Digital Lexicon Hub

The share of people with access to AI tools who use them systematically in real workflows, rather than occasionally or in experiments.

Paid AI seats only create value when people use them in recurring work, so adoption rate explains much of the gap between AI spend and AI returns and shows whether training and enablement are working and where licenses are wasted.

A product organization pays for 36 AI seats but finds that only 12 people use AI every time they synthesize research or review code, an effective adoption rate of 33%, and sets a target of 75% after a coaching program.

Login or activity counts overstate adoption because opening a tool is not the same as changing how work is done. Defining systematic use takes judgment per workflow, and measuring it person by person can raise privacy and trust concerns.

AI adoption rate = people using AI systematically ÷ people with access × 100, where access usually means paid seats. Systematic use is defined per workflow, such as AI used in every research synthesis or code review, and checked with admin-console activity, workflow sampling and short surveys. Time-saving measures complement the rate: the St. Louis Fed found that workers who had used generative AI in the previous week saved 5.4% of their work hours on average. In Return on Investment (ROI) models, effective adoption scales both the value recovered and the share of AI spend counted as waste.