What higher ed data shows about SEO visibility and AI search

AI search hasn’t killed SEO.

Now you have to win twice: the ranking and the citation.

Google searches for almost anything today, and there’s a good chance you’ll see an AI Overview before the organic results, sometimes even before the ads. 

That summary frames the query, shortlists sources, and shapes which brands get considered.

AI Overviews now appear for about 21% of all keywords, according to Ahrefs. And 99.9% are triggered by informational intent.

Search rankings still matter. But AI summaries increasingly determine who wins early consideration.

Here’s what we’re seeing: brands aren’t losing visibility because they dropped from position three to seven. They’re losing it because they were never cited in the AI answer at all.

This article draws on research conducted by Search Influence and the online and professional education association UPCEA, which examined how people use AI-assisted search and how organizations are adapting. (Disclosure: I am the CEO at Search Influence) 

Key takeaways

• AI citations are becoming a trust signal: Being cited by AI influences credibility and early consideration – before users ever compare sources directly.
• AI visibility is cumulative: AI systems pull from your website, YouTube, LinkedIn, and third-party publishers to assemble answers. Your URL isn’t the only thing that matters.
• Authority doesn’t guarantee inclusion: Even established brands get sidelined when their content doesn’t match how users ask questions.
• Most organizations know AI search matters but lack a plan: The gap isn’t awareness – it’s ownership, prioritization, and repeatable process.
• Content structure affects whether you get cited: Pages built for retrieval, comparison, and decision-making outperform narrative or brand-led content.

Examining both sides of the search equation

To understand what’s happening, we need to look at two sides of the same equation – how people are searching today and how organizations are responding (or aren’t).

“AI Search in Higher Education: How Prospects Search in 2025” surveyed 760 prospective adult learners in March 2025. It examined:

• Where online discovery happens.
• How AI tools are used alongside traditional search.
• Which sources people trust during early research.

While the study focused on professional and continuing education, these behaviors mirror what we’re seeing across industries: more AI-assisted discovery, earlier opinion formation, and trust signals shifting.

A separate snap poll of 30 UPCEA member institutions in October 2025 looked at the other side:

• AI search strategy adoption.
• Barriers slowing progress.
• How visibility in AI-generated results gets tracked.

Together, these datasets show a widening gap between how people search and how organizations have adapted.

So what does the data actually tell us?

The search patterns worth paying attention to

The research highlights several search behaviors that consistently influence how people discover and evaluate options today.

AI tools and AI summaries are influencing trust early

The data makes one thing clear: AI-driven search has moved from the margins into the mainstream.

• 50% of prospective students use AI tools at least weekly.
• 79% read Google’s AI Overviews when they appear.
• 1 in 3 trust AI tools as a source for program research.
• 56% are more likely to trust a brand cited by AI.

Trust is forming earlier now, often before users compare sources directly.

If you’ve been putting off your AI search strategy because “people don’t trust AI,” the data says otherwise. AI citations are becoming a credibility signal – a trust shortcut before deeper research begins.

Search behavior is diversified

Search doesn’t happen in one place or follow one clean path anymore.

• 84% of prospective students use traditional search engines during research.
• 61% use YouTube.
• 50% use AI tools.

These behaviors aren’t sequential. Users move between surfaces, carrying context with them.

What they see in an AI summary influences how they read a search result. A YouTube video can establish trust before a website ever earns a click.

This is where many strategies fall out of sync. Teams optimize one channel at a time – usually their website – and treat everything else as optional.

But AI search engines pull from everywhere your brand has a presence:

• Your website.
• Your YouTube channel.
• Your LinkedIn content.
• Third-party and publisher sites.

Your AI credibility is cumulative. It’s built anywhere your brand shows up, not just where you own the URL.

Search engines and brand-owned websites still matter

The rise of AI search doesn’t mean the end of traditional search. It raises the bar for it.

Even as AI summaries reshape early trust, people still rely heavily on first-party sources and organic results when they evaluate options:

• 63% rely on brand-owned websites during research.
• 77% trust university-owned websites more than other sources.
• 82% are more likely to consider options that appear on the first page of search results.

AI engines prioritize content that search engines can already crawl, interpret, and trust.

If your core content isn’t clearly structured, accessible, and eligible to rank in traditional search, it’s far less likely to be pulled into AI-generated answers.

Dig deeper: Your website still matters in the age of AI

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Organizational readiness lags behind

Most organizations recognize that AI search is reshaping discovery. Far fewer have translated that awareness into coordinated action.

AI search strategy adoption remains uneven

Most institutions sit somewhere between curiosity and commitment:

• 60% are in the early stages of exploring AI search.
• 30% have a formal AI search strategy in place.
• 10% haven’t started or believe AI search will have limited impact.

The majority of teams know something important is happening. But ownership, process, and prioritization remain unresolved.

