Last Updated on August 10, 2026 by Alex Birkett
What follows is my take on answer engine optimization (AEO) strategy.
It’s an evolving topic, but as of late 2026, this is what I’m seeing working with enterprise brands to grow through AI search.
The short version. An AEO strategy is a resource allocation plan for becoming a source AI answer engines trust, cite, and recommend. It decides three things: which prompts actually matter to your buyers, where your credibility comes from today, and how limited hours get spent across owned content, third party surfaces, technical optimization, and experiments to change both. Most published “AEO strategies” are checklists. A checklist is not an allocation.
What is Answer Engine Optimization (AEO)?
AEO is a process by which a brand seeks to increase their performance in AI engines, whether by increasing visibility in answers, increasing citation or retrievals for a prompt, optimizing a website for agentic discovery and usability, or driving purchase or product functionality via AI engines or agents.
In short, it is leveraging AI engines as a channel to reach users or customers.
What an AEO strategy actually is
An AEO strategy is not a checklist. An AEO strategy is a structured and intentional path towards an outcome or objective.
So first, we need to define what the goal or objective of AEO is.
In most cases, it is to grow brand visibility in AI engines. This means, in other words, to increase the probability that your brand is mentioned or recommended in an AI answer when prompted by a user.
Which, of course, means we must also define the prompt set that matters. We’re getting ahead of ourselves – I will cover prompt selection extensively.
Okay, let’s assume you’ve got a clear goal and proper telemetry. What’s included in an AEO strategy?
Start with the stack, because it clarifies where the choices live.
- Technical foundations – can machines reach your content, parse it, connect it to the right entity?
- On-page clarity and coverage – do you have content that answers prospective questions with clarity, depth, and utility? Can a passage be lifted out and still make sense?
- Off-page credibility – does anything outside your domain corroborate what you say about yourself? How present are you in influential citation sources? Is the information accurate, up to date, favorable?
AEO vs SEO: What’s the Difference?
Against SEO, the ingredients look similar on the surface. You’re still publishing, still optimizing, still trying to be the thing that gets picked. The math is different, the user experience is different, the scorecard is different.
| SEO | AEO | |
|---|---|---|
| Unit of retrieval | Page | Passage |
| Output | Ranked list | Sampled synthesis |
| Winner | Highest ranking page | Most trusted source |
| Scorecard | Rank, sessions, conversions | Mentions, citations, sentiment, self-reported attribution |
| Primary lever | Owned content plus links | Ubiquity within a category or segment |
| Stability | Weeks to months | Days |
That last row, stability, is something not often considered in strategic planning. It’s quite different to conventional planning than the rest combined, and I’ll come back to it.
In SEO versus AEO, it’s worth considering how much they overlap and how quickly they are drifting. The advice to “just do good SEO” rests on an overlap that is diverging.
Ahrefs, across 863,000 keyword SERPs and four million AI Overview URLs, found only 38% of AIO citations now come from top ten ranking pages, down from roughly 76% in July 2025.
BrightEdge puts top ten overlap at 16.7%, though their headline is that 54.5% rank organically somewhere. They disagree on magnitude, agree on direction, and I’d rather show you the disagreement than pick the number that flatters my argument.
Point is, there’s a divergence in the retrieval set; there’s also an increased importance of external sources and credibility to drive visibility and sentiment.
Increasingly, there is the emerging field of agentic experience optimization, where products optimize not only for discovery within AI answers for humans, but instead they are optimized for agentic usage or purchasing.
Even if SEO and AEO were built from the same ingredients, they represent different functionalities for users, interfaces and mechanisms of discovery. They are, in practice, often additive for the customer – in other words, a prospect may user AI engines in a completely different way to search engines, thus complementing a customer journey instead of upending it.
Is it AEO, GEO, or AI Search?
One paragraph on the acronyms, then never again.
In US search volume, generative engine optimization peaked at 14,343 a month in July 2025 and had fallen to 6,692 by June 2026. Answer engine optimization peaked lower and later. AI SEO – the term nobody in the industry uses but many buyers do – beats both at 8,500.
