Jennifer Neeley

Strategic Marketing Advisor for Trust-Sensitive Growth

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Teaching & Learning

In an AI-Saturated Summer, Judgment Became the Most Important Marketing Skill

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This summer, I taught digital marketing in a classroom where AI was already everywhere.

Not theoretically: Students were using it. Marketers were using it. Organizations were trying to figure out what it meant for search, content, social media, customer behavior and their own jobs.

Before the course began, I asked students what they wanted to understand.

The questions came quickly:

  • How should marketers use AI?
  • How is it changing search?
  • How do you know whether the information it produces is accurate?
  • How much content is too much content?
  • What makes people trust a brand now?
  • Which platforms still matter?
  • How do you know whether something actually worked?

The questions looked like they were about technology.

By the end of the term, I thought many of them were about something else: Judgment.

AI made producing an answer easier

That is the obvious change.

A marketer can now generate an audience profile, content calendar, competitive analysis, list of keywords, campaign concept or draft strategy before lunch. Tasks that once required hours of searching and synthesis can happen in minutes.

That is not trivial. AI’s extraordinary advantage is compression.

It compresses the distance between a question and something that looks like an answer.

But the classroom made the other side of that equation impossible to ignore.

The ability to produce something quickly does not tell you whether the thing is right.

  • Or useful.
  • Or based on credible evidence.
  • Or appropriate for this customer.
  • Or strategically connected to the business problem.
  • Or even solving the problem you thought you had.

Speed without verification just accelerates mistakes.

The work changed when the questions changed

One of the most interesting parts of teaching marketing is watching what happens when students stop asking: What should we do?

And start asking: Why would we do it?

A weaker strategy starts with tactics.

  • We need TikTok.
  • We need SEO.
  • We should use influencers.
  • We need an AI strategy.
  • We should run paid search.

A stronger strategy asks:

  • Who exactly are we trying to reach?
  • What are they trying to accomplish?
  • What do they already believe?
  • Where are they getting information?
  • What could make them hesitate?
  • What evidence tells us this is the actual problem?
  • What would have to change for the strategy to work?
  • What would we measure to find out whether we were right?

Those questions are considerably less glamorous than prompting an AI tool. They are also where most of the value lives.

A buyer persona can now be wrong faster

Buyer personas provided a particularly useful example.

Give an AI tool a company name and some context and it can produce an impressive-looking persona almost instantly.

  • Name.
  • Age.
  • Occupation.
  • Goals.
  • Frustrations.
  • Media habits.
  • Maybe even a stock image and a quote.

It looks “complete.” But that is precisely the problem.

A polished artifact can create the impression that a question has been answered when the underlying assumptions have never been tested.

The persona may be plausible, but plausible is not the same as true.

The better student work moved beyond demographic decoration to examining the decision itself.

  • What is this person actually trying to accomplish?
  • What creates uncertainty?
  • What information do they need?
  • What do they trust?
  • What alternatives are they considering?
  • What prevents action?
  • What changes their mind?

AI did not make the buyer persona obsolete, it made unverified personas easier to manufacture.

That raises the standard for the marketer.

The same thing happened with customer journeys

Funnels look wonderfully orderly in a presentation.

Awareness ➡️ Consideration ➡️ Conversion

The real people inside them are considerably less cooperative.

  • They search.
  • They leave.
  • They ask someone.
  • They watch something.
  • They forget.
  • They come back.
  • They read reviews.
  • They ask AI.
  • They compare.
  • They abandon the process.
  • They return three weeks later from another device.

My students repeatedly confronted the difference between the journey they wanted customers to follow and the one customers were likely to follow.

That distinction matters because marketing plans become much stronger when students stop designing around a diagram and start designing around behavior.

The marketer’s job is not to force people neatly through a funnel.

It is to understand enough about the decision to remove unnecessary friction along the way.

Trust kept showing up where I wasn’t explicitly teaching “trust”

This happened throughout the course.

  • We would be discussing search and end up talking about credibility.
  • We would discuss social media and end up talking about reputation.
  • We would discuss customer journeys and encounter uncertainty.
  • We would discuss AI and end up asking whether the answer deserved to be believed.

By the time we reached privacy, security, ethics and reputation, the pattern was difficult to miss.

Trust was not another topic in the curriculum, it was running through nearly all of them. That led to a question I kept returning to: What would make an audience hesitate to trust an organization online—even when the marketing appears polished?

It is an uncomfortable question, but also incredibly useful because so much of marketing today looks excellent—but still creates doubt.

