What it means for your marketing strategy in 2026

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.

In this article, I’ll share the latest findings on AI search behavior, its impact on brand discovery, an answer engine optimization (AEO) strategy you can implement today, and much more.

Table of Contents

In the past, traditional search consisted of a user entering keywords into a search engine like Google, getting a list of blue links, and clicking them to find their answer. But search behaviors are changing. Today, users are increasingly turning to AI with conversational queries (usually a few sentences long) and reading AI-generated summaries that instantly fulfill their search. AI search behavior differs from traditional search behavior in that it becomes a multi-turn Q&A — an entire conversation in one chat, not just a click to a single webpage.

Marketers should care about AI search behavior because it’s a growing part of search. SEO still determines which pages rank in the underlying search index, but answer engine optimization (AEO) determines which sources AI tools cite when composing summaries. Both have to be optimized in parallel, and increasingly, AEO is what influences whether buyers ever see your site listed in the first place.

Yes, AI search behavior decreases organic traffic, but the good news is that the traffic that comes from AI is higher intent. HubSpot saw 3x better conversion from AI-sourced leads versus other channels in 2025. Referral traffic from tools like ChatGPT and Gemini has also tripled, according to Search Engine Land.

AI-referred traffic converts better because summary-first experiences resolve the easy questions inside the answer engine itself. A reader asking “what is AEO?” doesn’t need to click a single result; they get a definition, sometimes a list of vendors, and move on. But a reader who clicks after reading an AI answer to their query, “how can a B2B marketing team of five implement AEO on their blog,” has usually progressed past that surface layer. They’ve validated their problem, seen who got cited, and want to verify, compare, or convert.

That shift in funnel shape changes how you measure success. Clicks become a smaller, later signal in a journey that now happens partly inside the answer engine. The metrics that capture the rest of it look different: how often your brand surfaces in the summary, which competitors you appear alongside, and which prompts route the highest-intent traffic to your site.

Brands that previously ranked #1 for a category term are competing for a smaller slice of visible real estate, and the AI Overview itself decides which sources to cite. About 60% of Google searches now end without a click, according to SparkToro. In my opinion, that number is likely to keep climbing as more queries trigger AI-generated answers.

Branded search has held up. Buyers who already know your name still type it and land on your site. Category-term discovery is where AI search has hit hardest: Google serves AI Overviews for non-branded queries 1.9x more often than for branded ones, according to Ahrefs. A query like “what is the best software for video editing” no longer returns just a list of blue links to evaluate. It returns one or two brands recommended by AI in a highly personalized output, sometimes with a comparison table, and the buyer often acts on that answer.

HubSpot’s State of AEO 2026 found that 42% of CRM software buyers used AI search to evaluate vendors. Across the full set of evaluation activities tracked in the report, AI search ranked as the strongest predictor of purchase intent for CRM buyers. When an answer engine names your competitor in that recommendation, the deal is often decided before your sales team knows the buyer exists.

Entity clarity, topical authority, and reputation signals now determine which brands answer engines surface. Each plays a distinct role:

• Entity clarity establishes whether an answer engine recognizes your brand as a distinct, well-defined option. Without it, answer engines may struggle to associate your brand with the right category, use case, or comparison set.
• Topical authority reflects the depth and consistency of coverage across a category. It influences which category questions, comparisons, and use cases your brand is eligible to be cited for.
• Reputation signals, such as third-party mentions, reviews, comparison pages, news coverage, and Reddit threads, tell answer engines that you’re an entity they can trust.

In the old model, signals like links, keywords, and authority won blue-link visibility, and reputation grew from there over time. Those signals still matter, but in AI search, they get evaluated by an answer engine before a prospect ever reaches your site. By the time someone clicks through, they’ve usually weighed several options inside an AI answer — including, hopefully, you.

Content planning for AI search behavior starts with prompts instead of keywords, requiring a different approach to content marketing strategy. A buyer using AI rarely asks one isolated query. They start with one, then ask a follow-up, then a clarifier, then a comparison question. To earn citations across that whole multi-turn exchange, your content has to anticipate the sequence and be more comprehensive.

Brainstorm the questions your buyers are asking AI.

