBR.
Start with a section below. Each one names the metrics in that area, how they are calculated, the healthy benchmark range, and the most common ways the metric gets misread. Sections still being written are marked.
Guide sections
-
AI Adoption
Who's using AI, how often, and how deep the usage goes. The foundation every other measurement builds on.
-
AI Fluency
How skilled your workforce and agents are at getting real, durable work done with AI, beyond raw usage.
-
AI Tools & Shadow Usage
Every tool in active use, sanctioned or not, broken out by browser, desktop, and unified activity.
-
AI Governance
Policy enforcement, unapproved-tool exposure, and where compliance risk actually concentrates.
-
AI Spend Intelligence
What you're actually paying for AI, by platform, source, and team.
-
AI Productivity
One defensible score for whether AI is making people more productive, grounded in survey and usage data.
-
Engineering Measurement
The deepest section: velocity, quality, reliability, CI/CD, and agent effectiveness for engineering orgs.
-
Workflow Intelligence
Where friction still lives in day-to-day work, and how much time automation could realistically recover.