An AEO strategy is the thing nobody hands you once the definitional article ends. You've accepted that answer engines matter, and still don't know what to do on Monday. Answer engine optimisation gets written up as a menu: add FAQ schema, shorten your paragraphs, get onto Reddit. A strategy isn't a menu. It's five phases run in order, and the sequence matters more than any tactic on the list. Skip the first and you'll never show the other four paid off.
What Is an AEO Strategy? (The Short Answer)
An AEO strategy is a sequenced plan for getting your organisation named and cited by answer engines such as ChatGPT, Perplexity, Gemini and Google AI Overviews. It runs in five phases: baseline measurement, entity clarity, answer-first content, technical accessibility, and ongoing tracking, in that order.
Three terms. Answer engine optimisation is the practice of making your organisation the source an AI-generated answer draws on, rather than a blue link someone might click. For the full definition, start with what answer engine optimisation is. An answer engine is any system answering a question with a synthesised response rather than a list of results. A citation is the moment that answer names or links to you, and it's the unit of success here, not a ranking position.
A strategy differs from a tactics list in one respect that sounds pedantic and isn't: it says what happens first, what has to be true before the next thing starts, and how you'll know it worked.
The Five Phases at a Glance
Here's the whole framework. Each phase has an objective, a duration, an owner, and a checkpoint that says it's safe to move on. Durations assume a small team, not a content department.
- Baseline. Record what answer engines say about you today, across a fixed set of real buyer questions.
- Entity. Make name, location and service naming consistent everywhere, so an engine can tell who you are.
- Content. Write answer-first pages against your baseline questions, structured so a passage can be lifted whole.
- Technical. Make sure the content can be crawled, parsed and indexed. This phase needs a developer.
- Measure. Re-run the same prompt set monthly, read direction rather than scores, change one thing at a time.
| Phase | Objective | Typical duration | Owner | Move on when |
|---|---|---|---|---|
| 1. Baseline | Know what engines say now | 1 to 2 weeks | Marketing lead | A logged, repeatable prompt set with results |
| 2. Entity | Make the brand legible | 2 to 4 weeks | Marketing lead, no developer | Name, location and services match everywhere |
| 3. Content | Give engines something worth citing | First tranche in 4 to 6 weeks | Writer | Top questions each have a self-contained answer |
| 4. Technical | Make content reachable and parseable | 1 to 3 developer weeks | Developer, parallel to phase 3 | Pages indexed, rendering without JavaScript tricks |
| 5. Measure | Read direction, decide what changes next | Monthly, indefinitely | Whoever owns phase 1 | Never. This one doesn't end. |
Phase 1, Baseline What Answer Engines Currently Say About You
The objective of phase 1 is a record of your current standing to compare against later. It's the phase that gets skipped, and skipping it is what makes the next six months unarguable.
The work itself is unglamorous:
- Write 20 to 30 questions a real buyer would ask, in their words, not yours.
- Run each several times, in fresh sessions, across all four engines.
- Log presence, share of voice against named competitors, accuracy, sentiment, and whether you were cited or merely mentioned.
We won't rebuild the protocol here: how to measure AI visibility covers scoring and session hygiene. What matters is that the instrument is fixed before anything else moves.
One warning, and it's why "several times" is in there. The same question asked twice in an hour returns different brands. A screenshot of a good answer proves nothing, nor does a bad one. Read the distribution, not the sample. For the method at scale, our Canadian AI visibility benchmark is the worked example.
Set expectations low on the first pass. Across the baselines we've run, brands showed up in a little under a fifth of the prompts they thought they owned. That's normal, not an indictment of anybody's marketing.
Phase 2, Fix Your Entity Before You Write Anything
The objective of phase 2 is to make sure an engine knows who you are before you ask it to recommend you. Most of it is correction rather than creation, which is why it's cheap and comes second.
Go through your website, Google Business Profile, LinkedIn, directories and any listicle already ranking in your category, and make these identical everywhere: legal and trading name, city and service area, service names, founding details. Inconsistent service naming is the finding we hit most often and the cheapest to fix. In the baseline audits we ran through the first half of 2026, 23 of 31 sites named at least one service differently on their site than on their Google Business Profile.
There's evidence this off-site work carries weight. Ahrefs' study of 75,000 brands found branded web mentions correlated most strongly with AI Overview visibility at 0.664, well ahead of backlinks at 0.218. Correlation isn't causation, as Ahrefs say, but it fits what these models are: systems that learn a brand from text about it.
