How to Rank #1 in ChatGPT: Your Guide to AEO
6 pillars to win AI search visibility in 2026
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Dear GTM Strategist!
We’ve been talking about AEO (Answer Engine Optimization) for over a year now, and honestly? Still a lot of talk, very little action.
The case for acting is obvious. Your customer has changed how they buy. AI search brings more qualified traffic that converts better. And for once, newer brands get a fair fight - because AIs don’t just reward the decades of SEO assets that make the giants nearly impossible to beat.
But here’s the question nobody can answer cleanly: how do you actually get AIs to take you seriously when your ICP types “What’s the best CRM for a Series B startup” or “compare Attio vs HubSpot”?
This isn’t a job for the SEO nerds and demand gen wizards anymore. The customer journey changed, your website traffic-to-conversion math changed with it, and that makes it a GTM problem - your problem.
And if you have no idea what to do about it, you’re in very good company. George Chasiotis, founder of Minuttia, asked 599 marketers about their AEO strategy. Only 14% felt very confident about it. The rest are running experiments they can’t validate, copying playbooks they’re not sure work, and watching organic traffic move in ways they can’t explain.
That gap between “this clearly matters” and “we know what to do” is exactly where most B2B companies are stuck right now. So I asked George to fix that.
In this edition, you’ll get:
Where most teams get stuck with AEO, and why
The 6 pillars to win AI search visibility
Real examples of what works and what backfires, including a case study
A practical way to measure AI search performance when the data is messy and the goalposts keep moving
Let’s hear it from George.
The State of AEO: What 599 Marketers Revealed
My team at Minuttia ran this survey with Kevin Indig and his Growth Memo newsletter. You already know the gist from the intro: lots of activity, not much confidence. So let me skip the “AEO is here” sermon and get into the numbers that should actually change what you do on Monday.
Almost everyone’s in - but most are just dabbling
82% say they’ve adopted AEO “to some degree.” That headline sounds intimidating until you look at the breakdown. Only 7.7% have a dedicated AEO team. Just 21.5% are actively implementing a real strategy. The biggest chunk, 25.9%, are running early experiments, which is a polite way of saying “poking at it.”
So when someone tells you everyone’s doing AEO, they’re technically right and practically wrong. Most “adoption” is tentative. If you build a committed motion with real ownership now, you’re not catching up to the pack - you’re ahead of most of it. That window won’t stay open long, though. 13% already call AEO their top priority, up sharply from a year ago.
Why nobody feels confident (and why that’s good news for you)
The intro gave you the 14% confident stat. Here’s what’s eating the other 86% - and notice that every single cause is something you can fix:
Measurement is the number one complaint. Not because tools don’t exist - we counted roughly 240 AI search tracking tools in a December 2025 study. The tools are everywhere; the good ones are rare. People can’t tell which numbers to trust.
18% can’t tie AEO to business value. No attribution means no ROI story, which means no budget when you go ask for it.
24.2% flat-out don’t know what works. They’re collecting playbooks and guessing.
Most companies are confusing being busy with AEO for being good at it. Doing the work and knowing the work is paying off are completely different things.
The takeaway is almost embarrassingly simple: fix measurement before you scale anything else. The team that cracks attribution wins the budget arguments and pulls ahead while competitors keep running experiments they can’t read.
Don’t just dump this on your SEO team
Most orgs hand AEO to whoever already does SEO. About 20% spread it across cross-functional teams, and a tiny 2.7% bring in outside help.
Defaulting to the SEO team feels natural, but think about what you’re actually doing: piling a brand-new, genuinely uncertain discipline onto people who already have a full plate, usually without new tools or a clear mandate. Then everyone’s surprised when there’s plenty of activity and no movement. Pick an owner on purpose, and give them what they need to actually win.
The traffic drop hiding under all of this
Nearly half - 47.2% - reported organic traffic falling. For 20.5% it was a moderate dip of 10-20%, and for 26.7% it was a serious drop of more than 20%.
Fair warning: it’s not clear how cleanly people separated “AI search ate my traffic” from everything else that moves organic numbers, so treat this as directional rather than gospel. But you don’t need a perfect attribution model to feel the floor shifting. Waiting until the data is airtight is itself a choice, and probably the wrong one.
