Most universities are losing organic traffic. It’s not a hunch. It’s a data point that comes up in conversation after conversation with university marketing teams across Spain and Latin America.

And yet some institutions are not only not losing — they’re gaining. And they’re gaining more qualified traffic.

What are they doing differently?

The answer isn’t a clever SEO tactic or a more creative branding campaign. It’s something more structural: they have understood that the prospective student’s customer journey has split in two, and that each half demands radically different things.

The false dilemma

There is a growing tension in marketing — not just in higher education, but in general — that surfaces as two seemingly opposing schools of thought.

On one side, the technical school. Carlos Saldaña, CMO of IE University, points out that the customer’s first contact with a brand is no longer human: it’s a system, an algorithm, a machine that filters, prioritizes, and recommends. In this new model, Saldaña says, the machine decides first whether a brand exists as an option at all. Voices like Neil Patel and Nate B. Jones reinforce this position: brands need to optimize their presence for AI agents, build structured data infrastructure, and accept that discovery is now algorithmic.

On the other side, the human-connection school. Seth Godin, Jaime Hunt, and researchers Allison Steinke and Haseon Park argue that marketing is built on trust, consistency, empathy, and purpose. Godin warns that if you let AI become the final buyer, you’ll always lose, because AI just looks for the cheapest option or the most direct answer.

Who is right?

Both schools. But they operate at different stages of the funnel. Saldaña sums it up with a line many marketers will find uncomfortable: emotion no longer opens the door; it closes it. Emotion is still critical, but it arrives at a different moment in the process.

The key is understanding where one stage ends and the next one begins.

Phase 1: Discovery is no longer human

The new first contact

Think about what’s happening right now. A student in Mexico City opens ChatGPT and types: “Best business universities in Europe with English-taught programs.” The agent doesn’t browse websites. It doesn’t watch your institutional video. It doesn’t read your brand manifesto. What it does is interpret structured data, extract verifiable information, and build an answer.

Neil Patel describes it bluntly: the entire funnel — awareness, consideration, decision — collapses into a single AI conversation. In reality that funnel compression depends on the product, and in education it doesn’t fully collapse. But it’s true that, in the discovery stage, advertising no longer needs to convince a human. It needs to convince an agent. And as Patel notes, if your answers are buried under five paragraphs of brand storytelling, the agent skips you.

Saldaña takes this observation into the concrete territory of university marketing — a sector where he has spent years building one of the most recognized brands in the Spanish-speaking market. IE University didn’t get to its current position by chance: it got there through a combination of a powerful brand and solid digital infrastructure. And when Saldaña says that an ambiguous brand can be inspiring to a creative but invisible to an algorithm, he speaks from the experience of someone who has solved that equation in practice.

Brian Rosenberg devotes an entire chapter of Whatever It Is, I’m Against It to explaining how extraordinarily hard it is to build reputation in higher education. Universities are complex organizations with distributed governance, multiple identities, and long change cycles. That a university manages to build a coherent, recognizable brand is an achievement very few reach. What Saldaña has built at IE University carries even more weight when you understand the structural difficulty of the environment.

The technical Truth Layer

Nate B. Jones articulates this need with a concept worth adopting as-is: the Truth Layer. An infrastructure of structured, verifiable, high-fidelity data about your company — or your institution — that lets AI agents accurately interpret what you offer. Jones puts it plainly: you need to have, and care about, a Truth Layer that lets you reliably distribute high-quality, high-fidelity data about your product to agents on the internet.

If that information exists but is scattered across PDFs, buried in subpages, duplicated with inconsistencies between the school site and the institutional site, or written in such aspirational language that it loses specificity, the Truth Layer is broken. And an AI agent with a broken Truth Layer does one of two things: it misinterprets you or it ignores you.

When fragmentation destroys the Truth Layer

I recently met with the team at Universidad Pontificia Comillas. For years, their digital presence reflected an internal tension many universities know well: three historical brands — Comillas, ICAI, ICADE — with strong identities, distinct audiences, and in many cases divergent messages across the digital ecosystem.