“very little impact on SEO, but a much bigger impact on GEO”
Ryan Law, Director of Content Marketing at Ahrefs, on unlinked brand mentions, Ahrefs
Two cautions. Google's guidance on generative AI features, last updated 10 July 2026, warns against chasing inauthentic mentions, so this phase is about accuracy, not volume. And getting onto the listicles already ranking in your category counts here as much as in link building, because those pages are often what an engine reads to learn your market.
Nothing here requires a developer. If your engineering time is booked out for a quarter, you can still start.
Phase 3, Build Answer-First Content Against Real Questions
The objective of phase 3 is to give engines passages worth lifting. Note the word passages. Engines extract sections, not pages, so a page can be cited for one paragraph and ignored otherwise. That changes how you write more than what you write about.
Three rules carry most of the value:
- Derive the plan from the prompt set, not the keyword tool. Phase 1 gave you the questions people ask. A keyword tool gives what they type into a search box, which is narrower every year.
- Answer in the first 40 to 50 words of every page. Self-contained, no set-up. If the opening paragraph needs the heading above it to make sense, it isn't self-contained.
- Use tables and numbered sequences. They survive extraction intact in a way flowing prose doesn't.
There's research behind the third point. The team behind GEO: Generative Engine Optimization, presented at KDD 2024, found changes of this kind raised source visibility in generative engine responses by up to 40 percent, with added quotations and statistics the strongest single methods.
This is why answer-first formatting compounds. Google's documentation describes a mechanism it calls query fan-out: the model generates concurrent related queries alongside the one the user typed, then pulls results for all of them. Whatever gets retrieved across that spread reaches the human, so one good page can be picked up through a dozen questions nobody asked. The same guidance treats spinning up a page per variation as scaled content abuse. Fan-out argues for fewer pages written better.
For surface-specific execution, how to rank in ChatGPT and how to show up in AI Overviews go deeper. On a recent engagement, CauseWorx went from roughly 2 of 25 tracked prompts to about 9 over two quarters. That's the pace our answer engine optimisation work moves at.
Phase 4, Make the Content Technically Reachable
The objective of phase 4 is to remove every reason an engine might fail to read what you've written. This one needs a developer, and it runs in parallel with phase 3.
The list is familiar: crawlable, indexable pages, real HTML headings instead of styled divs, structured data where it fits, content that doesn't need JavaScript to appear, and page speed to the extent it affects whether a crawler gets through your site.
Two things need a date attached, because this area moves and much published advice hasn't kept up. As of Google's guidance last updated 10 July 2026, structured data isn't required for its generative AI features and there's no special markup for them, though Google still recommends it for rich results. Implement it, but it isn't the lever.
The same document is blunter about llms.txt: Google Search doesn't use it, and maintaining one neither helps nor hurts you there. Other systems may read it, which is a fair argument for having one, but it isn't a Google play. Implementing llms.txt covers the trade-off.
On crawler access, check each engine's own documentation before writing robots.txt rules. Bot names and the split between training and retrieval crawlers have both changed inside the last year, and second-hand summaries go stale fast. Go to the source, and note the date you checked.
Phase 5, Measure, Then Change One Thing at a Time
The objective of phase 5 is to read direction reliably enough to make the next decision. Re-run the phase 1 prompt set monthly, same prompts, fresh sessions. Resist the urge to improve it. A changed instrument makes the trend unreadable, and a quarter is wasted.
A realistic timeline: entity corrections have surfaced within about three to five weeks in our engagements, while content-driven citation gains take far longer. Nothing should be judged on one month.
Be honest about attribution. Referral data from AI surfaces is patchy, and Pew Research Center's July 2025 analysis of March 2025 browsing data found people clicked a source cited inside an AI summary in just 1 percent of visits, and clicked any search result in 8 percent of visits with a summary against 15 percent without. Wait for traffic to prove the point and you'll conclude the work failed, when the click simply never existed. Track mentions directly. Google has since added a generative AI performance report in Search Console, which helps on its own surfaces and nowhere else.
Tooling is a separate conversation. Prompt trackers, brand monitors and rank trackers with AI modules all exist, and we won't compare them here. The method in our AI visibility measurement guide runs on a spreadsheet.
