use cases · AI adoption / transformation
You bought AI for everyone — and the real usage is quietly low
You bought AI for everyone and mandated it — and real usage is quietly low. It's not training; it's threat.
For who
What it finds
What you get
Binding constraint
The situation
Leadership rolled out AI copilots/assistants org-wide, ran the trainings, and in some cases mandated use. License dashboards look fine, but the productivity gains never showed up — and candid conversation reveals people aren't really using it, or are using it in the shadows and not saying so. The plan: more mandates, more training, maybe a new tool.
How the walkthrough goes
- 01customer-situation
You bought AI for everyone, mandated it — and the dashboards say it's adopted.
Licenses are active, the trainings ran. But the productivity lift never showed, and candidly, people aren't really using it — or they're using it quietly and not saying so.
- 02problem-cost
So you're about to spend again — more seats, more training, maybe a new tool.
Every one of those pushes harder on the thing that isn't the problem. The seats you've bought keep not converting.
- 03insight
Low adoption isn't a training gap. It's a threat.
People read AI as a threat to their standing or their job, and as something they'll be measured against — so they comply minimally or hide real use. Mandates and trainings add pressure, which makes it worse, not better.
- 04desired-outcome
Turn paid seats into real, compounding usage.
Make it safe to be a visible beginner and reframe AI as augmentation — and usage starts to build on itself.
- 05product-path
Performix finds what's actually blocking adoption.
Protected feedback + CAMS shows the block is Motivation — threat and safety, not capability or tooling.
- 06proof
License activity doesn't predict usage. Safety does.
In the data, seats-active doesn't separate real users from non-users; the threat/safety items do.
- 07risk-reversal
Honest by construction.
Protected feedback (anonymity primitive) + minimum-group-size gate; people can admit they're not using it — or are afraid of it — without exposure.
- 08next-step
Diagnose why before you buy the next tool.
One read on what's actually blocking adoption — before you spend again on seats, training, or a new platform.
Grounded in the research
- — Deci & Ryan (Self-Determination Theory) — autonomous vs. controlled motivation; mandates/surveillance crowd out the intrinsic motivation adoption needs
- — Edmondson — psychological safety; people won't experiment in public (or admit fumbling a new tool) where mistakes feel unsafe
- — Davis (Technology Acceptance Model) — perceived usefulness + ease of use drive use; perceived threat suppresses it
- — The 'shadow AI' pattern — sanctioned-on-paper, hidden-in-practice usage when adoption is mandated rather than made safe
Walkthrough data is composite and clearly labeled — shaped from the research to show the real shape of the finding, not a named client.
Convert paid seats into real, compounding usage by removing the threat and making beginnerhood safe — the decision-error avoided is spending again on more tools/mandates/training that push on the thing that's actually blocking adoption.