Bottom line: this space is still messy and young, and that’s precisely why there’s room to win. Let’s get into how.
6 Pillars of AI Search
The problem with how some organizations approach AEO is that they’re trying to reinvent the wheel.
That’s why so many of them are ready to throw SEO out of the window, declaring AI search the new sheriff in town.
But search is still here, and it’s more relevant than ever.
Many of the best practices you used for search engine optimization are still relevant, but we can certainly say they have evolved.
Take a look at the following graph, which provides an oversimplified view of what SEO used to be about:
At Minuttia, we still rely on these six pillars to help our clients with AI search visibility, but with a few tweaks.
Let’s take a closer look at each of them.
Pillar #1: Topic breadth and depth
These two notions are interconnected.
Topic breadth encompasses all topics relevant to you and your business model.
Let’s say you’re a CRM company, and that’s the core topic you’re talking about, but there are also the related ones:
sales pipeline
customer support
marketing automation
and so on.
In its turn, topic depth describes a set of sub-topics and the questions people ask for each of the main topics.
Let us illustrate.
Ideally, you need both topic breadth and depth to lay the foundation for successful AEO and SEO strategies, but there’s a caveat:
You should keep these topics as close to your business model as possible. Let us show you what can happen if you stray too far from it.
Connecteam, a workforce management software company, created a clever (at the time) strategy to boost Google and AI search visibility.
They created a repository of review articles analyzing 500+ of their competitors (as of January 2026).
To add weight to these reviews and show authority to AI search engines, Connecteam created a review methodology page explaining why readers should trust them.
You can find an entire breakdown of Connecteam’s AEO cast study here.
However, right after Google rolled out its Core Update in December 2025, which flagged content unrelated to a website’s area of expertise, Connecteam’s experiment started going downhill.
Organic traffic was the first one to take a hit, but AI citations also followed.
Why did that happen?
The nature of these pages goes against what Connecteam is - a workforce management tool, not a review site, like G2.
Besides, 500+ pages isn’t a small number, so their impact on Connecteam’s visibility is real.
We’re not saying you shouldn’t give up on comparative and self-promotional content.
They simply shouldn’t constitute such a big portion of your content.
Connecteam posting 500+ review pages and listicles, sometimes 16 in one month, is a perfect segway into the next point: Does publishing velocity impact your AI visibility?
Pillar #2: Publishing velocity
Now, it’s well-known that publishing velocity impacts organic visibility.
But we believe it has as much impact on AI visibility.
You already know that Google gives preference to expert content related to the website’s subject matter (Google has doubled down on it lately, with its December 2025 and February 2026 updates).
Data shows that AI search engines behave the same. According to Ahrefs, the average age of content cited by AI assistants is 1064 days - 25.7% “younger” than content cited by Google, which is 1432 days old on average.
What’s more, Ahrefs’ study proves that ChatGPT and Perplexity order citations from newer to older.
We know what you’re thinking: 1,064 days is basically 2.9 years. In what universe is that ‘fresh’?
Sure, both Google and AI assistants seem to cite long-lived content that has passed the test of time. However, your new pages will reach that moment in time once, too; that’s why you should keep them coming, but don’t sacrifice quality for speed.
On that note, as your content inventory keeps growing, you’ll need to find a way to strike a balance between publishing new content and updating older pieces, which leads us to the next pillar: content optimization.
Pillar #3: Content optimization
We follow the same content optimization principles for AEO and SEO.
In a nutshell, the content optimization elements look like this:
Although the process is the same, the playbook for AEO has changed based on how AI search engines surface the results.
Here are some patterns we noticed:
LLMs prioritize early passages, which is why the way the top of your page looks makes all the difference.
Chrome has a limit on the number of passages for embeddings, so you should keep intros short and straightforward.
Long reads don’t generate infinite citations; thus focus matters more than length.
Google’s AI follows the semantic HTML structure of a page and uses headings to understand meaning, which is why they shouldn’t feel ambiguous.
