AI search is changing how buyers discover businesses, research solutions and decide which companies are worth considering.
That does not mean traditional search is disappearing. It means marketers now have another discovery surface to understand.
HubSpot is responding with AEO tools designed to help marketers see how their brands appear in AI-generated answers, understand which sources are influencing those answers and identify opportunities to improve their visibility. HubSpot currently tracks AI visibility across ChatGPT, Gemini and Perplexity.
For Primo Coding, the important takeaway is not that AEO replaces SEO.
It doesn't.
AEO extends the same core discipline: publish useful, well-structured, trustworthy content that clearly answers the questions your buyers are asking and makes your expertise easy to understand.
Answer Engine Optimization, or AEO, is the practice of improving how a brand and its content are understood, referenced and surfaced by AI-powered answer engines.
Traditional SEO has generally focused on helping content get discovered and ranked in search engines, with performance measured through signals such as rankings, impressions, clicks and organic traffic. AEO introduces additional questions:
HubSpot's AEO tools are designed to help marketers monitor those signals over time rather than treating AI visibility as something they can only guess about.
One of the more interesting parts of HubSpot's approach is the connection between measurement, customer context and execution.
HubSpot AEO includes visibility tracking, prompt tracking, citation analysis, competitor analysis and recommendations. For Marketing Hub Professional and Enterprise customers, HubSpot can also use CRM data to help suggest prompts relevant to the company's actual business and buyers.
That creates a potentially useful loop:
Understand what buyers are asking → see where your brand appears → identify gaps → improve the content → measure what changes.
And because AEO sits alongside HubSpot's marketing and content tools, teams can move from identifying an opportunity to creating or updating content without building an entirely separate process around AI search.
That's where this becomes more interesting to us than simply adding another dashboard.
We would not recommend creating one set of pages "for Google" and another set "for AI."
A better approach is to create strong content for humans first, then structure it clearly enough that both traditional search engines and AI systems can understand what the page is about, who it is for and what questions it answers.
Many of the fundamentals overlap with good SEO and information architecture:
Clear page topics. Descriptive headings. Direct answers. Useful definitions. Strong internal linking. Credible authorship. Specific examples. Clean technical foundations.
That's the same approach we're taking with Primo Coding's HubSpot website and CMS work.
AEO adds another layer to the strategy, but it doesn't erase the fundamentals.
Do not make someone work through a long introduction before the page explains what the topic actually is.
Give the direct answer first. Then add context, examples and nuance.
That makes the content more useful for people and creates clearer passages for search and answer systems to interpret.
A heading such as "What Is HubSpot Work?" communicates substantially more than something vague like "The Future Is Here."
Creative language can still have a place, but important sections should make their purpose obvious.
Show who wrote the content and why that person's experience is relevant.
A real author bio, relevant experience, first-hand observations and specific examples provide more context than a name floating under a headline with no explanation.
Generic content is easy to produce, especially now.
Specificity is harder.
Actual processes, constraints, implementation considerations, examples and lessons learned make content more useful to the reader and give AI systems more meaningful information to work with.
One article does not need to answer every possible question.
Use internal links to connect definitions to deeper guides, related articles, implementation resources and relevant services.
That helps readers continue exploring while creating a clearer relationship between topics across the site.
Videos, graphics, interactive tools and downloadable resources can all improve a page.
But don't hide the only meaningful explanation inside them.
Important answers should still exist as readable text on the page.
This may be one of the biggest changes for marketing teams.
Traditional SEO reporting has centered heavily on rankings, impressions, clicks and organic traffic.
AI discovery introduces different signals.
HubSpot AEO tracks things such as brand visibility, prompt performance, citations, sentiment and competitor share of voice across supported answer engines. Citation analysis can also show which domains and pages are being referenced in AI answers.
That doesn't mean traditional SEO metrics stop mattering.
It means marketers may need a broader definition of visibility.
Someone can learn about your company, see it recommended and form an opinion about it through an AI answer before ever visiting your website.
If you've followed our other Fall Spotlight coverage, you've probably noticed one word appearing repeatedly:
Context.
It matters here too.
Tracking hundreds of generic prompts isn't necessarily useful if those prompts have little relationship to your actual buyers.
HubSpot's approach is interesting because Marketing Hub can use CRM data to help recommend prompts based on business context.
The goal shouldn't be:
How do we show up in AI?
It should be closer to:
Are we showing up when the people we actually want to reach are asking questions related to problems we solve?
That's a much more useful question.
AEO is new enough that it's tempting to overcorrect.
We wouldn't recommend rewriting every page into robotic question-and-answer copy, stuffing pages with dozens of low-value FAQs or creating large volumes of generic AI-generated content simply to cover more prompts.
We also wouldn't treat schema markup as a substitute for useful content or assume that visibility in one AI platform guarantees visibility everywhere.
And we definitely wouldn't abandon technical SEO, indexability, canonicalization, site architecture or the other fundamentals that help search systems discover and understand a website.
The technology is changing.
The need to create something genuinely useful isn't.
AI search gives businesses another reason to stop publishing generic content.
If a page says essentially the same thing as 100 other pages, has no visible expertise and doesn't answer a specific buyer question particularly well, changing the acronym from SEO to AEO isn't going to fix it.
What is changing is our ability to measure another part of the buyer journey.
We can begin asking whether a brand appears in AI answers, which sources influence those answers, where competitors are appearing instead and what content gaps may be contributing to the difference.
That's useful.
But the response shouldn't be to chase every new AI tactic.
Build useful content. Make the expertise clear. Structure it well. Keep the technical foundation clean. Measure what happens. Then improve.
That's still a pretty good strategy, whether the buyer starts with Google, ChatGPT, Gemini, Perplexity or whatever comes next.
Explore our Fall Spotlight 2026 analysis to see how HubSpot's AI search updates fit alongside Growth Context, Breeze, Agent Builder, Marketing Studio and the rest of the platform changes.
Or, if you're trying to figure out what AI search means for your own website and content strategy, talk to Primo Coding.