I do not care about the acronym, I care about the work. Pick a word, put it in your job titles, move on.
Why most AEO strategies fail: the middle is missing
The industry has caught up to a few tactics: listicles and off page brand mentions.
There’s a lot more to the story, though.
Most people understand the narrative around AI search. A prompt fans out into subqueries, retrieval, synthesis, re-ranking, and answer delivery. Simplified, of course. People understand that your website as well as other websites inform the answers. They understand, to an extent, the systems are probabilistic and personalized.
Tactically, the industry seems to have converged on schema, question headers, chunking, FAQ blocks, and page speed. We’ve run dozens of experiments on the commonly espoused ones, like headers as questions, and found no significant effect size on AI search or traditional SEO. The original paper on generative engine optimization has largely failed replication.
What’s missing is the middle: concrete, resourced, measurable programs.
[this section needs to be fleshed out. It does not yet make the case as to what a concrete, resourced, measurable program. Obviously this is important.]Start with the prompt set, because the prompt set is the measurement
[this section needs to better frame – concisely – what prompts are, what prompt tracking is, etc. for the reader]There is no reliable prompt volume data, and there probably won’t be. And that’s probably a good thing.
If a credible prompt volume metric existed tomorrow, we’d do what we always do: sort descending, brief writers (or agents) against the top rows, reverse engineer whoever’s winning.
A decade of keyword volume produced a genre (the derivative ultimate guide) because the metric was legible and legibility is magnetic.
Fisherian runaway, marketing department edition.
The absence of the number forces you back onto the only thing that reliably creates value, which is customers. Alpha hides where spreadsheets fear to tread: inside the messy, idiosyncratic contours of your actual strengths, weaknesses, opportunities, threats.
Practically speaking, the prompt set is the measurement, at least for the approximation of the experience within AI engines. It determines what you’re measuring, who for, and whether the result correlates with anything. You could run a hundred prompts that all contain your brand name and produce a magnificent number that means nothing.
Build it by triangulation. Buyer research (interviews, sales call transcripts, the Reddit threads where your category argues with itself), product (what you do, your features, your category entry points, how you stack up in the market), and channel data (keywords, observed prompts, query fanouts, reverse engineering first party data to extrapolate to market questions).
Then segment by intent – unbranded discovery, comparison, branded, proof seeking – and map to funnel stage, verticals, or personas. Track against six to eight direct competitors, not the whole market.
One caveat almost nobody gives you: week over week movement on a small prompt set is noise. SISTRIX, running 82,000 prompts across three platforms for seventeen weeks, found citation sources rotate 56% per week in Google’s AI Mode and 74% in ChatGPT.
AirOps reports only one in five brands hold citation visibility across five consecutive runs of the same query.
If your AEO dashboard reports daily deltas, it reports variance.
What is an AEO strategist?
Someone who owns three things: the prompt set, the allocation, and the reporting line to revenue. Not a content optimizer or technical SEO with a new title. The job is deciding what to do and what not to do and defending that decision to people who’ve read a checklist.
The AEO strategy allocation: barbell it
How should you allocate your time and resources for AEO?
Obviously, you could do this many ways. And many people have different philosophies.
Here’s mine: think of it as a risk adjusted portfolio. I like a barbell, where most bets are predictable, some percentage are speculative, and then we have an optimization bucket:
70% stable assets. Predictable, slow upside. Things that have worked in every information-retrieval regime and will keep working in the next one. Review site presence and velocity. Being in the third party comparisons and listicles (Surround Sound SEO). Original research with distribution and media relations. Product marketing clarity, so that when a model describes you it has something correct to describe. Baseline extractability and technical accessibility.
20% speculative. The outlandish bucket. New surfaces, agentic commerce readiness, creator and community programs, formats you have no evidence for yet. Capped downside, but solid optionality. Outright failure generates a signal and learnings, it teaches you something and lets you iterate.
10% optimization. Schema. Headers. Chunking. llms.txt. Small experiments.
Then the hard part: kill the middle.