Clear heading structure, such as the one asking a question and immediately giving the answer, makes content more skimmable for AI search engines.
An AI search engine may retrieve a separate section, which is why it should be logically structured but not isolated from the rest of the content.
The main priority is the reader, so aim for a natural flow with a clear structure rather than optimizing content solely for LLMs.
Based on these patterns, we developed a set of best practices that you can see below.
Do these practices work?
We applied them when writing this piece on the best AEO agencies:
a clear structure
straightforward intro and headings
succinct and focused paragraphs
lots of tables
and zero fluff.
Here’s what we’ve got in return:
Of course, all of these best practices become largely decorative if crawl efficiency optimization gets ignored.
Let’s talk about it next.
Pillar #4: Crawl efficiency
Our crawl efficiency analysis is a 4-step process.
First, we check if AI search engines can access your pages, which you can easily do by going to your website’s robots.txt file and seeing if it restricts any user agents from crawling your website.
Here you can see that the website disallows AI agents from training on and crawling the website information, mitigating its chances to appear in AI search engine results.
The next step is creating (or checking the correctness of) the llms.txt file (or the AI Info page).
We should emphasize that adding this page to your website is not a guarantee for better AI bot crawlability, and Google says this page isn’t necessary for better AI bot crawlability.
Nevertheless, our experience working with clients shows otherwise, and a properly structured AI info page can make an incremental difference to your AI visibility.
So, how do you structure your AI info page?
Essentially, it boils down to what AI search engines should know about your company in a specific order:
Now, there are a few things to zoom in on here.
First and foremost, pay attention to brand disambiguation: ensure that an AI search engine doesn’t confuse your brand’s name with something else.
Take Notion, for example. It’s a well-known brand, but it’s also a word on its own, so their llms.txt file has to specify the nature of this brand.
Another important insight here is to be specific about your target audience.
Make sure you include both who you want to target and who is not the right match for your product.
Here’s an example of how we interpreted this section for Minuttia’s AI info page:
You also want to add sources that prove and verify your credibility, such as case studies or research.
For Minuttia’s AI info page, we added three prominent client cases and referred AI bots to our Case Studies website page for more information.
On a general note, you should aim for incremental improvements that would have a tangible impact on how you’re cited by AI search engines, and not chase overnight AI wins.
As for steps #3 and #4, we want to zero in on the best practices for the URL structure that improve crawl efficiency:
For the URL architecture, we follow two different approaches for blog post URLs and for cluster URLs.
In both approaches, we use three different types of keywords to structure URLs.
Here’s how these keyword types compare against each other.
The rules are simple for a blog post URL - you simply choose the keyword type based on the main query your post is targeting.
Here are some examples with 3 types of keywords:
https://www.meilisearch.com/blog/rag-for-structured-data
https://viral-loops.com/blog/how-to-build-a-community-around-a-product
https://viral-loops.com/blog/coming-soon-page-examples
The picture looks a bit different for the topic cluster pages.
Essentially, you have the same keyword types, but they apply to the URL slugs corresponding to each subtopic found in the given topic cluster.
Here’s a visualization to make things clearer:
If you want a real-life example for the topic cluster, the Product Launch 101 piece by Viral Loops shows how to structure URLs for the phrase match.
Basically, you have:
Core page: viral-loops.com/product-launch
Cluster page: viral-loops.com/product-launch/marketing-plan
Cluster page: viral-loops.com/product-launch/plan-templates
Cluster page: viral-loops.com/product-launch/strategy, and so on.
What you want for both phrase and term/semantic match is to keep things simple. Avoid numbers, dates, or anything that would confuse the crawlers.
Now, let’s move on to content monitoring.
Pillar #5: Content monitoring
Let us return to the aforementioned finding by Ahrefs that AI search engines tend to cite fresher content more often. That’s a point to the importance of publishing velocity.
However, focusing too much on new content while ignoring outdated posts is a bit like renovating the kitchen while the basement floods.
Just look at the Peanut app’s website.
According to Ahrefs, Peanut’s blog contains 2,441 pages, which means they built them at scale, most likely posting over 100 blog posts a month.
