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AILast updated 4 min readBy The PixelCrayons team

“Just do what ChatGPT recommended”: how we review an AI-written marketing plan

The short answer

When a client brings an AI-written marketing plan, we check each line for fit with their own data, whether the platform still works that way, and whether they can actually ship it. This post sets out that keep, change, drop review as a worksheet, with an illustrative plan worked through.

An AI-generated plan splitting into what to keep, change and drop

More clients now arrive with a plan an AI assistant wrote and ask us to carry it out. We do not refuse and we do not rubber-stamp. This post sets out the keep, change, drop review we run on each line of such a plan, with a worksheet you can use yourself and an illustrative plan worked through.

Why the plans read so well

A prospect used to arrive with a problem and a vague sense of the fix. Increasingly they arrive with a specific, confident plan: a channel mix, a content calendar, sometimes exact tactics. That is a real improvement, because the first conversation can start from specifics rather than education.

It also brings a new failure. The plan is fluent, and fluency reads as authority. Nothing in the tone of the answer tells you which lines fit your business and which would fit any business in the category.

Three reasons a fluent plan can be wrong for you

It answers the general question. “How should an online store approach SEO?” has a good average answer. Your store’s biggest problem may not be on that list at all, because the assistant could not see your rankings, your site or your competitors.

It can be out of date. Search and ad platforms change faster than training data, so a line can describe how something worked a year or two ago. The answer gives no signal that it is stale.

It does not know your constraints: budget, existing contracts, the platform you are on, brand rules, who on your side can approve and ship work. None of that was in the prompt, so none of it shaped the plan.

The keep, change, drop review

We take the plan line by line and put each line through three checks. You can run the same review with access to your analytics and Search Console.

  1. Fit. Does your own data show this is a problem on your site? A speed recommendation needs your field data to show slow pages; a content recommendation needs a gap in the searches you want to win.
  2. Currency. Does the platform still work this way? Check the line against the platform’s own current documentation, not a summary of it.
  3. Constraints. Can you do it with your budget, stack, contracts and approvers? A sound tactic you cannot ship is not a plan.
  4. Verdict. Passes all three: keep. Right idea, wrong form or order: change. Fails fit or currency: drop.
  5. The missing line. Finally, ask what your data shows that the plan never mentions. That line is often worth more than everything on the list.
Keep, change or drop: one verdict per line
Fits your business nowGeneric or out of date
Keep
When
Passes fit, currency and constraints
What it usually means
The assistant got it right, and we say so
Change
When
Right idea, wrong form or order
What it usually means
A generic idea applied without your constraints
Drop
When
Fails fit or currency
What it usually means
It answers the general question, or it is stale

An illustrative plan, reviewed

Illustrative: a plan of the kind an assistant writes for a mid-sized online store, with the verdicts our review would typically reach. The verdicts depend on the site’s own data, which is the point.

Line in the plan What the review checks Typical verdict
Improve page speed Field data for the templates that earn revenue, not a lab score for the home page Keep if those templates fail; otherwise lower its priority
Add FAQ schema to every page for rich results Google limited FAQ rich results to well-known government and health sites in 2023 Drop as a rich-results tactic; keep FAQs only where they help readers
Target the highest-volume keyword in the category Who ranks for it now, and whether its searchers buy Change: target the searches one step closer to a purchase
Publish several blog posts a week Who writes and approves them, and whether the topics map to what buyers ask Change: fewer, deeper pieces against a topic map
Build backlinks through guest posting Whether the links would be earned or bought, and the risk under search spam policies Change: earn coverage with something worth citing
(Not in the plan) Two pages competing for the same search; a category the store sells but has no page for Add, often first

Where the change is large, the plan needs rebuilding rather than editing. For content, that is our topic map and strategy sprint, which takes 2 to 3 weeks on our content marketing rate card. Where most lines survive, we recommend keeping the plan and building the work around it.

When the AI plan is right, and when you do not need us

Often most of the plan is reasonable. The failure is rarely a bad idea; it is a generic idea applied without your constraints. Where a line passes all three checks, we keep it and tell you plainly that the assistant got it right.

And sometimes the review is all you need. If the plan passes and you have people to carry it out, run it yourself. A focused SEO short focused review can confirm the riskiest lines without a programme. If the plan’s first recommendation is to “build an agent”, what that word actually means decides the brief.

If your worry is the reverse, that an assistant recommends a competitor when buyers ask about you, the AI visibility audit describes how to capture that evidence before changing anything.

Parvessh Agarwal

Written by

Parvessh Agarwal

Founder and Chief Executive, Vinove

Parvessh founded Vinove in 2004 with one idea: build focused technology companies that earn their place in real markets. His focus is on the long game, on companies that survive and serve real customers long after launch.

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Scored on public evidence by the PixelCrayons Index. PixelCrayons is scored like every firm: ranked where it has a case study, listed where it has not.

The services behind this piece

AI that earns its keep in a business process: agents, chatbots, knowledge bases and automation.

Questions

Frequently
asked.

More often reasonable in direction than wrong outright. The usual problem is a generic plan applied without your data, your platform’s current rules or your constraints. The review exists to sort the lines that fit from the ones that only sound as if they do.

By running the platforms directly and reading their own documentation and change notes, rather than summaries. Hands-on work surfaces changes faster than most published content does, AI-generated or not.

That is the client’s call. We say clearly what we would do differently and why, in writing, before starting. The job is an honest second opinion, not a veto, and sometimes the original line turns out fine.

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