AEO Is About More Than Being Easy to Extract. Here’s What We’re Missing.
Most AI visibility advice aimed at content marketers boils down to one idea: Make your content easier to extract.
Which is why we keep hearing the same recommendations:
Put the answer at the top
Use headings
Add definitions
Write FAQs
Use lists
None of that is bad advice. But to me, it feels like we've started to reduce AEO to a formatting problem — and it shows in some of the content being produced.
When we don't understand why the tactics work, we tend check the boxes without understanding what they're trying to accomplish. And I've been seeing a lot of that lately: content that follows the advice but still doesn't say much.
Which raises a more interesting question nobody seems to be asking:
Why do these tactics work in the first place?
What AI Systems are Actually Doing When They Cite Something
Extractability is a big piece of this, obviously.
AI systems retrieve and evaluate content in chunks. A claim that is self-contained and clearly stated is easier to:
Match to a query
Attribute to a source
Reuse in an answer
Let's stop and think about it from a human angle.
You’ve just read an article and understand the concept well. Now someone asks you to pull a quote from it. If the key insight is spread across three paragraphs, you can still do it. but now you're editing, combining ideas, filling in gaps.
That's work — and work introduces uncertainty. For AI systems, uncertainty generally lowers the likelihood that something gets used.
So yes, formatting helps make content extractable. But there's another part to this that people are missing.
When AI systems build answers, they don't just pull information. They also choose among competing sources.
They retrieve multiple candidates.
They rank them.
And then they favor the information they can understand, attribute, and reuse with the least amount of uncertainty.
Exactly how different systems weigh those decisions is still evolving. But the broader pattern— that selection happens in addition to retrieval — is well established.
And I think that changes how we should think about AEO.
If AI systems are choosing among multiple candidates, visibility isn't just about getting extracted. It's about being selected. Which means the challenge isn't just making content easy to extract — it's creating something actually worth citing.
So instead of asking "how should I format this for AI?", I think we should be asking: beyond the formatting, what are these tactics actually doing?
Specificity Signals Someone Actually Knows Something.
One of the most common pieces of AEO advice is to use specific numbers. Good advice — but if we're not careful, "use numbers" becomes a blind requirement, and we miss the deeper point: numbers are often a signal of something more important
Specificity. And specificity tends to correlate with something even more valuable: actual insight.
Compare these two sentences:
"Email segmentation improves engagement."
and
"Segmenting by purchase history reduced unsubscribe rates by 40% and increased click-through rates by 22% during a six-month test."
The second sentence could only exist because somebody actually investigated the problem. And that's really the point.The value isn't in the numbers themselves. It's in the fact that they point to observations, constraints, and experience.
A lot of B2B content never gets past the surface. It introduces an idea, explains why it matters, lists a few considerations, and then…stops. Nothing wrong with it. But nothing particularly concrete gets said either.
And when enough expertise doesn't make its way into the content, the writing starts to drift into what I think of as -ity language:
Visibility. Efficiency. Flexibility. Scalability.
These words usually appear where a real example, threshold, or observation should have been.
But the deeper point is this: Content becomes more useful to AI when it includes details that couldn’t have been written by just anyone.
Sometimes that shows up as numbers. But it can also show up as:
Defined conditions or constraints
Named use cases
Clear tradeoffs
First-hand observations
So, before you publish a claim, ask yourself: Could anybody have written this? If the answer is yes, that's usually a signal to go deeper to win that citation.
Definitions Help. Clarity Is the Real Goal
Definitions are one of the most commonly recommended AEO tactics — and for good reason.
A well-written definition is self-contained. You can pull it out of an article and it still makes sense without the surrounding paragraphs. That's what makes it easy to extract and reuse.
That's really what AI systems are responding to. Not the definition format itself, but the fact that the meaning travels with it.
And that's important because answer-shaped content isn't always answering "What is this?"
Sometimes the answer is:
"X is Y, it matters because Z, and it works best when A is true but B is not."
That's still self-contained. It's a claim that doesn't require the reader — or the system — to reach for outside context to understand it.
Which is why I think "AI likes definitions" has unintentionally led to some of the worst content on the internet.
Because when that's the takeaway, content turns into glossaries. Terms get defined before any real point is made. Experienced readers get walked through basics they already understand.
The real goal is claims that carry their own weight. And in B2B content especially, that's harder than it sounds — because we tend to assume connections are obvious. We lean on shorthand. We string ideas together without explaining how they relate.
For example:
"Segmenting users improves retention."
The ambiguity isn't in the words. It's in the relationships. Which users? Segmented how? Why does that affect retention? The reader and the AI are left to fill in the gaps.
Compare that to:
"Segmenting new users by product usage patterns lets teams trigger different onboarding sequences. Users who get guidance matched to the features they're actually using reach value faster—and that's what moves retention."
No formal definitions. Just relationships made explicit enough that the claim stands on its own.
It's about making clear:
What exactly is being claimed
Where one idea ends and another begins
How concepts relate
What conditions or tradeoffs matter
Why one thing leads to another
A definition answers: What is this?
Clarity answers: What does this mean, why does it matter, and how does it connect to everything else?
Answer Early. But Only If You Actually Answer.
A lot of B2B content introduces a question, builds context, adds nuance — and never clearly lands. Instead, it defaults to:
"It depends."
"There are several factors to consider."
"Results may vary."
And honestly, as writers, I think we hedge because we're trying to be responsible. We want to acknowledge exceptions. And we don't want to overstate.
But here's what hedging actually produces from a citation standpoint: nothing attributable.
An AI system building an answer needs a claim it can repeat with confidence. A hedge doesn't give it that. "It depends on your situation" doesn't resolve the user's question. It's that it gets passed over in favor of a source that actually commits.
Consider the difference between these two answers to the same question:
"It depends on your budget and goals."
"For most B2B SaaS companies running under $50k per month, broad match outperforms exact match."
Both might be accurate. Only one gives a system something to hold onto. The second will get cited. The first won't — because it never quite said anything concrete.
You can always follow with nuance. But if the article never makes an actual claim, there’s nothing to cite.
Stop Optimizing for Extraction. Start Optimizing for Selection.
We’re all still learning how citation and selection work under the hood. No one really has the full picture. But a few patterns are clear.
Vague, generic content that requires stitching ideas together across paragraphs is less likely to be chosen — regardless of how it's formatted.
We should be more focused on creating content that is:
Specific enough to be meaningful
Clear in its assertions
Consistent and internally coherent
Grounded in real expertise from a source that actually appears to know what it's talking about.
So the question isn't: "Is this article formatted for AEO?"
It's: What about this piece is actually worth citing? What does it say that someone couldn't get from a dozen other articles? What claim is clear enough to extract, specific enough to matter, and defensible enough to repeat?
Because formatting alone doesn't make content valuable. Formatting makes valuable content visible.
And those are two very different things.
