The Problem
Larridin, an AI measurement platform backed by A16z and GV, tracked enterprise AI adoption across sales teams. The numbers are ugly. Roughly 60% of employees with enterprise AI licenses still use personal accounts instead. You negotiated the ChatGPT Teams deal, ran training sessions, and most of your team logged into their free account anyway. Zero data capture. Zero learning. Zero visibility into what drives pipeline.
What Sales Leaders Are Missing
For every sanctioned tool, employees found five to six more on their own. One Larridin customer had six different AI notetakers with just 10 people. CEO Russ Fradin joined a customer call early and counted four competing bots before any humans showed up.
Most companies treat this as a compliance problem. Wrong frame. Your best reps are running experiments for free. The question is not how to lock it down but how to capture what they are learning.
The Proficiency Gap
When Larridin measured proficiency, not just usage, they found a lot of AI activity was basically search queries. Sports scores. Random lookups. Without distinguishing between a rep using AI to build custom pitch decks and a rep asking ChatGPT what time dinner is, you have no idea if your AI spend is generating pipeline or just burning tokens.
Where the ROI Actually Lives
The sharpest insight: AI will not make your best reps much better. It will make your worst reps a lot less bad. That is worth more.
Every sales leader knows the feeling of going back through leads and realizing reps never followed up. Not your A players. Your average and below-average ones. AI will not turn a C-minus rep into an A player. But it will turn them into a B. Across a team of 500 or 1,000, compressing that distribution and raising the floor on follow-up quality is one of the biggest revenue levers most leaders are not thinking about. The bell curve shifts right. That is where the money is.
What This Means
Larridin's data confirms what a lot of VPs suspect: most AI investments in sales are not paying off because there is no measurement layer. You cannot optimize what you cannot see. The companies that figure out instrumentation first will have a 12-month advantage on everyone else.
Larridin raised $17M seed led by A16z to build this measurement layer. Their platform, Scout, tracks which AI tools employees actually use, how effectively, and what drives pipeline. The product deploys via browser extension and desktop agent.
Worth noting: only 28% of sales and revenue leaders report that AI actually improves revenue-driving performance, despite widespread adoption. That gap is the market Larridin is building for.