For a prospective student looking for information, the experience was confusing. For an AI agent trying to interpret what Comillas is and what it offers, the signals were contradictory. Three identities that didn’t resolve into a coherent structure. The technical Truth Layer was fragmented.

They’ve worked on aligning those divergences. And the result is that, against the overall sector trend, they’re not losing organic traffic.

It’s not an isolated case. IE University — an institution that has invested consistently in building both brand and solid digital infrastructure — is not only holding its organic traffic; it’s growing it, and with a more qualified visitor profile.

The pattern is clear. Universities with a well-built technical Truth Layer — coherent data, structured content, a unified digital ecosystem — are weathering the shift toward agent-driven discovery. Those with fragmentation, inconsistencies, or purely aspirational content that no machine can parse are losing ground every month.

Phase 2: The decision is still deeply human

The filter is not the decision

But here comes the nuance the purely technical school tends to ignore.

Once the AI agent has filtered options and the student has a shortlist of three or four universities, the machine has done its job. Now the human steps in. And the human doesn’t decide with structured data. They decide with trust, with emotion, with the feeling that “this place is for me”.

Seth Godin warns with his usual bluntness: when an AI is the buyer, you’re going to lose, because when an AI shows up it’s hard to teach it intangible value, so it just goes and buys cheap. AI can filter. But the decision about which university you’ll dedicate four years of your life to — and a considerable financial investment — is not made by an algorithm.

This is where the second Truth Layer comes in.

The human Truth Layer: brand believability

Researchers Allison Steinke and Haseon Park have worked on a concept that’s highly relevant here: Brand Believability. They highlight the importance of being emotionally attached to a brand that is also credible and consistent in delivering high-quality products and services. Emotional attachment only works when the brand generating it is perceived as believable.

Godin reinforces the same idea from a different angle. For him, authenticity is overrated: authenticity is a trap, he says; what customers actually demand is consistency. Consistency is what professionals do. Do I believe you’ll keep your promise? If you make real promises and you keep them when it’s hard, that’s how you build trust.

Applied to higher education: a prospective student who already has a shortlist (filtered by AI or by their own search) enters a phase where they evaluate whether they trust each institution’s promise. Is what they claim to be believable? Does what they promise match reality?

And this is where university marketing has a structural problem.

The Better Thesaurus Fallacy

Jaime Hunt, one of the sharpest voices in Anglo-American higher-ed marketing, describes a phenomenon he calls The Better Thesaurus Fallacy: the tendency of universities to search for more sophisticated words to say exactly the same thing every other university says.

Is this a theoretical problem? No. It’s verifiable.

The case of the Spanish Business Schools

I analyzed the public positioning of seven of the leading business schools in Spain: ESADE, IESE, IE Business School, EAE, ESIC, EADA, and Deusto Business School. The result is a textbook case of the syndrome Hunt describes.

“Positive impact.” All seven schools — all seven — promise to generate positive impact on society. ESADE talks about “positive impact on society”. IESE about “positive impact on people, companies, and society”. IE about “Business with Purpose”. EAE about “sustainable organizations”. ESIC about “transforming people for a better world”. EADA about “positive impact on society and the world”. Deusto about “a more prosperous, just, and inclusive world”.

“Shaping leaders.” All seven shape leaders. Responsible leaders (ESADE), leaders with purpose (IESE), leaders who make a difference (IE), leaders of sustainable organizations (EAE), agents of change (ESIC), the business leaders of the future (EADA), professionals who lead sustainable projects (Deusto). Variations of the same promise with different synonyms.

“Humanism” and “excellence.” ESADE, IESE, EADA, and Deusto — the four with Jesuit or humanist roots — use exactly the same phrase: “humanist approach to leadership”. “Excellence” shows up as a founding value at ESADE, IESE, EADA, and Deusto.

“Sustainability.” Five out of seven mention sustainability as a positioning axis.

“Transformation.” ESADE offers a “transformative experience”, ESIC promises “Transforming People”, EADA talks about “organizational transformation”, Deusto about “social transformation”.