What’s slowing progress

When asked what’s holding them back, institutions cited execution constraints:

• 70% report limited bandwidth or competing priorities.
• 37% report a lack of in-house expertise or training.
• 27% report unclear ROI, leadership buy-in, or uncertainty around how AI search works.

For many organizations, AI search has entered the roadmap conversation. It just hasn’t earned consistent operational focus yet. (Sound familiar?)

Dig deeper: Why most SEO failures are organizational, not technical

What teams say they’re prioritizing

When teams do take action, their priorities cluster around two themes:

• 59% focus on the accuracy of AI-generated information about their offerings.
• 48% focus on improving visibility and competitive positioning.

Those goals are linked. Clear, structured information makes it easier for AI systems to represent a brand. Visibility follows clarity. When that clarity is missing, AI fills in the blanks using third-party sources and competitor content.

Tracking AI visibility remains inconsistent

AI visibility tracking varies widely:

• 57% know their institution appears in AI-generated answers.
• 27% have seen their brand referenced occasionally but don’t actively monitor it.
• 13% are unsure whether they appear in AI-generated responses at all.

Among teams that do track AI visibility:

• 64% use dedicated tools or formal tracking methods.
• 29% rely on informal checks or don’t track consistently.

This creates a familiar blind spot. Teams feel the impact of AI search anecdotally but lack consistent visibility into where, how, and why their brand appears.

Dig deeper: How to track visibility across AI platforms

Why higher ed is a useful lens

Universities bring everything search engines are supposed to reward:

• High domain authority.
• Deep, long-standing content libraries.
• Strong brand recognition.

Yet in AI-generated answers, those advantages often don’t translate. When AI systems generate answers, they cite content that already matches the way users ask questions. That often means:

• Comparisons.
• “Top tools,” “top programs,” or “top options” lists.
• Third-party explainers written about brands.

Those formats are dominated by aggregators and publishers – not the institutions themselves.

AI doesn’t look for the biggest brand. It looks for the best answer. Higher education shows what happens when brands rely on authority alone and why every industry needs to rethink how it publishes.

So what do you do about it?

1. Get your foundations in order before chasing AI visibility

The most common question right now: “How do we show up in AI results?”

In many cases, I think the honest answer is to fix what’s already broken.

AI systems rely on the same signals that traditional search does: crawlability, structure, clarity. If your pages are blocked, poorly organized, or weighed down by technical debt, they won’t surface cleanly anywhere.

We’ve seen teams invest energy in AI conversations while core pages still struggle with:

• Indexing issues.
• Bloated or unclear page structures.
• Content written for storytelling, not retrieval.

Start with your traditional SEO foundation. AI systems can only work with what’s structurally sound.

Dig deeper: AI search is growing, but SEO fundamentals still drive most traffic

2. Optimize content for retrieval, not just reading

AI search engines favor content that can be lifted cleanly and reused without interpretation. The job of content shifts from “telling a complete story” to “delivering clear, extractable answers.”

Many brand pages technically contain the right information, but it’s buried in long-form prose or brand language that requires context to understand.

Content that performs well in AI answers tends to:

• Lead with direct answers, not setup.
• Use headings that map to search intent.
• Separate ideas into self-contained sections.
• Avoid forcing readers (or machines) to infer meaning.

This isn’t about shortening content. It’s about sharpening it. When intent is obvious, AI knows exactly what to pull and when to cite you.

3. Compete on format, not just authority

If AI keeps citing comparisons, lists, and explainers – and it does – brands probably need to own those formats themselves.

AI systems pull from content that already reflects how people evaluate options. When those pages don’t exist on your site, AI cites the aggregators and publishers instead.

To compete, brands need to publish:

• Comparison pages that reflect real decision criteria.
• “Best for X” content tied to specific use cases.
• Standalone explainers that help buyers choose.

Put simply: publish what AI actually wants to cite.

Dig deeper: How to create answer-first content that AI models actually cite

4. Prioritize third-party platforms

Your website shouldn’t be doing all the work.

AI answers routinely pull from a mix of sources:

• YouTube videos.
• LinkedIn posts.
• Instagram content.
• Reddit threads (when relevant).
• Brand content published on third-party platforms.

In some cases, being cited from a third-party platform matters more than where your site ranks.

We’ve seen AI Overviews where a brand’s YouTube video is cited alongside their webpage and third-party sources – all shaping the same answer. That blended source set is becoming the norm.

If your content strategy only prioritizes on-site publishing, you’re narrowing your chances of earning AI visibility.

Dig deeper: YouTube is no longer optional for SEO in the age of AI Overviews

Where things stand

AI search is moving faster than most SEO strategies are built to respond.

• Discovery is happening earlier.
• Trust is being assigned sooner.
• Visibility is being decided before rankings ever come into play.

The question isn’t whether AI search will matter to your industry.

It’s whether you’ll be cited, overlooked, or summarized by someone else.

The brands that adapt now – not later – will be the ones that win.

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