Written for the COO.
The choice isn’t between banning AI and risking a breach. For a COO, there’s a third path — controlled experimentation — that lets your operation capture the value without exposing the business.
Most operations approach AI risk as a binary. Either you lock it down to protect the business, or you let people loose to capture the upside. Framed that way, both options are bad: the lockdown drives usage underground where you can’t govern it, and the free-for-all sends confidential data into tools you don’t control. The good news for a COO is that the binary is false. There’s a third path, and it’s the one mature operations take.
That path is controlled experimentation — deliberately enabling your people to use AI on real work, inside boundaries designed to keep the business safe. It captures the value because it manages the risk, not despite it.
Why the two obvious options both fail
The ban fails because it changes visibility, not behaviour. People who found AI useful don’t stop; they move to personal devices and accounts, and the same risk now runs with none of your oversight. You’ve lost the ability to see or govern what’s happening.
The free-for-all fails for the opposite reason. Without rules, well-meaning staff paste sensitive material — client data, financials, contracts — into whatever public tool is to hand, usually without grasping the implications. No malice, just the absence of a framework. And the exposure stays invisible until something goes wrong.
Controlled experimentation threads the needle: visible, governed use that still leaves room to explore and benefit.
The framework, in operational terms
It’s more straightforward than the risk makes it sound, and it’s the kind of process discipline a COO already runs:
An approved tool stack. A small, named set of tools the operation has chosen and configured — including settings that keep inputs from training the model where that option exists.
Clear acceptable-use rules. A short, readable statement: what AI may be used for, what data must never go in, and where human judgement stays in charge. Plain English, not legal boilerplate.
Human review where it counts. A simple principle that consequential outputs get checked by a person before they’re actioned. AI drafts; a human owns.
An explicit “when not to use AI” list. Mature governance is as clear about the no-go zones as the green lights. Naming them is a sign of seriousness, not timidity.
A usage review and an owner. A periodic, honest look at how AI is being used, owned by a named person who keeps it current. Unowned frameworks decay.
A caveat worth stating plainly: no framework guarantees zero risk, and this isn’t legal advice. The point is that a modest tool inside a sound framework is far safer and more useful than a brilliant tool with no guardrails.
The leadership question
For a COO: are we making it easy for our people to use AI safely — or leaving them to choose between not using it and using it dangerously? If you’ve given them no safe option, they’ll improvise an unsafe one.
A short safe-experiment checklist
Before experimentation runs, can you answer yes to these?
Have we chosen and configured a small set of approved tools?
Have we told people, in writing, what data may and may not go in?
Is there a clear rule that important outputs get a human check?
Have we named where AI must not be used at all?
Is there someone who owns this and reviews how it’s actually used?
Mostly “no” simply means you have a ban or a free-for-all, not a framework — and a short, focused effort fixes that.
What to do next
Set the boundaries first, then invite experimentation inside them. The approved stack and the one-page rules alone convert a risky free-for-all into managed exploration; then name an owner. You get the upside your people want without the exposure that keeps you up at night.
In closing
“Supercharge” shouldn’t mean letting AI loose and hoping. For an operation it should mean growth with guardrails — real value, captured safely, by design.
If you’d like help building a safe-experiment framework — approved tools, clear rules, the right ownership — that’s exactly what Savant and Axulu set up, from a short engagement through to a fractional operations-technology leader if that’s what your operation needs.