Someone on your team is probably already using a public AI tool at work. These tools are free and they open in a browser tab. They save real time on a first draft. People use them because they work. Nobody is being reckless. They are trying to get their job done.
That is the starting point. The behavior is rational, and it was happening before you had a policy and before you had an opinion about it.
Why a Ban Does Not Solve the Problem
A ban removes the approved path and leaves the motive.
When there is no sanctioned option, the use moves to personal accounts on personal devices. The company loses whatever visibility it had. The employee does the same thing as before, somewhere the company cannot see.
That is the actual risk: use that happens outside your view.
What Invisible Use Looks Like in Practice
When a staff member pastes a client summary or a contract clause into a consumer AI tool, that information moves through a service running under terms nobody at your company has read. You do not know what those terms permit the service to do with that input. You have no record that the exchange happened.
If the AI output goes into a client email or a bid document without anyone checking it, you also do not know whether the output was accurate. Consumer AI tools produce confident-sounding text. They also produce errors. A human review step is the control that catches the difference.
Neither of those problems is dramatic. They are quiet. That is what makes them worth taking seriously.
Governing It Is More Practical Than Banning It
Governing shadow AI means giving people a sanctioned way to do what they are already doing.
The mechanics are straightforward. Pick one approved tool on a business account with business-grade data terms, so you know what the service can and cannot do with your information. Write a short policy in plain language: what categories of information may go in, and what may not. For example, client names and anything covered by a confidentiality obligation stay out, while general drafting and internal summaries can go in.
For guidance on writing that policy in language your staff will actually use, see An AI Policy Your Staff Will Actually Read.
Train people with examples from their own work. A concrete example from a real task is what sticks. And require a human to review anything before it leaves the building, whether that is a bid or a client email.
The Ladder Matters Here
The guardrails come first. The approved tool and the rules around it are protection measures. They are what make the productivity gain repeatable.
Lambert Technology Solutions sets up business-grade AI tooling, configures the guardrails, and trains your team on appropriate use. That is the sequence: protection in place, then the productivity gain is one you can keep and build on.
If you want to talk through what that looks like for your business, we are here.