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It seems like every brand is scrambling to get a piece of the pie in this new answer engine optimization (AEO) world. But what if you could get ahead of the curve by knowing the best on-page content formats for AI as verified by research? I pored over results from the new HubSpot State of AEO 2026 report and Wix Studio’s AI Search Lab research on most-cited content types to find out.
There’s a widening gap between what the market says about AI and what we actually hear from customers. The media, the VCs, the AI labs, and influencers have all talked about AI replacing humans, ripping out trusted software, and token-maxxing as ends worth pursuing. But the leaders running real businesses are increasingly asking the right questions. How do I make my people better with AI? Which systems can I trust? How can I measure the ROI of this spend? We hear these questions every day.
The AEO benefits that matter most to marketing leaders have shifted from theoretical to measurable. As more buyers discover brands through AI tools like ChatGPT, Google AI Overviews, and Perplexity, the teams investing in AEO now are seeing real returns in conversions and long-term authority.
AI search behavior may be causing a dip in your traffic, but it’s also sending higher-quality leads your way. For marketers, that second part is a massive win. AI search was the number one predictor of purchase intent for CRM software buyers, according to HubSpot’s State of AEO 2026 report. And there are more findings from the report that every go-to-market team needs to know.
For years, the SEO playbook was straightforward: earn backlinks, climb rankings, capture clicks. But as AI reshapes how traditional SEO works, a different mechanism is determining which content gets seen — and it’s not backlinks. It’s citations. The role of citations in AEO is fundamentally different from link-building: instead of other publishers vouching for your page, AI answer engines are selecting your content as the direct source behind their generated answers.
A few weeks ago, I wrote about our vision for the agent era: agents should be able to run on HubSpot, and to run HubSpot. I want to go a level deeper on what “run HubSpot” actually means, and our latest step in bringing this vision to life.
Walk down a suburban street, and you might stumble across a following sign. It’s probably messy with poor formatting and inconsistent font size. Here’s one that I saw in Houston.
If you’re worried about what AI Overviews mean for SEO, let me remind you of the panic over featured snippets circa 2017. Remember how that turned out? At first, bloggers and SEOs bristled over these quick-glance summaries at the top of the Google SERPs, fearing they’d steal all our traffic. Eventually, however, we adapted and started optimizing content to get mentioned in them. I believe the same will be true of AI Overviews. I mean, it’s already happening: The internet is now filled with the latest advice on how to get cited in AI Overviews (including this article).
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Google AI Overviews appear in Google Search results for a growing share of queries, and if your content isn’t structured to earn a citation, you’re losing visibility to competitors who’ve already adapted. Unfortunately, the challenge isn’t awareness. Most SEO leaders know AI Overviews exist. The challenge is execution: translating Google’s deliberately vague guidance into repeatable content workflows, measuring whether your AI website optimizations are actually earning citations, and proving business impact when traditional metrics like rank position and CTR no longer tell the full story. This playbook closes that gap.
Growing up, the only “top 10” I cared about was MTV’s Total Request Live (TRL). When I started working, that became the top 10 results in the Google SERP. Now, my eyes are set even higher as we marketers explore how to rank in AI Overviews.
I’ve spent the last year watching marketing teams scramble to understand why their organic traffic reports tell one story while their pipeline tells another. The missing link is almost always a need for AI search analytics tools. 
The brand tracking dashboard says awareness is up. Social listening tools show steady mention volume. The PR platform logged a dozen media hits last quarter. But, none of those tools show how a brand shows up when a buyer asks ChatGPT, Perplexity, or Gemini for a recommendation.
Brand mentions aren’t a new concept, but answer engine optimization (AEO) is giving them a different weight. Brand mentions are any online reference to your brand, product, spokesperson, or company name; right now, they’re happening in more places than most teams can track.
You’ve seen it with your own eyes, reader. The way buyers discover brands is changing faster than most marketing teams realize.
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Digital marketing optimization plays a major role in whether a marketing program grows or remains stagnant. Most teams are running campaigns, tracking metrics, and still scratching their heads, wondering why the pipeline isn’t moving. Honestly? The problem usually comes down to process, not effort.
In 2007, Coulter and Coulter showed two advertisements to two random groups of customers. Each advertised £10 discounts on flights to Turkey. One listed the tickets at £188. The other showed a higher price: £233. 
Brand visibility determines whether your business gets found or gets passed over — in search results, on social feeds, and increasingly, in AI-generated answers. It’s one of the highest-leverage investments a marketing team can make, and also one of the most commonly mismanaged.
Product SEO is one of the highest-leveraged — and most overlooked — strategies in B2B and SaaS marketing. While most teams pour resources into top-of-funnel content, the pages that actually drive pipeline decisions, such as feature pages, comparison pages, and pricing pages, often go unoptimized and underperform.
