Stop Letting Your Best People Fix the AI
Every unrecorded correction makes your next retainer deliverable depend on the same expensive person.
You fix the draft. Change the claim, strip out the weird enthusiasm, put back the wording the client actually approved. It goes out on time, and everyone calls that an AI productivity win.
I keep coming back to what happens tomorrow. Another draft, same mistake, same senior person dragged back in.
This week I was listening to Allie K. Miller, AI business leader and former AWS executive, explain why treating AI as another software purchase misses the point. Faster output is the easy bit. Changing how the work gets done is the uncomfortable bit.
She was talking about new business lines, restructuring, reinventing processes. Then this:
None of that really happens if you're just rolling out a productivity tool.
That's the line I'd put next to your agency's AI subscription bill.
Because you can buy faster drafting and still run exactly the same business underneath. Same approval chain. Same account director carrying six clients' preferences in her head. Same founder checking the sensitive stuff before it leaves the building.
Except now there's more output arriving at those people.
You've accelerated production without moving the knowledge that makes production useful. Your best people become the cleanup crew for a machine that never gets handed their corrections.
I'm not saying stop reviewing client work. Please don't. I'm saying stop confusing a corrected deliverable with an improved process.
Those are different things.
If your strategist removes an unsupported claim, you've saved that draft. If she records the rule, gives it an approved example, and puts both where the next workflow can use them, you've started fixing the system.
That distinction matters on a retainer. The client pays you again next month. You shouldn't have to buy the same lesson again with your team's time.
And this is the slightly uncomfortable part: the person everyone calls indispensable may also be the person whose judgement you've never bothered to make transferable. That's not their failure. That's a business you've designed around access to their head.
So here's what I'd do this week. Take five minutes with the last AI draft you personally corrected. Pick one change and finish this sentence: “We changed this because, for this client, we always…”
Then ask where that rule lives before the next draft gets written.
Not where someone could find it after asking three people. Where the workflow actually receives it. You'll learn whether you're building reusable judgement or just funding repeat repairs.
In the [video](https://www.youtube.com/watch?v=JsQgvPeTMK8), I get into Allie's chief-of-staff agent setup and the question it raises: what has to change before you can delegate work without becoming the approval desk for all of it? That's the useful bit, not the agent count.
Talk next week, Michael
This is why I'm building Vythos: to give your AI tools the client rules, brand voices and ways of working that otherwise stay in people's heads. If that's the gap you're trying to close, take a look at
Learn more about Vythos →Subscriber Exclusive: Correction Card
Only in the newsletter — not in the video.
Use this correction card on one recurring client deliverable. Ten minutes, one real example, no grand documentation project.
- Deliverable: What were we producing?
- Original output: Paste the sentence or decision you rejected.
- Approved version: Paste what actually went to the client.
- Reason: What made the original wrong? Name the client-specific constraint, not just “bad tone.”
- Reusable rule: “For this client, always ___ / never ___, unless ___.”
- Source: Link the approved brief, client feedback or policy supporting that rule. If it's your judgement, say so.
- Placement: Where will this rule enter the next run—brief, client context document or workflow instructions?
- Check: Generate one fresh example with the rule supplied. Does it avoid the same mistake without introducing another?
Keep human approval where it belongs. The test is whether you need to make this particular correction again, not whether the output sounds confident.
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