Question mapping starts with a seed query and traces the follow-ups. Pick a question your category gets asked early in the funnel (“what is AEO?”), then write out the next five questions a buyer would logically ask (“how is AEO different from SEO?”, “do I need an AEO tool?”, “which AEO tools do marketers actually use?”, “how much does AEO software cost?”, “what’s the ROI of AEO?”). That sequence is what your content needs to answer collectively.

HubSpot’s topic cluster model organizes the question set into a pillar page and supporting cluster pages: one pillar for the broad seed question, cluster pages for each follow-up. That structure gives answer engines a clear entity to cite for the broad query and a clear trail of supporting pages for the long-tail follow-ups.

Source: Matt Barby

HubSpot’s Content Hub helps marketing teams organize topic clusters and manage pillar pages right within its CMS.

Pro tip: Run your seed question through ChatGPT and Perplexity yourself, then track which sources they cite for each follow-up. Those brands are who you’re competing against inside the answer engine, and the citation patterns tell you what kind of content earns a mention at each step.

Restructure existing content into extractable answers.

A content audit reveals which pages already earn citations and which need work. Re-run your top 20 or so organic landing pages’ target queries through ChatGPT, Gemini, and Perplexity. Cited pages are working. Absent ones are restructure candidates.

Here are some strategies to apply to your existing content to make it more AEO-friendly:

• Put the answer upfront. The “lost in the middle” Stanford research maps a U-shaped extraction curve: Answer engines pull most reliably from the opening and closing of a passage, not the middle. If the direct response to the target query sits four paragraphs in, cut the context-setting ahead of it and lift the answer into the first sentence of the lead.
• Write self-contained paragraphs. Answer engines retrieve passages, not pages, so each paragraph has to make sense as a standalone chunk. Pronoun-led openers (“This is why…”) or paragraphs that braid two ideas together land in retrieval as broken context. Rewrite each one to lead with its own named subject and cover one idea. As AEO/SEO expert and founder of iPullRank Mike King puts it, “A passage that focuses on one idea will, in nearly every measurable case, retrieve better than a passage that tries to cover three.”
• Make content skimmable with tables and bullet points. Comma-separated lists embedded in prose (“the benefits include speed, accuracy, and cost”) should be bulleted lists; embedded numeric comparisons should be tables. In Yu et al.‘s March 2026 preprint, lists and tables had 43% better extraction accuracy across six engines than the prose versions they replaced.

See how to write for AI search for more.

But traditional analytics tools like Google Analytics weren’t built to count brand mentions or share of voice. To do that, you can manually check within AI answer engines or get a specialized tool like HubSpot AEO to automate AI visibility tracking.

How to Audit Your AI Search Visibility

A baseline audit starts by running your 10 highest-priority prompts through ChatGPT, Gemini, and Perplexity (make sure you’re logged out in each instance or using a temporary chat). Record which sources get cited, whether your brand appears, and which competitors are pulling ahead across your most important topic clusters, branded queries, and category-level questions. Use this baseline to identify gaps between where you and your competitors sit and create a roadmap to optimize content for better AI visibility.

How to Track AI Search Visibility Over Time

AEO Grader is a free tool that gives you a quick snapshot of where your brand stands across ChatGPT, Perplexity, and Gemini, including a share of voice score.

HubSpot AEO monitors your brand visibility across answer engines over time, analyzes how competitors appear in your tracked prompts, and prioritizes recommendations to lift your citation rate. It’s the continuous-tracking layer once your baseline is set.

For example, when OpenAI rolled out GPT-5 in August 2025, the update marked a substantial improvement in how ChatGPT answers health-related questions. As OpenAI wrote in its announcement of GPT-5, regarding health: “The model also now provides more precise and reliable responses, adapting to the user’s context, knowledge level, and geography, enabling it to provide safer and more helpful responses in a wide range of scenarios.”

To keep up with the changes and ensure your content is still optimized for the newest models, you can track release notes from OpenAI, Anthropic, Google, and Perplexity.

I also recommend a consistent review cadence:

• Monthly: Re-run your core prompt set across ChatGPT, Gemini, and Perplexity. Compare citation and brand mention counts against your baseline. Flag any prompt where your presence shifted noticeably in either direction.
• Quarterly: Audit the pages that lost citation share. Check whether the content format, schema, or entity definitions still align with how each platform is currently structuring answers.
• On major model announcements: Run an immediate re-test on your five highest-priority prompts. OpenAI, Google, and Perplexity all publish release notes — a public model update is a signal to audit before you see the impact in your tracking data.