Medium risk is a sucker’s game. In AEO the middle is the derivative guide (think the ultimate guide to a topic four hundred people have already ultimate guided). No matter how fast you produce one, you’re producing the same artifact as everyone else, at a marginal cost that has gone to roughly zero for them too.
Calibrate on difficulty too. Narrow, low competition citations are fairly simple, but may not have strong impact. Correcting factual errors about you in third party sources moves faster than people expect (and is very, very impactful, even if less measurable). Shifting category level consensus is pretty hard.
Where AEO isn’t worth it
If you haven’t launched, there’s nothing to optimize. No third party footprint, no reviews, no mentions. The fix is customers, not schema.
If your product marketing is unresolved, slow down. The model will faithfully repeat whatever confused thing your site says about you, at scale, to everyone. Fix the sentence before you amplify it.
If your buyers don’t use AI for discovery in your category (and some don’t) you’re optimizing for a room nobody’s in (though this is, in my humble opinion, very likely to change as generations evolve and AI is introduced into nearly every interface).
And if the same hours could go to a channel already producing pipeline, you know, there’s an opportunity cost to consider. Do I think AEO is very, very impactful? Yes. I get a lot of leads from it myself. But I also do private dinners and IRL events, and quite frankly, those are usually better lead drivers.
Four AEO Programs
There are many AEO programs you could run. Here are a few I’ve seen that are effective and fairly commonly applied.
1. Review ecosystem. G2, Capterra, whatever your category’s equivalent is. My company’s research on 25,755 AI citations across 200 B2B SaaS prompts spanning all intent levels found G2’s citation share rose 76% over the study window, 2.09% to 3.68%, making it the second most cited source in bottom-of-funnel answers. This was past YouTube and LinkedIn, behind only Reddit. On proof and evidence queries its combined share hit 12.69%, a 93% lead over the next domain. G2 is functioning as the trust layer.
Which makes this a review velocity program, and review velocity isn’t solely a marketing function. Own it jointly with CS and sales, who are the ones talking to happy customers. Report as: review count and recency by site, plus citation share of those sites within your prompt set.
2. Third party placement. Editorial listicles, comparison pages, aggregators, directories, communities. Prioritize by what your prompt set retrieves, not by domain rating. Report as: placements landed in confirmed retrieved sources, and change in co-occurrence with named competitors.
3. Original research. Highest leverage in the stack, hardest to copy. In our own crawler logs, after the homepage and service pages, original research pages are the most frequently visited by AI crawlers by a strong margin. I’ve seen several other case reports that show similar findings.
The thing that works about original research, though, is that it is extensible and repurposable. You can leverage it for dozens to hundreds of external brand mentions. You can ship it at scale through organic social. You can sprinkle it in your existing content for better performance, or into your sales enablement for better close rates. But most importantly, it’s substance and it’s source material, unlock most of the copycat content out there. Report as: citations per study, crawler hit rate, placements unlocked.
4. On page and technical hygiene.
Write self-contained passages. Use schema for entity disambiguation. Check robots.txt for if it is blocking OAI-SearchBot keeps you out of ChatGPT’s search answers. Plenty of sites have blocked the wrong one and don’t know it.
How to measure AEO strategy
I think AI visibility is a diagnostic metric, not a performance metric. It’s like bloodwork.
With bloodwork, you get a snapshot in time and a panel (metabolic, lipid, CBC), each marker with its own reference range, each meaningful in a different clinical context. There are ranges, but it’s really a diagnostic that you need to read in context and learn from
With average visibility scores, you’re stuffing a lot into this metric. All data in aggregate is crap, as Avinash Kaushik has been saying for twenty years about a different set of dashboards.
It’s got a few problems as a performance metric:
- Heterogeneous inputs: you’re averaging discovery prompts with comparison prompts with branded prompts, which are not the same measurement.
- No denominator: you might be 80% visible on prompts nobody asks and 5% visible on the one that drives all the pipeline, and the composite looks fine.
- Mentions aren’t recommendations: ask ChatGPT for the best CRM for a five-person startup and Salesforce appears. Visibility score, one. What the answer says is that Salesforce is the industry leader but overkill for a team that size.