This effort didn’t go unnoticed, and their organic traffic peaked at 2.5 million monthly clicks in 2023.
However, as their content inventory started to age (some content pieces haven’t been updated since 2022), organic traffic also took a hit.
Essentially, too much focus on net new content created a dead weight of older, sometimes irrelevant pieces, making regular content updates a real necessity.
However, a content update doesn’t necessarily mean a full rewrite.
At Minuttia, we developed a signal-based system that flags content due for an update. This system assigns value, which determines how much of that content needs to be revised:
Light refresh: we change < 30% of content, making improvements to the specific sections, such as outdated statistics.
Content update: > 30% of content is changed, involving a full rewrite for specific sections, e.g., when rankings have significantly declined, or the SERP landscape has changed.
Complete rewrite: we rework the entire piece, mostly when it’s too outdated to salvage, or the search intent for the target keyword has changed.
What is important here is not to fully automate this process and always have a human in the loop.
The cost of a poor update is too high to delegate this task fully to, say, an AI assistant, so there should always be a team member who does quality assurance.
Let’s move on to the last pillar.
Pillar #6: Brand experience and perception
Now, if you go back to the intro to this section, you’ll see that the sixth pillar for SEO optimization mentions backlinks.
However, since the introduction of AI search, backlinks have become only one part of that pillar, yielding to the following for elements, brand experience being the most important.
Now, where do you start with improving brand experience?
You start with what you can control: owned perception of your brand.
Here are some examples of how we manage it: being listed in the directory, including a positioning page on our website, creating relevant blog posts, adding the AI Info page, and so on.
How does that translate to AI search visibility?
The more you invest in owned channels, the more likely AI search engines are to cite them in a branded prompt.
So, it makes sense to invest in multiple platforms for owned perception, but you want to do it in a way that matches your positioning, messaging, and business model.
What about the risk of sounding overpromotional?
It’s a good practice to develop or acquire digital assets to help with distribution - a newsletter, asset library, knowledge base - anything that corresponds to your brand’s positioning.
Now let’s talk about multi-channel perception. Why does it matter for AI search visibility?
The following data insights lay out the reasons perfectly.
According to Ahrefs, YouTube is the most cited domain in AI Overviews: its presence has increased by 34% over the past six months.
Kevin Indig said on his LinkedIn that more G2 reviews correlate with higher AI visibility.
Ahrefs claims that brands getting the most web mentions earn up to 10x more mentions in AI Overviews.
Essentially, it all boils down to the number of brand mentions, and there are quite a few ways to get them without appearing too self-promotional.
But here’s the thing: you don’t always control the narrative when it comes to multi-channel perception. Nevertheless, when a negative brand mention comes along, it doesn’t mean you shouldn’t react.
Take the following example.
Asana, one of the leading work management platforms with 4.4 stars on G2, still gets negative reviews on Reddit. The one below complains about the unintuitive UI, but instead of leaving it to other Reddit users to solve, an Asana representative chimed in to assist with the problem.
This is one of the correct ways to handle such blows to your brand image. And overall, if you want to join a conversation about your company, we suggest doing it from a branded account, so your participation is transparent and trust isn’t undermined.
The same goes for discussing your brand in communities. It’s a bad practice to flood subreddits with promotional posts, like in the example below.
Instead, build or help moderate a relevant subreddit, where both your marketing and support teams can actively contribute without breaking trust.
Obviously, developing owned and multi-channel perception alongside other components of AI visibility is pointless unless you can tie all that to business outcomes.
Yet, AI search ROI and measuring AI performance, according to our survey, proves to be a big challenge for most respondents.
How to Measure AI Search Performance
If you look back a couple of chapters, you’ll see that in our survey, 40.6% of respondents reported measurement and attribution of AI search performance as their biggest challenge, followed by the lack of best practices and inability to tie AI search visibility to ROI.
It’s not entirely unexpected!
If you run the same prompt several times and across multiple AI agents, the results will vary, and even if your brand appeared once or twice, there is no guarantee it will be there the third time. How can you properly measure that?
There’s one crucial caveat to mention when it comes to AI performance metrics: measurement needs repeated sampling and context.