Pro tip: HubSpot AEO tracks brand visibility across answer engines over time, making it way less burdensome to monitor AEO efforts.

Between review cycles, here are the four content-side elements that are most worth maintaining:

• Entities: Confirm your brand, product names, and key people are defined consistently across your site, about page, and third-party profiles like LinkedIn, Crunchbase, and G2. Inconsistent naming can confuse an answer engine.
• Schema: Verify that relevant schema markup, such as Article, FAQPage, and Organization, is present and error-free using Google’s Rich Results Test and Schema.org’s validator.
• Internal links: Check that pillar pages and cluster pages are still pointing to each other and that no links have broken due to URL changes or content migrations.
• Answer summaries: Re-read the lead paragraph of each high-priority page. AI models may extract more reliably from the beginning and end of a long context, per the “lost in the middle” research, so a lead that no longer opens with a direct answer to the page’s target query is a fast fix.

But sales reps need tools to understand what AI is saying about their brand. AEO in Marketing Hub surfaces prompts and citations that are shaping these conversations, making those signals visible to sales and marketing teams.

How AI Search Behavior Changes Service Content

Service content is great answer-engine source material. Knowledge base articles and help center documentation feed the same answer engines buyers consult during evaluation. A well-structured support article on “how do I export X from your tool” is exactly the kind of extractable, question-format content models prefer to cite. Service teams optimizing their docs for clarity are also, by extension, optimizing for AI visibility.

Here’s a real-life example: I asked ChatGPT, “Can I export my website from Wix?” (a common buyer evaluation question), and its answer cites a Wix help center article.

How Sales and Service Teams Inform AEO Content

Feedback loops between sales, service, and marketing turn buyer language into answer-engine source content. Sales and service teams hear the actual questions buyers and customers ask before those questions show up in keyword tools. A shared doc, a Slack channel, or a quarterly review routes that language back to the people creating content for AI search.

Marketers who are serious about optimizing for AI search behavior benefit from using a specialized AEO tool for prompt discovery and tracking. Subscribers of Marketing Hub Professional or Enterprise plans have an advantage because they can access AEO, which can suggest prompts based on business context within the CRM.

Source

Step 2: Build extractive answers and entities.

Now take the questions you identified in step one and create new content (or optimize existing content) to address them. Structure each page to answer the main question in its introduction, then reinforce the brand entity behind it. AI answer engines favor content that resolves the query immediately and identifies the source clearly, and as a March 2026 preprint from Junwei Yu et al. showed, structural changes — heading hierarchy, paragraph chunking, and visual emphasis — can lift citation rates by double digits across the six engines they tested.

• Direct-answer openers answer the target query inside the first sentence of each paragraph; anything else is preamble that pushes the answer lower than it needs to be.
• Q&A, definition, and decision-guide formats map cleanly to the response shapes answer engines reuse when composing summaries.
• Brand entity consistency across your domain, LinkedIn company page, Crunchbase profile, and review listings (G2, Capterra) strengthens recognition when answer engines compose responses.

Step 3: Apply schema markup and internal links.

Schema markup and internal linking give answer engines structural cues to help them interpret pages and rank source quality.

HubSpot’s State of AEO 2026 found that pages with FAQ sections are more likely to be cited in AI Overviews, and FAQ sections paired with schema markup correlate with higher citation rates in Gemini, Google AI Mode, and Perplexity. The combination that performed best in the dataset: a descriptive H2 like “Frequently Asked Questions About [Topic]” with each question formatted as an H3 below it. Generic “FAQ” headings produced weaker results.

Heading structure carries its own citation signal in the same dataset. Keyword-rich H1s correlate with more citations. Including the year in H1s and meta titles helps, and more headings overall — particularly H3s and H4s — track with higher citation rates. The sweet spot is pages with 7 to 15 H2s.

Adding schema to optimize webpages is a debated topic in AEO. “It’s not a bad idea, but it’s not going to move the needle that much,” says AEO strategist Kaleigh Moore, who prefers to focus on off-site signals on platforms like LinkedIn and YouTube. “Those kind of off-site, third-party sources that are getting really in-depth are really great at earning citations,” she adds.