So use a constellation instead of a score.
- Lagging: pipeline, self reported attribution, sentiment and messaging alignment.
- Leading: AI referral traffic, AI visibility, branded search.
- Inputs: brand mentions, citation share, crawler hits, passage retrieval rate, topic coverage.
Together they converge on whether your visibility means anything.
Self reported attribution is huge here because of the “dark funnel” with AEO. It’s apparent that man users don’t click within the interface, so traffic often shows up as organic or direct. ,Click-based telemetry often says 2–10% of leads are LLM attributed, but self reported will often show a significantly higher number (we’ve seen 2-8x differences).
Visibility tracking is good. Rooted in real product marketing foundations, built on a prompt set that reflects actual buyers, calibrated against business outcomes, it’s the most useful new instrument marketers have gotten in a decade, which is a regular and samples read on the market’s synthesized opinion of you, which no brand tracking study ever managed.
Who owns AEO strategy?
I don’t know yet.
AI search visibility is an emergent property of many things your organization does, and of what your competitors and your customers do. One output. Many inputs. The blog is one input among many, which is uncomfortable for those of us who built careers on the blog.
Everyone has a claim. SEO says good AEO is good SEO, and they’re (mostly) right about the technical layer. PR says off page dominates, and the data agrees, but is descriptive and observational, and likely has interaction affects with owned content strategy. Brand says it’s inherently multidisciplinary, which is actually a very good argument.
So who knows? Director of Digital Visibility sounds good to me. What they need to do is be able to orchestrate and direct multi-disciplinary teams while owning outcomes and resource allocation.
I care less about the title than the traits of this person: customer obsessed, oriented towards influence instead of just sessions/traffic, and very effective at cross-function alignment and execution.
Probably a growth or experimentation mindset as well.
Conclusion
The tactics are, in large part, free now.
Your competitors have the same checklist you do, which was probably generated from Claude and is probably regurgitating terrible information. They’ve got the same tools, the same ability to write the same guide at the same marginal cost.
When everyone gains access to the same efficiency, the efficiency stops functioning as a differentiator.
What’s left is the expensive, or dare I say, fun part. Customer research and obsession. Real customers who leave real reviews. Research nobody else can run because nobody else has the data. A position clear enough that a machine can repeat it correctly to someone who’s never heard of you.
An AEO strategy isn’t the list. It’s the decision about which expensive thing you’re willing to do.
Load the ends. Clear the middle.
AEO Strategy FAQs
What is an AEO strategy?
An AEO strategy is a resource allocation plan for becoming a source AI answer engines trust, cite, and recommend. It decides which prompts matter to your buyers, where your credibility comes from today, and how limited hours get spent across owned content, third-party surfaces, and original research.
What is an AEO strategist?
An AEO strategist owns three things: the prompt set being measured, the allocation of hours across owned and off-site work, and the reporting line connecting visibility to revenue. The core skill is deciding what to do and what not to do and defending that decision to stakeholders who have read a tactics checklist.
How is AEO different from SEO?
The ingredients are similar; the math differs. SEO competes for a ranked position with the page as the unit. AEO competes to be the trusted source, with the passage as the unit and the output sampled from a distribution rather than retrieved from an index. Off site credibility carries more weight, and results move faster (though perception is difficult to sway).
Is AEO the same as GEO?
Functionally, yes. AEO, GEO, LLMO, and “AI SEO” describe the same practice with different labels, and the terminology has not settled — GEO led search volume through 2025 and has declined since, while “AI SEO” quietly outranks both. Pick one, use it consistently in your job titles, and stop debating it.
How long does an AEO strategy take to show results?
It depends entirely on what you’re changing. Correcting factual errors about you in third-party sources moves fastest. Review-velocity and placement programs compound over one to two quarters. Shifting category-level consensus — being named among the best options in your space — takes considerably longer, because it means changing an entire conversation.
How do I know if my AEO strategy is working?
Not from a single visibility score. Use a constellation: pipeline and self-reported attribution as lagging indicators, AI referral traffic and branded search as leading ones, citation share and crawler activity as inputs.