A Policy Is Only as Good as It Is Understood

A policy that cannot be understood is functionally not a policy. It is a document. Most AI policies are documents: written carefully, filed on a shared drive, linked in an all-hands deck, and then left to sit while people quietly decide on their own what is and is not allowed. The policy exists. It just does not do anything.

If deployment is not adoption is the principle, this is the mechanism: the unglamorous, expensive work of turning a written rule into a thing people actually understand well enough to use. At Avidity, where I was the VP of AI, that turned out to be most of the job.

The part everyone expects to be hard

Between Legal, IT, and my team, we wrote an AI policy we believed in: the data tiers and where each was allowed to go, our position that there is no AI work product, only AI-assisted human work product, and practical guidance on everyday questions like AI for meeting notes. Writing it is the part everyone expects to be hard. It was not the hard part.

The part that actually was

A policy is worthless until the people it governs understand it well enough to act. So I taught it. Personally, to more than half the company, hundreds of people, in fifteen-minute increments. Not a recorded module. Not a memo from Legal. Me, in the room, walking a group through what the data tiers meant for their actual Tuesday, what “no AI work product” meant for the report they were about to write, how to think about turning on transcription in their next meeting.

Fifteen minutes, over and over, is a large amount of a VP’s calendar. That was the point. Spending it said, at a level no email can, that this mattered, and that the person accountable for it would sit with you and answer your question to your face.

The teaching goes both ways

The questions were the second reason to be in the room. Every session surfaced things the policy had not anticipated. I collected them, turned them into FAQs, and carried them back to update our leadership team and, where needed, the policy itself. The policy got better because I was explaining it, not in spite of it. A rule you have to say out loud to a hundred skeptical people is a rule whose weak spots you find fast.

Responsible conservatism, and then blossoming

At first, almost everyone did the same thing. I call it responsible conservatism. Handed a new capability and a new policy, good people default to the most risk-averse reading. They do not use the tool, to be safe. That looks like caution, and it is, but an organization frozen in caution is not safe. It is just slow, and quietly falling behind while it feels responsible.

Teaching the nuance is what broke the freeze. Once people understood not only the rules but the reasoning, where the real risk lived and where it did not, they started to blossom. The person who would not touch a transcript in week one was, a month later, rethinking how their team ran meetings. The confidence came from understanding, and the understanding came from someone taking the time to hand it to them directly. Then it became self-sustaining: we built AI-policy acceptance into standard onboarding, and it held, because a new person with a question could ask the colleague at the next desk, who now knew the answer. That is the quiet signal that adoption is real. The organization can teach itself.

What does not transfer

Take the principle, not the format. Fifteen minutes was what fit us; your dose may differ. What transfers is that the policy has to be taught, in person, by someone senior enough that the time itself carries the message, and refined by the questions it provokes rather than defended against them. Be honest about the cost. This is expensive, a large amount of a senior leader’s calendar, and it only worked because leadership backed the time as worth spending. If no one senior will spend the hours, you do not have an enablement plan. You have a PDF. And treat responsible conservatism as what it is: not apathy, not resistance to be overcome, but good people trying to do the right thing without enough information. The answer is never pressure. It is information, delivered by someone they trust. The full record, at Avidity, is here.

Monday morning

Do not ship your AI policy to the shared drive and call it launched. Pick the most senior person who can credibly teach it, and have them teach it, in small live doses, to as much of the company as it takes. Collect every question. Let the questions improve the policy. Then wire it into the place new people learn everything else, so the organization keeps teaching it after you stop.

Cheers,
-Titus