Do you have search market fit for AEO/SEO?
The volume numbers from any tool tell you how many people typed something into a search bar, but they tell you nothing about why they typed it.
This week’s newsletter is sponsored by North Star Inbound and Airops
Paid subscribers can download the search market fit playbook linked in this newsletter
Most search strategies start in the wrong place because they begin with a keyword or prompt in a research tool and rely on the suggestions it produces. The volume numbers from any tool tell you how many people typed something into a search bar, but they tell you nothing about why they typed it. Understanding the search user is not optional if your KPI goes beyond visibility in an LLM or search results.
[Sponsored by Airops]
Just because a process uses AI doesn’t mean it’s efficient.
Most SEO teams are running into the same wall right now. One-off prompts, scattered workflows, a handful of internal GPTs nobody maintains. AI is in the stack, but the work still feels manual.
The shift that’s actually moving the needle is agentic Playbooks. Always-on systems that handle execution end-to-end, get better the more they run, and let the team focus on strategy instead of prompts.
On June 11, AirOps is hosting a session with Tyler Roehmholdt (Bitly), Nicholas King (Udemy), and Bridget Nelson (Chime). They’re walking through how their teams use Quill, AirOps’ agentic Playbooks product, to win AI search. Live demos, the path from prompts to workflows to agentic systems, how to add guardrails without creating bottlenecks, and the use cases that tend to deliver the first real wins.
The teams 3x their AI citations aren’t doing it with prompts. They’re doing it with systems.
June 11, 2026. 12pm ET. Live on Zoom, recording sent after.
I have watched enterprise organizations spend millions on content programs built around keywords that looked excellent in the tool but had zero connection to a real commercial transaction. One well-known company built thousands of product templates targeting terms that simply did not exist in any meaningful search context. They achieved the desired “rankings” but never converted any users.
There was a lot of blame to go around, but the problem was that no one asked the most obvious question before the program launched: Is anyone actually searching for this in a way that leads to us? And, more importantly, will the traffic they did get lead to any conversions?
The requirement to understand the user was true when SEO was purely about traditional search, and it is even more true now that AEO has entered the picture. AI has substantially altered the user journey, and as I wrote about a few weeks ago, AEO is not SEO 2.0.
User journeys are paramount
To begin mapping the user journey, you need to understand search-market fit, which is akin to the startup world’s product-market fit. The idea is simple: before you invest serious money or time in a search effort, you need to confirm that the users who would benefit from what you offer are actually searching for it, and that what they find when they arrive matches the performance you deliver. If those two things are not true, you should redirect your search budget.
The analogy to product-market fit extends further than most people realize. A startup without product-market fit doesn't solve the problem by running better ads. So too, a company with no search-market fit doesn't fix that problem by conducting technical audits and adding more content.
Spend appropriately
This isn’t to say that you should skip SEO or AEO completely, but there’s no justification to spend huge budgets when you would get no returns from that investment.
Programmatic SEO efforts can be the worst offenders of this strategy. There might be demand for one product, but not if you scale it to millions of variations, especially when search engines and LLMs can parse whether a search modifier leads to a different intent. For example, should you make a product landing page for every state or even every city? In 2026, I hope no one thinks that zip code pages are ever a good idea.
For a job site like Indeed, does the job location matter? Certainly. But for a product like Upwork's, does it matter which city the freelancer is based in? Maybe. Building pages for every city doesn't expand the addressable market; it just creates thousands of pages competing for users who were never going to filter by geography anyway. Showing up in search is not a real goal; it’s a means to an end.
Breaking down the journey
Most people focus on the keyword part of the user question but ignore two other essential pieces of data. Getting keyword ideas is the easy part. Knowing their intent far more important.
The assumption that the query and the intent are the same thing is completely flawed. A user searching for “employee survey” might want to run a survey, or they might want to see examples of how other companies have done it, or they might be a researcher writing about HR technology, or they might be an HR manager building a budget case for their boss. This same query has four distinct users, so each will have a different next step after landing on your page.
Intent itself still doesn’t solve for search-market fit, because creating an experience that might match user intent doesn’t tell us whether the user wants to convert on your landing page. This is the hardest part to be honest about. It requires a company to clearly look at its own product and ask whether the commercial transaction it wants to occur aligns directly with the search intent, not loosely. This is where you factor in price, conversion funnel, and most importantly, trust.
AEO + search market fit
When you layer AEO onto all of this, the stakes get higher. AI responses make it even easier for users to solve their search intent without ever clicking anything. The pre-LLM model of capturing users via search visibility and converting them on the landing page still works for high-intent transactional queries. Informational and consideration content now feeds directly into the AI engine, which keeps the user. They will never even begin that journey.
Users who previously would have clicked through to a review site or a comparison page are now getting synthesized answers directly. The companies cited in those answers might be feeding the LLM without giving users any reason to click on the source. Remember, being cited is not the same as being chosen. If your goal is to appear in an AI response, that is a brand awareness outcome, not a growth outcome. Conflating the two is how companies end up optimizing for visibility metrics and then complaining that AEO isn’t driving revenue.
How to find search market fit
Search the terms you are targeting and look at what Google or the AI engine surfaces, not as a competitive analysis exercise but as a user simulation. What does someone actually get when they search for the thing they want to be visible for? If the results show a completely different type of content or product than the one your business is built around, that is your signal. No additional effort will overcome a fundamental mismatch between what you offer and what the engine has decided users in that query context need.
Paid subscribers can download this search market fit playbook and use my TAM calculator
Pay attention to when search engines modify your query. When you type in a term, and Google changes it to something else in the results, the algorithm is telling you it has decided your query means something different than what you think it means. This happens constantly, and many either ignore it or fight it. Understanding why it happens tells you something important about how users actually use language when searching, as opposed to how your company talks about itself internally.
The same dynamic exists in LLMs. If you prompt an LLM with your target topics and the response it generates doesn’t mention your category of product or solution, the engine has effectively told you the same thing this exercise on Google did.
Timing
There is also a timing dimension to this that gets ignored. If you were ranking for "LLM" as a keyword in 2020, you had traffic but no business outcome. If you were visible for the same keyword two years later in 2022, that ranking became one of the most valuable pieces of real estate on the internet. Search-market fit is not static. It shifts as markets mature, as user awareness grows, and as products improve.
To illustrate with an example of something that will change before the end of the year. LLM glasses aren't something people are really looking for right now, but they will be soon. (Google re-introduced Google XR at I/O last week)
If you built content around LLM glasses, you could be first in Google’s results and still not drive meaningful revenue. But when that changes, the revenue will come without making any substantive changes to SEO. You can make this bet if you have conviction that the future will change, but without that, you are chasing empty-calorie SEO.
This also means search-market fit can disappear. Categories that had strong search-market fit two years ago can disintegrate into AI-synthesized answers, leaving no room for what once drove search traffic. Affiliate sites will feel this crunch. If you built a comparison site or a review aggregator in a category that LLMs now answer directly, the fit you had is gone, and no amount of optimization recovers it.
User research is not pre-work for an SEO or AEO program. Before you build content briefs and landing pages, you need to understand who is searching. You need to know what they want, and whether there is a real line between their intent and your product.
That line either exists or it does not.
When it does not, you can still build a perfectly optimized page that ranks for nothing useful and gets cited by every AI engine on earth, because the user at the other end of that query is never going to become your customer anyway.
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