We are quickly entering a new era for higher education marketers. Large language models and intelligent agents are actively shaping the brand narratives future students will see. However, the question still remains how much of the typical decision-making process will be affected outside of the ways prospective students filter options. As artificial intelligence and AI agents become more pervasive in use and usage, there’s a narrative that trust is moving from brands to platforms. Implicit in this narrative is that brands must not only consider the attention of minds, but also now machines.
The Great Shift: From Browsers to Bots
Over the last year, a not-so-subtle shift has taken root: prospective students are now asking their questions directly to Generative AI tools like ChatGPT, Perplexity, and Google’s own AI-enhanced search. Google’s embedded “AI Overview” box — built on Gemini, its AI assistant — now tackles complex queries directly atop the results page, absorbing user attention that once went to college search sites, .edu’s, or perhaps Adwords campaigns.
Concurrently, search queries are growing richer. The average query length on AI platforms is now more than 10 words, compared to just 2–3 words typical of a legacy Google query — demonstrating a user shift toward conversational, intent-rich querying.
Roughly one in four students now use some form of AI chatbot during their college search, According to a recent EAB survey, and nearly a third of Gen Z students report engaging with ChatGPT or similar tools to help them research schools. Exploring the “Modern Learner’s” search habits, EducationDynamics found that nearly 70% of learners regularly use AI tools including chatbots, while roughly 37% use these tools to find information about institutions.
For machines, relevancy comes from clear, solutions-based content, while visibility is achieved through structured data, schema markup, and authoritative sources.
This migration has fueled a surge in “zero-click” search behaviors. Today, just 27% of users click the top organic result; fewer than 1% venture to a second results page. Remarkably, about 60% of searches conclude without a click to any site at all, compared to 26% in 2022 according to Semrush, an SEO and marketing platform.
Zero-click has led to visibility gaps for marketers. Brands that dominate AI summaries but lag in traditional SEO now possess unequal brand impressions and awareness across query platforms, with AI-selective summarization favoring authoritative, structured sources. Consequently, a university may be recognized by students via an AI-generated snippet without any meaningful backlink-driven engagement.
The Challenge of More: How AI Has Led to Excess
For higher education marketers, the explosion of student discovery pathways has triggered what you might call the perception of more. There is more data, more platforms, more questions, more channels, and, naturally, a sense that marketing teams need to do more to keep pace. And no doubt with more moments in a seemingly endless array of journeying, there will be more technology to stack.
This anxiety is not unique to higher ed. As a recent Madison & Wall analysis of the ad industry argues, planners and marketers everywhere are experiencing a shift from managing a neat set of steps to adapting within platform-driven ecosystems. Media fragmentation has rendered management an unrelenting task for marketers. Now with Agentic SEO and LLM-driven discovery, there will be the fragmentation of funnels, as well.
We need to invest in two major moments: the moment before the consideration set and when prospects are actively engaged in the purchase decision.
While we know this will bring additional layers of complexity, we must be wary of solutions chasing problems. Acknowledging that either (a) customer journeys may inherently be ineffective or (b) impossible to manage, isn’t conceding. If we find that 84% of purchases are already decided before the buying stage, then we need to invest in two major moments: the moment before the consideration set — i.e. building brand equity. And the second moment, when prospects are actively engaged in the purchase decision.
If we are overcome by the challenges of more, the biggest concern will be the loss of focus. Institutions of higher ed seeking to manage every moment on the very long path to purchase will inevitably spread their resources too thin. This diffusion causes a reduction in effectiveness during an already critical moment. However, the pendulum can swing the other way.
If AI accelerates how quickly people discover and filter choices, then brand trust must reassert itself with emotional relevance and technical visibility. We can acknowledge complexity of the customer journey but also remain clear where our marketing efforts will have substantial impact. Given this reality, the strategic starting points reside in capturing both brains and bots.
Building Brand Resilience
What sounds like two concepts in tension — marketing to humans and marketing to machines — actually shares the same antidote. Both brains and bots thrive on relevancy and visibility, underpinned by trust. For humans, relevancy is built through emotional connection, while visibility is secured through memorable, distinctive brand-building. For machines, relevancy comes from clear, solutions-based content, while visibility is achieved through structured data, schema markup, and authoritative sources.
In both cases, trust is what gives the institution permission to be considered. Where higher ed marketers should focus is the point at which trust overlaps, both in the technical and the tactical. Here are four ways to focus on building brand resilience that maintains a clear through line to what matters to both brains and bots.
1. Consistent, authentic brand narrative
- Humans want a story they can believe in.
- Bots want consistent, factual language.
- Overlap: One clear, authentic brand voice expressed with disciplined consistency across every asset, from ad copy to program descriptions to faculty bios.
2. Clear, relevant content
- Humans want information that feels useful and empathetic.
- Bots want structured answers to specific questions.
- Overlap: Write content with clarity, structure, and empathy. Put key details up front. Organize your program pages, student stories, and value propositions logically but also in a way that’s context-rich and marked up schematically. Long-form or short-form, well-organized, verified and directly addresses a need or query builds trust.
3. Proof and credibility signals
- Humans trust testimonials, rankings, alumni success.
- Bots trust authoritative third-party mentions, reputable links, and consistent data sources.
- Overlap: Invest in proof. Alumni outcomes, respected rankings, and positive earned media stories build trust with humans and are increasingly weighted by AI systems as authoritative signals. Keep third-party profiles consistent and accurate, and nurture high-quality backlinks from reputable sources. As earned media regains influence in the AI-powered ecosystem, these credible references become essential for ensuring your institution is trusted and visible to both brains and bots.
4. Broad, memorable brand assets
- Humans recall your institution through distinctive visuals, tone, and repeated exposure.
- Bots crawl, classify, and index consistent brand assets to build confidence in their outputs.
- Overlap: Consistency is critical. Maintain a unified visual identity, naming conventions, and tagline across every channel — paid, owned, and earned. Humans rely on repeated, recognizable brand codes to build familiarity and trust, while AI systems depend on consistent signals to classify and recommend your institution accurately. Broad application of these assets ensures you remain memorable to people and discoverable by machines.
In higher education, where decisions are complex, high-risk, and highly emotional, this is a profound change. Historically, we built marketing strategies to influence humans — counselors, parents, alumni, influencers, and of course prospective students. Now, we must add a new stakeholder to that audience map: the machine. Navigating how to “do more” will ultimately become the challenge. Yet, we know that a strong strategy isn’t about doing more. It’s often about doing less with the discipline to focus. For now, these overlaps offer that opportunity.
