Why the future of marketing (content in particular) requires better intelligence, stronger human judgment, and a lot less fluff.

I am hearing “AI content is tanking SEO,” “AI is making everything sound the same,” “AI is ruining brand trust…” and the list goes on. And with all of that fear comes the debate over whether brands should use AI at all.

I have a very different perspective…

First, some of that is fair. If a company uses AI to mass-produce generic “top XYZ” blogs, rewrite what already exists, chase volume, then yes, that is probably going to hurt.

But most marketing teams are being asked to move, produce, show impact faster… oh, and now get found in AI search too. That is a lot.

The current process isn’t conducive to both impact and speed (I KNOW, I spent years in content marketing):

Do you know what it takes for a single writer to have the data, strategy, buyer understanding, SEO insight, AI-answer structure, client knowledge, competitive context, and source discipline to then write a best-in-class piece?

That is what I call the “inputs” problem. But lets talk about the process: kickoff calls with the writer, SEO specialist, client strategist, and editor. Then add client interviews, writing samples, keyword data, prompt data, competitor research, positioning notes, proof points, and rounds of revisions.

Now ask ONE writer to remember all of that, interpret it correctly, align it to the business goal, write for the buyer, write for search, write for AI visibility, and still make it sound like the company.

(If you can do all this perfectly, you may be superhuman… If so, please call us; we will hire you)

I believe a great writer’s talent can be used SO MUCH MORE effectively when AI helps apply the science first.

Here is our no-shame dual approach to content:

AI applies the science, Humans apply the judgment.

What AI Should Own

1) Agnostic target audience understanding

Your writer or communications team may be deeply swayed by the product/marketing leader’s opinion of themselves and how they fit within the client’s pain points. We ask AI to first study the buyer in a fresh, context-free environment before we introduce the client’s product, positioning, or messaging.

That means it is not working from an existing GPT that already “knows” the company. It is looking at the audience more objectively: their challenges, where they invest, what they compare, what they are skeptical of, and what problems are urgent enough to move budget.

Then we map those buyer insights back to the client’s solution.…. NO “force-fitting” of solutions to pain points that budget doesn’t exist to solve!

2) Deep data analysis

We look at the opportunity before we write.

That means identifying buyer questions with strong AI citation potential, keywords we can win or take back from competitors, and topics where the market is still under-answering the buyer. Then we study what AI engines are already pulling into answers: the structure, tone, sources, examples, and level of detail that seem to earn trust.

3) Locked client blueprint

AI does not start from a blank page. It first reads the client blueprint we built with the team: strategy, messaging, differentiation, trust signals, customer language, proof points, what success looks like, and what has not worked before.

That way, the content is not just “technically good.” It stays tied to the business, the buyer, and what the client actually wants to be known for.

4) Locked voice fingerprint

AI also learns how the client actually sounds. We feed it strong writing examples by content type (ideally 10 or more) so it can understand the client’s tone, phrasing, level of detail, point of view, and writing patterns. That way, the content does not just follow the strategy. It starts to sound like it belongs to the client.

5) AEO answer-readiness rules loaded into every piece

This is where it gets VERY hard for one writer to follow everything manually.

Rules like: Does it match the core buyer questions/prompts? Does it have the answer near the top? Are definitions simple? Does each section actually answer something? Are we using specific service descriptions instead of vague benefit statements?

Are we including the comparison language buyers and AI engines look for, like alternatives, pros and cons, use cases, buyer fit, and what makes one option different from another?

Is the schema aligned with what is visible on the page?

So our AEO agent process helps check for these rules before the draft ever gets finalized, and it keeps adjusting as AI search best practices change.

6) Anti-AI edits applied

We also run the draft through our anti-AI quality rules. House banned-word list, em-dash ban, no staccato sentences, “not this, but that” sentence, lead with proof not adjectives, cite-a-real-number rule, all enforced before a draft can save.

Basically, we are checking for all the little tells that make content feel generated instead of thought through.We have a giant anti-AI rules file that helps here!

What a Human Should Own:

1) Two points of review

The human review happens in two places. First, we review the outline before the draft starts. Is it aligned to the goal, the business objective, the reader, and the takeaway? Is there a reason this piece should exist?

Then we review the AI-assisted draft and add what AI cannot fully create on its own.

2) Actual storylines

This is where the piece starts to feel “lived in.” The human adds real experiences, examples, use cases, client nuance, and numbers. Not generic points that sound fine, but details that make the piece feel like it came from someone who has actually seen the problem.

3) Frankness and judgment

The human has to know what is interesting, what is too generic, what is overclaimed, and what deserves to be said. This is also where we pressure-test claims with market data, citations, examples, or real proof. If we cannot support it, we should not make it sound bigger than it is.

4) A lens on non-commodity content

If it can easily be replicated by a competitor, scrap it!

5) A lens on the buyer

Even though this is baked into our agents, AI can still drift stray. If a competitor could easily publish the same piece, it is probably not strong enough.

That does not mean every article needs to be wildly original. But it does need a sharper angle, better proof, a clearer point of view, or a more useful way of explaining the problem.

6) A lens on human attention (which unfortunately is getting shorter)

Could someone skim this on their phone between meetings and understand the point on the first pass? Or does it wander, repeat itself, and bury the takeaway (which is very typical of AI content).

So no, I do not think AI is killing good content. Lazy AI content is, and our job is to teach teams how to use AI for better intelligence while still protecting the judgment, voice, and quality that make content worth publishing!!

Opinions on this? We are only seeing positive numbers from our approach.