  • A beautifully designed healthcare site can make claims that are technically accurate but difficult for a patient to interpret.
  • A polished social presence can generate attention without providing evidence.
  • An AI-generated explanation can sound authoritative while flattening important distinctions.

A company can be visible everywhere and credible nowhere.

Trust is not the decorative layer we add after the marketing strategy, it fundamentally changes whether the strategy works.

AI clarity and human clarity are mostly the same discipline

One of the stranger developments this year is the growing industry around making organizations understandable to AI.

There is real work to do there.

Entities need to be clear. Expertise needs to be attributable. Information needs structure. Relationships between people, organizations, topics and evidence need to be legible across systems.

But teaching this summer reinforced something more fundamental for me. Many organizations do not primarily have an AI-clarity problem. They just have a clarity problem.

  • Who are you?
  • Who is this for?
  • What problem do you solve?
  • What makes your expertise different?
  • What evidence supports your claims?
  • Where can someone verify them?
  • What should the person do next?

Those are questions a human needs answered, and they are increasingly questions a machine needs answered, too. We should absolutely learn how emerging discovery systems work. But there is a danger in applying sophisticated optimization to fundamentally unclear positioning. Sometimes the best thing you can do for AI visibility is make the organization easier for everyone to understand.

My students also reminded me that tools are seductive

Marketing has always had this problem.

Every generation gets a new tool that appears capable of solving the discipline.

  • Email.
  • Search.
  • Social.
  • Marketing automation.
  • Influencer marketing.
  • Data.
  • Now AI.

The tool changes, but he temptation remains: we mistake capability for strategy. One of the harder things to teach—and one of the harder things to practice professionally—is restraint. A marketer who understands a new tool can tell you what it can do. A strategist also needs to know when it is irrelevant. That distinction becomes much more valuable when the cost of creating something approaches zero.

We can now make more.

  • More copy.
  • More images.
  • More analysis.
  • More campaigns.
  • More variations.
  • More content than audiences could reasonably consume.

The strategic question is no longer merely whether we can make something, it is whether creating it changes anything that matters.

The scarce skill may be moving

For a long time, access to information was scarce, then information became abundant. AI is making synthesis abundant, too and that changes where human value sits.

I do not think the answer is that humans must simply become more creative than machines. That is too easy.

The more important advantage may be discernment.

  • Knowing which question matters.
  • Knowing when the data is weak.
  • Knowing what assumption an answer depends on.
  • Knowing when a customer description is generic.
  • Knowing what evidence would change your conclusion.
  • Knowing when a tactic is fashionable but strategically irrelevant.
  • Knowing when a beautifully produced answer should make you more skeptical, not less.

AI can help enormously with the work, but it does not relieve us of responsibility for the judgment.

Teaching is becoming a form of market intelligence

This is one reason I continue to value the classroom: teaching forces precision. Students notice when an explanation does not hold together. They ask questions practitioners sometimes stop asking because an industry convention has become familiar.

And each class provides a live view into what people are struggling to understand as the market changes.

This summer, AI generated many of the questions, but underneath them I kept hearing older, harder ones:

  • How do I know what matters?
  • Who should I believe?
  • How do I tell whether this is working?
  • What is the customer actually doing?
  • What should I do with all this information?

Technology changes the surface of those questions, but it does not make them less important.

What I am taking into the fall

I started the summer thinking I would spend a considerable amount of time helping students understand new tools.

I did.

But the more important work became helping them interrogate the output.

  • Ask a better question.
  • Examine the evidence.
  • Identify the assumption.
  • Understand the audience.
  • Connect a tactic to an actual decision.
  • Decide what not to do.

Those are not anti-AI skills, they are the skills that make AI useful. And they are becoming more valuable precisely because producing a plausible answer has become so easy.

The students did not convince me that marketers need to become less technical. They convinced me of something harder: we need to become better judges.

Because in an environment where almost anyone can generate an answer, the competitive advantage may belong to the people who know which answers deserve to survive.

What My Students Have Taught Me So Far About Search, AI and How People Decide in 2026

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I recently gave two groups of students the same assignment: run the same search across Google, an AI tool, and TikTok or YouTube, then explain what appears first—and why.

Same prompt. Same instructions.

What came back read like two different internets.

Then I taught another group of digital marketing students this summer.

What they showed me made the original observation more interesting.

The platforms still looked different. But increasingly, the students did not seem to experience what they were doing as three separate kinds of search.