For years, HubSpot invested in making our platform the best place for marketing, sales, and service teams to do their work. With AI, we’ve been building it to do the work for them – through agents that qualify leads, resolve tickets, save deals, and drive outcomes across the business. That’s why we call HubSpot an agentic customer platform.
As a content writer with over 7 years of SEO experience, I can confidently say that keyword clustering is a critical technique—even in a world where the SEO landscape has changed significantly.
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You already track and analyze your SEO strategy — keyword rankings, organic traffic, SERP positions. But when a prospect asks ChatGPT, Perplexity, or Google AI Overviews a buying question and your brand doesn’t appear in the answer, traditional rank tracking can’t tell you that. AEO prompt tracking helps you measure brand visibility within AI-generated answers by monitoring whether (and how) your brand gets cited when real AI prompts are run across the engines your audience is actually using. For marketing leaders, SEO managers, and demand gen teams, it’s the measurement layer that closes the gap between “we publish great content” and “we can prove AI search drives pipeline.”
Every company’s competitors are showing up in AI-generated answers, but do marketers know which ones, for which queries, and why? That’s exactly what AEO competitor analysis is designed to tell teams.
When tracking share of voice for marketing teams, it’s often assumed to be a vanity metric — a number executives like to include in board decks but one that rarely influences strategy. In practice, that assumption doesn’t hold up.
AI search engine citation tracking helps measure brand visibility and authority in AI-powered search results. As AI-powered search experiences reshape how people discover information, evaluate vendors, and build shortlists, visibility inside AI answers is no longer a vanity metric. If AI engines aren’t citing your brand, you’re missing influence at the exact moment buyers are forming opinions.
Generative AI is changing how people discover brands, products, and information. Because it disrupts the buyer journey, it requires new metrics, specifically GEO KPIs, that accurately reflect performance within these AI engines.
Answer engine optimization (AEO) is a marketing strategy designed to help brands appear more consistently and accurately within AI-driven answer engines such as ChatGPT, Perplexity, and Copilot.
Research shows that 32% of buyers discover new B2B vendors using generative AI chatbots. This is why an answer engine optimization (AEO) strategy for B2B businesses is essential. AI-driven answer engines help buyers discover, evaluate, and shortlist vendors. The same research found that buyers start with an average of 7.6 potential vendors and narrow this to 3.5 before making their final decision.
GEO has found its place in the search landscape, and it’s reasonable to think that the future of generative engine optimization is guaranteed. According to Datos’s State of Search report, Q4-2025 saw some interesting changes. For the first time, AI tools had a consistent 1.31% to 1.34% of visits in the U.S. In previous quarters and reports, traffic to AI tools was growing. This stability in traffic suggests that AI search tools may have found their place in the wider search landscape. ![Free AEO Grader: See Your Brand's Visibility in Answer Engines [Free Tool]](https://no-cache.hubspot.com/cta/default/53/d4233c10-60b6-46d7-9852-c71dde8507b6.png)
While many are still skeptical, the global creator economy is expected to reach $1.18 trillion USD by 2032. And for minority creators and entrepreneurs from underrepresented groups, this moment is especially significant.
Every company I talk to right now is convinced they have an AI problem.
An AEO strategy for SaaS won’t stray too far away from a good SEO strategy, but some tactics benefit AI search more than others, and it helps to know what these are. We all know that AI has shifted how brands earn visibility, and how visibility doesn’t equal clicks. But for SaaS, the way buyers conduct discovery and evaluation has changed disproportionately.
Two-thirds of marketers say that marketing has changed more in the past three years than in the past 50. Understanding Loop Marketing versus traditional marketing has become essential for marketers in 2026. The two frameworks differ fundamentally in how brands reach, engage, and retain customers in an AI-driven world. 
A marketing forecast estimates future marketing results, such as leads, pipeline, and revenue, using historical data and conversion assumptions. Marketing forecasting connects planned activity to expected outcomes, helping teams understand what performance is likely to look like before campaigns are executed. This approach supports clearer planning, more predictable growth, and stronger alignment between marketing inputs and revenue targets.
Maybe you’ve opened ChatGPT a handful of times, gotten subpar results, and moved on. Maybe you’ve sat through an AI training or two and thought, “Cool, but how does this actually apply to my job?” Or maybe you’ve bookmarked a dozen AI tools you saw recommended on LinkedIn and haven’t tried a single one.
Search results used to be a doorway. You ranked, someone clicked, and they landed on your site. But today, that model is eroding faster than most marketing teams are equipped to move.
AI-referred traffic has increased by 600% since January 2025, and marketers are racing to understand what that means for brand discovery. For teams seeking clarity on how AI impacts brand and pipeline means investing in new tools like Profound or Athena AI for Answer Engine Optimization (AEO).