You publish content and close deals, yet you cannot tell whether ChatGPT, Perplexity, Gemini or Google AI Overviews describe your brand accurately or cite your pages. An AEO strategy closes that gap by naming the buyer questions you intend to be the answer for, the changes you will make, and how you will know.
This article is for CEOs, founders and revenue leaders at B2B companies with the basics of AI search optimization in place. You will get a loop to improve AI search visibility, a way to rank your backlog and an honest view of what a test can prove. See our guide to AI Engine Optimization (AEO).
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AEO Strategy An AEO strategy is a repeatable system for choosing which buyer questions to win in AI generated answers, testing changes, measuring AI visibility and connecting results to pipeline. |
An AEO strategy is not a content calendar with an AI label on it. It states which buyer questions matter to your pipeline, what you will change across content and signals to earn mentions and citations, and how you will track whether AI engines describe you accurately.
AEO is the practice of optimizing content for AI-powered search engines. Propello calls it AI Engine Optimization, and answer engine optimization is the same idea under its wider market name.
GEO, short for generative engine optimization, is a close cousin aimed at generative search. All three share one aim: being the answer, not just one of the blue links.
Traditional SEO aims for higher click-through rates on search results. AEO aims to be quoted as the best answer, and AI search uses multiple sources to build that answer where traditional search engines list ranked pages.
AEO should complement traditional SEO, not replace it, because both reward accurate facts, clear structure and a site engines can reach.
Three facts make a fixed playbook a weak foundation. The AI models behind answer engines change often, and the detail of how they pick and cite sources is not published. Most public advice about what they reward is correlation drawn from small samples. And the answers themselves are unstable.
That last point has evidence behind it. In a 2025 SparkToro study, 600 volunteers ran 12 prompts through ChatGPT, Claude and Google's AI features a combined 2,961 times in November and December.
The study found less than a 1 in 100 chance that ChatGPT or Google's AI would give the same list of brands twice.
The same study suggests that how often a brand appears across many runs is probably a more useful signal than its position in any one answer. So treat every tactic as a hypothesis, not a rule, and read results across many runs. Dropping the old playbook and testing instead is safer for these reasons:
A mature program does not scramble after every rumor. It rests on structural, technical and authority components working together, not on one trick.
Its marks are a clear list of buyer questions tied to pipeline stages, a stable prompt set with a logged baseline, small experiments with untouched control pages, and one shared log of hypotheses and decisions.
The loop is an operating rhythm, not a campaign with an end date. Each cycle shows you what moves how AI platforms describe and cite your brand, and what does not.
Start from revenue, not curiosity. Which questions, if you were the default answer, would change how opportunities form and close? Listen for what people talk about on sales calls and in win/loss reviews, then group those questions by user intent: problems, questions where buyers compare options, vendor comparisons, and risk or proof.
Only the questions that matter commercially deserve a place on the list. Drop vanity prompts that never appear in real buyer conversations.
Your prompt set stands in for real buyer behavior. Keep it modest: mostly non-branded conversational queries, plus some branded and comparison prompts that mirror the way buyers ask.
Prompts to AI search tools are longer, more conversational and more question-based than the short queries behind traditional SEO, so write yours in the buyer's own conversational language and match the search intent behind them.
For each prompt on each engine you track, record whether your brand appears, whether your domain is cited, how the answer describes you, and which competitors show up. Note any clear errors too, and treat each answer as one of many possible AI generated responses.
Run each prompt more than once, because one run misleads. Google says its AI features may use a "query fan-out" technique, issuing multiple related searches across subtopics, so one question can draw on several sources.
Do not change or expand the prompt set during a test window. If you move it, you cannot tell whether a shift came from your changes or from different questions.
A good hypothesis names one change, one directional outcome and one time window. Stacking changes destroys what you learn, because you cannot say which one caused a shift. Frame the expected outcome around mentions and citations, not rankings.
Examples worth testing:
Your treatment group is the limited set of pages, profiles or signals tied to the chosen prompts. Your control group is a set of similar pages you leave alone, so you can separate your change from everything else that moved.
Start with existing content before you write anything new. Put a plain, direct answer near the top, use clear headings, and keep the facts accurate and the references trustworthy.
Plain wording is simpler to process, so these edits make a page easier for AI systems to read and quote, and they help buyers who read the page too. Write each change down before you publish it.
Pick a window before you start. Engines need time to crawl and reflect changes, and measuring too early produces noise. When the window closes, rerun the same prompts on the same engines and record results in the same format as the baseline.