A 90-Day AEO Strategy You Can Actually Run
If you need something for a slide, this is the shape of it.
| Month | Focus | What gets done | What you should expect to see |
|---|---|---|---|
| Month 1 | Baseline and entity audit | Build and run the prompt set. Audit name, location and service naming everywhere you appear. | Nothing. This month produces a document, not a result. |
| Month 2 | Entity fixes and first content | Correct every inconsistency. Publish the first answer-first pages against your highest-intent questions. | Better accuracy in answers. Presence largely unchanged. |
| Month 3 | Technical work and second measurement | Developer ships crawlability and structured data. Re-run the identical prompt set and compare. | A readable direction, not a verdict. |
Say the quiet part out loud: visible movement in month one isn't the goal, and a strategy promising it should worry you. Month one buys the ability to prove something later, which is what most of this work lacks. If a proposal skips to tactics, ask what it'll be measured against.
This is roughly the sequence we run inside our answer engine optimisation service, and it's runnable in-house by one organised person with a spreadsheet.
Where AEO Strategy Overlaps With SEO (and Where It Does Not)
Plenty of this is ordinary hygiene with a new label, and pretending otherwise insults anyone who has watched this industry rename things for a decade. Technical accessibility, content quality and entity consistency are all things good SEO already asked for. Google's position, in its July 2026 documentation, is that optimising for generative AI search is just optimising for search.
Three things really are different, though:
- The unit of success is a citation, not a position. There's no number ten to climb from.
- Measurement is prompt-based and probabilistic. You're sampling a distribution, not reading a rank.
- The same question returns different answers on different runs. Nothing in classical SEO behaves like this.
Arrived from the generative side? GEO and SEO compared, what generative engine optimisation is and the AEO definition cover this from other angles.
Common Ways an AEO Strategy Fails
- No baseline. You can't demonstrate a change you never measured the start of. This is the one that ends budgets.
- Tactics before entity. Good content attached to a brand the engine can't identify works for somebody else.
- One engine only. Usually ChatGPT, because it's the one the boss uses. The others differ enough to matter.
- Judging on a single run. Variance will happily tell you whichever story you were hoping for.
- Changing the prompt set between measurements. Understandable, tempting, and it destroys the trend.
- Treating it as a project with an end date. The engines keep changing. Phase 5 has no finish line.
Frequently Asked Questions
What is an AEO strategy?
An AEO strategy is a sequenced plan for getting your organisation named and cited by answer engines such as ChatGPT, Perplexity, Gemini and Google AI Overviews. It runs in five phases: baseline measurement, entity clarity, answer-first content, technical accessibility, and ongoing tracking. The order does the work.
What should you do first in an AEO strategy?
Build a baseline. Write 20 to 30 real buyer questions, run each several times in fresh sessions across all four engines, and log presence, share of voice, accuracy and citation. Do this before changing a single page, or you'll have nothing to compare against.
How long does AEO take to work?
Entity corrections can surface within weeks. Content-driven citation gains take longer, and a first honest read on direction lands around month three. Anyone promising visible movement in month one is either measuring something trivial or not measuring at all.
Is AEO different from SEO?
Partly. Technical hygiene, content quality and entity consistency carry straight over, and Google's July 2026 guidance treats optimising for AI search as ordinary search work. What's new is that success is a citation rather than a position, measurement is probabilistic, and the same question returns different answers on different runs.
Do you need special tools to run an AEO strategy?
No. A spreadsheet and the discipline to re-run the same prompts monthly gets you through all five phases. Prompt trackers save time once the set grows, but buying one before you've built a baseline is a more expensive way to have no comparison point.
Five phases: baseline, entity, content, technical, measurement. Run them in that order, because each makes the next legible, and because the first is the only thing that lets you prove any of it later. The category is young enough that an ordered process is itself an advantage: most competitors are running tactics without a baseline, and they'll still be arguing about whether it worked long after you've stopped having to. For the market picture, read the 2026 Canadian AI Search Visibility Benchmark. Weighing in-house against hiring? Our rundown of Vancouver AI SEO agencies helps. Wider programmes sit under our SEO and AI visibility service, with generative engine optimisation and AI search optimisation.
Parabolic Studio builds and runs AEO strategies for British Columbia brands across ChatGPT, Perplexity, Gemini and Google AI Overviews. If you'd rather not spend month one building a prompt set by hand, we'll build it with you.