Elie Berreby, head of SEO and AI search at Adorama, takes a different view on schema markup. “100% I would recommend using it,” he told me, “but not like most people use structured data — in a smart way, by interconnecting the different entities.” Schema’s value, in Berreby’s framing, is building the knowledge graphs that help answer engines map entity relationships. Even when schema is injected via JavaScript (which many AI crawlers can’t render), Googlebot can still process it, which has downstream effects. “If you have good structured data and this leads to a richer search result, it now feeds the AI scraper, which then feeds the AI-generated answer,” Berreby explains. “It’s an indirect mechanism.”

My take: Implement schema, but don’t expect it to be the single lever that wins you citations. The State of AEO 2026 data is correlational, and the citation lift only shows up reliably in combination with a well-structured FAQ section.

Lastly, don’t forget internal links; they reinforce topical authority and route ranking signals between related pages.

Step 4: Publish, monitor, and iterate.

After you publish content, make changes based on what the data tells you. Keep a spreadsheet or create a dashboard to track citation shifts, lost prompts, and competitor gains, and review this on a weekly to monthly basis. Here’s what to log:

• Baseline snapshots capture where your brand stands at the moment of publication; without them, later movement is impossible to interpret.
• Loss logs record which prompts your brand stopped appearing in and which competitor replaced you, surfacing the patterns worth fixing first.
• Win logs track which new prompts your brand started showing up in after edits, helping you reverse-engineer what worked.

AEO Grader generates the baseline snapshot in minutes; HubSpot AEO handles ongoing tracking, competitor monitoring, and prompt-level reporting so you can iterate without manually prompting.

AI visibility measurement tracks two metrics invisible to GA4 and Search Console: brand mentions (answers naming your brand without a link) and share of voice (how often your brand surfaces versus competitors for category questions). You can manually enter your highest-priority prompts in ChatGPT, Gemini, and Perplexity on a fixed cadence and log which sources get cited. But HubSpot AEO automatically tracks prompts and monitors shifts in those signals over time.

How often should we update AI-optimized content?

Update top-performing pages whenever you see a major drop in citations in your AEO software. Otherwise, AI-optimized content needs a monthly visibility re-check, a quarterly content audit, and an immediate re-test after any major model release. Models update often enough that it could affect your key content considerably (OpenAI, Anthropic, Google, and Perplexity all publish release notes worth watching).

How can we increase our chances of being cited by LLMs?

LLM citation likelihood rises through four content disciplines: answer-first writing, parseable structure, entity consistency, and topical authority. The Yu et al. study found that structural rewrites alone — without changing the content’s meaning — lifted citation rates across six engines by 17.3% on average

Here are four changes worth making to your content to increase LLM citations:

• Answer-first content opens with the direct response to the query in the first paragraph, then supports it with clear definitions, original data, expert quotes, examples, and up-to-date sources. Stanford research shows language models pull most heavily from the beginning of a passage, which is why a buried answer might not earn a citation.
• Parseable structure uses descriptive H2s and H3s, concise summaries, comparison tables, and FAQ-style sections where appropriate, paired with valid Article, Organization, Product, or FAQPage schema. Structured formats like lists and tables outperformed prose on extraction accuracy by 43% in the Yu et al. cross-engine testing.
• Entity consistency means ensuring the same brand, product, author, and executive names across your site and others. This might include your about page, author bios, LinkedIn, Crunchbase, G2, and other trusted third-party profiles.
• Topical authority builds through internally linked content clusters and a refresh cadence that updates high-priority pages when facts, products, pricing, rankings, or model behavior change.

Do we need to change link-building for answer engines?

No, you don’t need to change link-building for answer engines, but you do need to understand why it still matters for AEO. Backlinks help with SEO, and because answer engines use search indexes, they matter for AEO too. However, what’s different in AEO is that unlinked brand mentions influence AI answers: YouTube videos, Reddit threads, comparison roundups, and third-party reviews. So diversifying into the formats and platforms answer engines actually quote matters more than chasing raw link counts.

What’s the best way to align teams around these changes?

Sales, service, and marketing teams can align around AI search behavior changes by creating a shared dashboard and a feedback loop. Sales reps hear the AI-surfaced objections shaping early conversations, and service teams see which questions land in chat first — both signals belong in the marketing content team’s roadmap. HubSpot AEO surfaces citation and competitor data in one workspace, making it easier to pair AI search signals with the questions sales and service heard that month.