Even the institutional taglines share the same aspirational structure: ESADE says “Do Good. Do Better”. EADA, “Leading What Matters”. ESIC, “Transforming People”. Deusto, “Business Education to Serve the World”. All inspiring phrases. None differentiating.

A prospective student comparing ESADE, IESE, and EADA in the consideration phase finds no real differentiation in the messaging. All promise the same thing with different synonyms. And an AI agent asked “what differentiates ESADE from IESE?” has a serious problem: the narrative Truth Layer of both says essentially the same thing — leaders, impact, humanism, excellence, sustainability. The agent falls back to rankings or gives a generic answer, because the positioning content offers nothing to differentiate.

Why does this happen? In part, because these schools compete primarily in proximity markets. The Spanish student’s real decision is shaped by factors like location (Barcelona vs. Madrid vs. Bilbao), local alumni network, price, and inherited reputation. Aspirational messaging is a technical draw nobody wins or loses, because real differentiation happens through other channels.

But that doesn’t mean the problem doesn’t exist. It means it has been normalized. And in a context where the first filter is algorithmic and the student’s attention span keeps shrinking, the aspirational draw becomes a growing vulnerability.

Show, don’t tell

Hunt offers a concrete alternative. The strongest brand platforms show instead of telling. He gives an example I haven’t forgotten: instead of saying “we are a supportive community”, he talks about the professor who creates personalized video feedback for every student. That’s evidence. That’s texture. That’s specific.

Specificity convinces the human evaluating whether this university is theirs.

And here’s the most interesting part of the thesis.

The bridge: two Truth Layers that feed each other

That same specificity — the concrete evidence, the texture, the verifiable data point — is exactly what best feeds the technical Truth Layer from the previous phase.

An AI agent can interpret “87% of [university] MBA graduates work in leadership positions within three years” far better than “we shape leaders with global impact”. The first sentence is structured, verifiable, differentiating data. The second is category language no machine can distinguish.

This is the point where the two schools of thought stop being opposites and become a system.

The technical Truth Layer — structured data, interpretable content, a coherent digital ecosystem — determines whether you exist in the discovery phase. Whether an AI agent can find you, interpret you, and recommend you.

The human Truth Layer — brand believability, consistency, specificity, real evidence — determines whether you convince in the consideration phase. Whether the student who already has you on their shortlist feels this university delivers on its promise.

And as Jones points out, the best marketers will refuse to choose between these two worlds. They’ll choose both. Because if your human brand says one thing and your agent-readable reality says another, the institution weakens in both directions.

Jaime Hunt sums it up with his motto “Heart Over Hype”: against the excess of noise, empty tactics, and soulless automation, deep connection and empathy are what transform marketing in the long run. But that deep connection needs an infrastructure that supports it and makes it visible in a world where the first filter is no longer human.

What this means for your university

If you lead marketing at a university, the question is no longer “do we invest in technical SEO or in branding?”. That’s a question from the previous paradigm.

The question now is: do we have both Truth Layers built, and are they aligned with each other?

The technical Truth Layer. Does your digital ecosystem send coherent, structured signals an AI agent can interpret correctly? Or do you have fragmentation across schools, inconsistencies across campuses, and content only a patient human can decipher? Could an AI agent correctly answer “what does your university offer and why should I choose it?” using only what it finds in your digital presence?

The human Truth Layer. Does your brand say something specific and demonstrable, or are you using the same aspirational language as every other university in your category? Can you show concrete evidence of what makes you different — not claims, but data, real stories, verifiable results? If you put your positioning claim next to your five direct competitors’, could a student tell which one is yours?

And the most important question: do they say the same thing? Because if your institutional site promises “educational innovation” but an AI agent can’t find concrete data to back it up, you have a coherence problem that will cost you students. And if your technical infrastructure is impeccable but your brand narrative is indistinguishable from the competition, you’ll pass the algorithmic filter only to lose in the consideration phase.

The universities winning in this new landscape haven’t chosen between the technical school and the human school. They haven’t prioritized algorithmic optimization over emotional connection, or brand over infrastructure.

They’ve built both Truth Layers. And they’ve aligned them.