Most Businesses Are Getting Answer Engine Optimization Backwards

There's a new phrase making the rounds in every marketing meeting right now, and it's Answer Engine Optimization. The pitch goes like this: search is dying, AI is taking over, and if you don't optimize for ChatGPT and Google's AI answers today, you'll be invisible tomorrow.

I want to push on that. Not because the shift isn't real. It is. But because the way most businesses are responding to it tells me they've completely misread what's happening.

They're treating AEO like SEO wearing a new outfit. Same tricks, same knobs, just pointed at a chatbot instead of a search bar. And that instinct is going to waste a lot of money.

What people think AEO is

Ask a room full of marketers what answer engine optimization means and you'll get some version of this: it's how you make ChatGPT recommend your business. It's how you show up in Google's AI overview. It's the new keyword game, and whoever cracks the code first wins.

So they go hunting for the code. They add FAQ schema to everything. They stuff their pages with question shaped headers. They start writing in that weird, hollow voice that's supposed to be "AI friendly." They ask an agency to sprinkle some structured data on the site and call it a strategy.

Then they wait for the robot to fall in love with them.

It doesn't.

They're treating AEO like SEO wearing a new outfit. Same tricks, same knobs, just pointed at a chatbot instead of a search bar.

Here's the problem. All of that activity is built on a hidden assumption: that an answer engine works like a search engine. That there's a ranking. That you can climb it with the right technical moves. And that assumption is wrong in a way that matters.

Search ranks pages. Answer engines form opinions.

This is the whole thing, so slow down here with me.

A traditional search engine gives you a list. Ten blue links. It's saying, in effect, "here are some places that might have your answer, go sort it out yourself." Your job in SEO was to be one of those ten places, and ideally the first.

An answer engine does something different. It doesn't hand you a list. It gives you a verdict. When someone asks ChatGPT "who's the best commercial roofer in Denver" or "what tool should I use for onboarding emails," the model isn't showing them a menu. It's making a recommendation. It's forming something that looks a lot like an opinion.

And you don't optimize your way into somebody's opinion. You earn it.

Think about how you'd answer if a friend asked you to recommend a good accountant. You wouldn't run a technical audit of the accountant's website. You'd pull from everything you've absorbed over time. Who's come up in conversation. Who other people trust. Who has a reputation for being sharp and honest. Who you've simply heard about, again and again, in the right context.

That's much closer to how these models behave. They've read an enormous amount of the internet, and they've built up a kind of statistical impression of who matters for what. When they answer, they're surfacing that impression.

You're not trying to rank. You're trying to be the answer a well read stranger would give.

Why the technical checklist misses the point

Now, I'm not going to tell you schema markup is worthless. Clean structure helps a machine understand what a page is about. That's real. But it's the floor, not the strategy.

A hammer is not a house. Structured data is a hammer. If you think adding it is going to build you a reputation inside these models, you've confused the tool for the outcome.

Here's the test I keep coming back to with clients. If a smart human researched your industry for a week and never once came across your name, why would a model trained on that same internet come across it either? The AI didn't invent knowledge about your market. It absorbed it from the same articles, forums, reviews, comparisons, and conversations a person would find.

So if you're absent from that source material, no amount of on page tinkering saves you. You can format a page beautifully. You can answer the question perfectly. But if nobody else in the ecosystem corroborates that you exist and that you're any good, you're a well dressed stranger the model has no reason to trust.

That matters.

If a smart human could research your whole industry and never hear your name, the machine won't hear it either.

What actually moves the needle

Let me get concrete, because I don't want this to float in theory.

When we look at businesses that do get named by these tools, they tend to share a few unglamorous traits. Not tricks. Traits.

• Other people talk about them. Not their own blog. Other sites. Reviews, roundups, industry pieces, Reddit threads, podcasts, comparison articles. The model sees a business mentioned across many independent places and reads that as significance.
• They're associated with something specific. Vague, everything for everyone businesses are hard to summarize, so they get summarized as nothing. The ones that get recommended tend to own a clear niche or point of view that's easy to attach them to.
• They've published real substance the internet actually cites. Not thin, keyword bait content. The kind of thing other people reference because it's genuinely useful.
• Their basic facts are consistent everywhere. Same name, same description, same positioning across their site, directories, profiles, and press. Contradictions make a model uncertain, and uncertainty gets you left out.

Notice what's not on that list. There's no clever prompt. No secret schema tag. No way to sneak in through the back door. This is reputation, distributed across the web, made legible.

Which is why I keep telling people the uncomfortable truth: AEO is mostly just being genuinely known and genuinely good, made machine readable.

The part nobody wants to hear

Here's where the room usually goes quiet.

If getting recommended by an answer engine is downstream of your actual reputation, then AEO is not really a marketing tactic you can bolt on. It's a reflection of whether the market thinks you're worth recommending.

You can't fake that at scale. You can nudge it. You can make sure your genuine strengths are visible and consistent and well documented. But you cannot manufacture a reputation you haven't earned and expect a model trained on reality to co sign it.

I find this oddly reassuring, actually. For years, SEO rewarded people who were good at gaming systems more than people who were good at their jobs. Whole industries got built on manipulating signals that had drifted away from real quality. Answer engines are messy and imperfect, but they push in a healthier direction. They're harder to trick precisely because they're synthesizing so many sources at once.

Which means the businesses panicking about AEO are often the wrong ones. The company with a strong reputation and quiet marketing is in far better shape than the company with loud marketing and nothing real underneath.

Okay, so what should you actually do?

Let me answer the obvious question. If it's not schema tricks, what's the work?

Start by being honest about whether you're even findable. Search your own category the way a customer would. Read what's out there. Are you mentioned? By whom? In what context? If the honest answer is "barely," that's not an AEO problem. That's a visibility and reputation problem that happens to also hurt your AEO. Fix the real thing and the AI outcome follows.

Then get specific about what you want to be known for. "We do marketing" is not something a model can confidently recommend you for. "We help SaaS companies fix their onboarding emails" is. Specificity is memorable, to humans and to machines.

Then earn mentions in the places your buyers and the models actually read. That means doing things worth talking about, and making it easy for other people to reference you. Contribute real expertise where your industry gathers. Build relationships that lead to being cited. This is slower than adding a plugin. It's also the only thing that lasts.

And keep your facts straight everywhere. Your name, your focus, your description. Boring, but it removes the friction that keeps a model from confidently naming you.

You cannot manufacture a reputation you haven't earned and expect a model trained on reality to co sign it.

The shift underneath the shift

Zoom out and something interesting comes into view.

For twenty years, digital marketing rewarded a certain kind of gaming. Find the algorithm's blind spot, exploit it, extract traffic, repeat until the loophole closes. A lot of careers were built there. A lot of agencies too.

Answer engines don't kill that game entirely, nothing does. But they change the odds. When a machine is synthesizing thousands of sources to form a recommendation, the cheapest path to being recommended is, weirdly, to deserve it. To be the thing people would have pointed to anyway.

So when someone asks me how to optimize for AI answers, I don't reach for a technical checklist first. I ask a harder question. If I removed your marketing entirely, would anyone in your industry still say your name? Would there be evidence, out in the world, that you're good at what you claim?

If yes, AEO is mostly about making that evidence clear and consistent so the machine can find it. If no, then you don't have a search problem or an AI problem. You have a substance problem, and no optimization on earth fixes that.

Most businesses are getting AEO backwards because they're asking how to be chosen by the machine. The better question is whether they've built anything worth choosing. Answer that one honestly, and the rest is just plumbing.