You need to see how things change and evolve over time, and it should be done often, given the volatility of AI search results.
When it comes to specific AI performance metrics, we think the following ones matter most:
Referral traffic from AI platforms (e.g., ChatGPT, Perplexity, Claude)
Brand mentions and visibility in AI search engines
Share of voice in AI search engines
Average position your brand appears in AI search engines against other brands
Number of citations and citation share in AI search engines
Conversions and revenue attributes to AI-referred traffic
Sentiment
Incrementality (h/t Kevin Indig)
AI Crawler Activity
AI Overview Impressions
Here’s a short description of each metric (how we perceive them):
Now we come to the point where those 40.6% of respondents feel the most pain: setting up the tracking tools.
Let’s make one thing clear: you’ll need to use several platforms and assign them to different metrics, such as GA4 for AI referral traffic or Google Search Console for AI Overview impressions.
Below, you can see how we triangulate across multiple data sources:
From these sources, GA4, Google Search Console, Bing Webmaster Tools, and server logs are already familiar to you. The question is, how do you set up an AI search tracking report?
At Minuttia, we follow this 8-step process.
As you can see, the first two steps involve the client because we need to figure out all the relevant topics and related prompts (go to Topic Breadth and Depth to read more about it). Then we break down these prompts into two categories:
Topical prompts related to a use case, problem, or category in which a brand competes.
Evaluation prompts that directly test AI’s perception of your brand (pricing, integration, customer profiles, etc.).
Most AI tracking tools focus on topical prompts alone, but the problem with that is they often give results for synthetic prompts, which don’t always reflect the way how people communicate in AI search engines.
That’s why you need a combination of topical and evaluation prompts to see the whole picture, but while topics are quite straightforward, it’s not always clear how to get evaluation prompts.
Allow us to let you in on how we do that.
The process runs like this:
And here’s the template for an evaluation prompt:
Evaluate [Brand] on the [platform/product/solution]’s ability to [specific evaluation criterion], [desired outcome or business impact].
You can type it into ChatGPT and then check whether the response matches the reality and if there are discrepancies to be fixed.
Note that here you’re measuring perception, not visibility. The primary goal of evaluation prompts is to see how AI search engines see you.
We also recommend checking the cited sources to understand if anything needs to be fixed or created to fill the perception gap, and do that for each AI search engine separately - different search engines mean different retrieval methods, sources, etc.
Which tools do we recommend to track the prompts and generate reports?
Honestly, there is no single tool that would do this job perfectly.
A go-to platform for any brand would be GA4, but what we found is that the data it shares often deviates from the real picture.
For example, in our experience, some AI-driven referral sessions are flagged as direct.
What we recommend you do instead is to create a separate AI Search channel in GA4 that groups ChatGPT, Perplexity, Claude, and any other AI search engines you want to track. But even then, the data can get misrepresented.
So, our closing point is that you shouldn’t take this data at face value and treat it as directional.
No decimal-point precision reflects reality but can indicate some trends, which ultimately make a difference for the bigger picture.
Maja here. The truth about AEO is that nobody has it fully figured out yet. The mechanics shift every month, the tools are still maturing, and what worked last quarter might not work next quarter. But the teams that start building muscle here now will have a real lead 12 months from now.
A few things worth doing this week:
Pull up your
robots.txtand check whether you’re accidentally blocking AI crawlers. Takes 5 minutes, and I’ve seen teams discover they had agents blocked they didn’t even know about.Pick two evaluation prompts (the ones that test how AI describes your brand and category) and run them across ChatGPT, Claude, and Perplexity. Compare what gets cited. That’s your perception gap, sitting right there in plain text.
Big thanks to George and the Minuttia team for putting this together. Drop your questions in the comments, or feel free to contact them directly.
If you want to dive deep in AEO, check out previous GTM articles on this topic:
See you next week.
Maja
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Great piece. Thanks for working together on this, Maja!
Nice! I enjoyed that very much. I wrote about this from more of a beginners lens recently: https://michaelperumal.substack.com/p/discovery-optimization-for-ai-a-simple