Everyone has the AI. One person gets the result.

One person knows how to get a useful client update from AI. They supply the right context, correct the weak parts and remember what the client asked last week. A colleague uses the same tool and gets a polished draft that still needs substantial work.

The difference is more than how they phrase the request. One person is bringing knowledge the other hasn't supplied: what matters, what can be left out, which numbers need checking and what a good update looks like. The business benefits when that knowledge becomes available to both.

The expertise may already be written down.

A saved prompt can contain a great deal of it. So can an SOP, a house template or a completed example with the right corrections. Other parts live in people's heads: the exception they recognize, the question they know to ask, the detail they refuse to guess.

Start by finding those parts. The person who gets a useful result may be skilled at AI, skilled at the job, or both. You want to capture the way the work is done, including how they judge the answer.

A shared prompt is a useful starting point.

Training and shared instructions help people get more from AI. For a simple task, they may be enough. As the job grows to include several inputs, exceptions and a particular output format, it helps to package the whole workflow so each user has the same starting point.

That means specifying what to provide, which steps to follow, which source takes precedence and what to do when information is missing. It also means giving the team a clear way to judge what comes back. AI output can vary between runs; an agreed standard makes those differences easier to evaluate.

Put the knowledge that makes the result good into the work everyone runs.

Make the standard specific.

Illustrative example: a weekly client update.

An operations lead compares two drafts of the same update. One says the project is progressing well. The other identifies the completed milestone, the decision waiting on the client and the effect of a late approval on the next delivery date.

The second draft is useful because it tells the client what to do. Capture that requirement: every update shows progress against the plan, names outstanding decisions and explains the next step. Add a rule to distinguish a confirmed date from an estimate. Now the team has something more precise than “make it sound like our best person.”

At build time, the useful inputs are past updates, the team's reporting rules and the lead's explanation of a good result. Each week, the workflow takes that week's project export and notes to prepare the new draft.

Test whether the team can use it.

At Fully Powered AI, we capture that standard with your people and use Fully Powered Studio to build and test the workflow. Your team then runs real work in your own ChatGPT, Claude, Copilot or Gemini account, with your expert judging the results.

Look beyond whether a draft reads well. Does it include the required information? Does it flag the missing decision? Can a colleague run it without asking the original author to reconstruct the prompt? How much review and correction does it need?

Those questions turn individual success into a practical team capability. Your best person's expertise becomes a shared asset, with an owner who can keep the instructions current as the work changes.