Compare treatment against control. Look for consistent shifts across prompts and engines, not one flattering screenshot.
End every cycle with one of three decisions. Adopt the change more widely, adapt it and retest, or drop it. Log the hypothesis, the pages touched, the dates, the result and the decision, so the next person can see why. That log is how AEO efforts compound.
Be honest about the limits, or the loop will mislead you. Samples are small. Answers vary by user, location and time. Any effect you see is directional, and a change in one run can be nothing more than variation.
A result is more believable when it repeats across prompts, appears on more than one engine, and shows up again when you rerun the test. Treat a single win as a reason to look again, not as proof.
Review AI visibility beside demand generation and sales metrics, in the same cadence as revenue. A separate meeting makes it drift away from pipeline, and each review should end in a decision.
Ideas will always exceed capacity, so score them. Use three factors.
Rate each factor low, medium or high in a simple sheet or a HubSpot custom object. Start with high value, big gap and low effort.
Content edits alone will not move AI visibility if your facts are inconsistent, your site is hard to crawl, or nobody outside your site says anything about you. The strategy needs six parts.
Engines need to work out who you are. If your name, category, location, offerings and proof points differ across your site, directory profiles and bios, AI systems may misdescribe you or skip you.
Building a consistent entity footprint helps AI understand your credibility. See knowledge graph SEO for the steps that make a company a clear entity in the first place. Keep one source of truth for these facts, in HubSpot or a shared document, and update every profile from it.
Google's documentation says it uses structured data to understand a page's content, and Google also says there is no special schema.org markup needed to appear in AI Overviews or AI Mode.
Use accurate schema markup for articles, organizations and FAQs because it removes ambiguity, and test it like any other change. Keeping that markup current is covered in organization schema markup maintenance for teams that update often. Do not expect it to guarantee a citation.
Crawler settings decide whether an engine can reach your pages at all, and the major providers publish separate controls. OpenAI says sites opted out of OAI-SearchBot will not be shown in ChatGPT search answers, and that GPTBot, which relates to training, is a separate setting.
Perplexity says to allow PerplexityBot if you want to appear in its results. Check your robots file and make each choice deliberately.
Much of what an engine says about a company comes from places other than your own website. AI models review third party mentions when they judge a brand, so third party validation is a trust signal you can only earn off your own pages.
Review sites, expert commentary, media coverage and Reddit discussions all shape your brand reputation.
Digital PR and reputation management therefore belong in the strategy. Coordinate with PR and customer marketing so tests include these authority signals.
Review key pages on a schedule too, because stale facts weaken the trust signals engines can see. Some AI systems may also weigh how people interact with content, which you cannot observe, so write for buyers first.
Pair the prompt log with your normal reporting, and track AI visibility by citation rate and the cited URLs behind it. The full measurement setup is in AI search analytics, including how to read the data.
Google says sites appearing in its AI features are included in overall search traffic in Search Console, so Search Console and your organic traffic reports are one place to look.
Add lead source, conversation notes and deal records in HubSpot so you can show measurable impact on pipeline and see where your content appears in answers.
Without an owner and a log, AEO becomes folklore, and most teams skip the log. Pick a senior marketing or RevOps leader who can change messaging, measurement and priorities across teams, and who reports progress next to pipeline.
Most wasted effort comes from poor testing discipline, not bad ideas.
A disciplined loop replaces rumor with evidence, and the gains reach every team that touches a buyer. None of it is guaranteed, but each piece is something you can check.
When answers describe your product and proof points accurately, prospects arrive with better-shaped expectations. Discovery calls spend less time correcting misconceptions.
The loop shows which topics and prompts connect to pipeline, so effort goes where it counts. You also get a live view of how the market describes your category in AI summaries and AI search tools, which sharpens your messaging for every channel.
When AI answers reflect your real strengths and limits, buyers start with a more accurate picture, which gives onboarding a better footing. Customer success can feed recurring post-purchase questions into the prompt set, so the work covers the questions that matter after the sale.
AEO compounds when it joins GTM Engineering, RevOps and leadership decisions. On its own it is visibility work. Connected, it shapes how buyers find you, how sales qualifies them and how customer success keeps them.
HubSpot is where you can connect prompts, experiments, contacts, deals and content in one place.
Propello designs and builds connected GTM systems on HubSpot. If you want a test-and-learn loop that connects AI visibility to pipeline, an audit is the place to start.