They were trying to answer one question.

The platform was simply changing along the way.

The same search, three versions of “truth”

Across the earlier groups, the mechanics were consistent.

On Google, students encountered a layered page: ads at the top, product grids, maps, “best of” lists and, increasingly, AI-generated summaries appearing before they had much reason to scroll.

On AI tools, the experience was something else entirely: instead of working through a page of links, they received a clean, structured answer. Categories. Recommendations. Explanations. A tone that often felt decisive.

On TikTok and YouTube, they saw people.

Creators explained, compared and demonstrated. Thumbnails did much of the initial work. Comments functioned as a form of social proof. Content felt immediate and lived-in.

Same query.

Three distinct ways of shaping perception.

At the time, I described the environments this way:

Search surfaces options.
Social surfaces context.
AI delivers conclusions.

I still find that useful.

But after another term of watching students search, evaluate and build marketing strategies, I would add an important qualification:

People do not necessarily experience those as three separate activities anymore.

Increasingly, they experience them as one continuous search.

Where the students diverged

The younger group in my earlier exercise didn’t hesitate much.

They trusted what looked familiar, clicked based on visuals and often didn’t click at all. They described AI as “easy,” “clean” and “helpful.” TikTok felt “real.” Google felt “cluttered.”

Their behavior was fast:

scroll → recognize → decide

The professional group slowed down.

They noticed Sponsored labels, questioned why certain brands appeared repeatedly, and made careful distinctions between paid, organic and generated content.

They trusted structure, but interrogated it.

Their behavior was more deliberate:

query → compare → evaluate → decide

What interested me was not who was right.

It was how precisely this mirrored what I see in the field—and how much more complicated the pattern became over the summer.

A person might now begin with Google, use an AI tool to clarify a category, watch someone demonstrate the product or service on YouTube, check Reddit or reviews for validation, and finally visit a company when they are close to acting.

Marketing systems count those as separate channels.

The person experiences one problem they are trying to solve.

The pattern in practice

In nearly every engagement—particularly in healthcare and fertility—I encounter some version of this disconnect.

A brand believes it is highly visible.

And in a traditional sense, it is.

It ranks well. It invests in paid search. It maintains accurate, comprehensive content.

But when I step into the user’s experience, a different picture emerges.

A patient searching a high-intent question may encounter an AI-generated explanation before reaching a clinic site. They may watch a physician or creator explain the category in thirty seconds and feel they understand it. They may trust reviews, Reddit discussions, repeated mentions or third-party validation more than institutional language.

The organization is present.

But it is not necessarily shaping the decision.

That distinction has become more important to me.

Visibility is no longer the same as being findable—and being findable is not the same as being understood.

A closer look

In one recent case, a clinic had strong performance across conventional metrics: high rankings, consistent SEM investment and a well-developed website.

Yet patient inquiries had plateaued.

Not declining, exactly. Just not growing in proportion to spend.

When we mapped the decision journey—not the funnel as it was imagined, but the sequence as it was actually experienced—the pattern became clearer.

Patients were encountering simplified explanations before they reached the site. Creator-led narratives were framing expectations early. Reviews and third-party mentions carried weight. The clinic’s own content was accurate, but comparatively harder to interpret.

Accuracy matters.

So does cognitive effort.

And effort, in this context, can become friction.

We did not replace what existed.

We translated it.

We restructured key pages so their meaning, expertise and relationships were clearer to both people and machine-mediated discovery systems. We simplified language where possible without compromising accuracy. We elevated external signals—reviews, mentions and expert positioning—that could travel across environments. And we reframed paid search around moments of decision rather than traffic alone.

The changes were modest.

The larger lesson was not.

What another summer of students changed for me

Before my summer course began, I asked students what they most wanted to understand about digital marketing.

Their questions did not remain neatly inside the categories marketers use to organize the field.

Questions about AI became questions about search.

Questions about social media became questions about trust.

Questions about content became questions about visibility.

And questions about visibility eventually became questions about whether any of it changed behavior.

That overlap may be more instructive than the categories themselves.

The channels are converging in the user’s experience faster than many organizations are converging them internally.

We may therefore be making a mistake when we ask only:

Where did someone search?

A better question may be:

How did that person reach a decision?

Search is becoming an interpretation problem

This was one of the clearest lessons for me this summer.

Discovery and credibility are becoming harder to separate.

An organization can be easy to find and difficult to believe.

It is equally possible for an organization with real expertise to be represented poorly by the systems people now use to understand a category.

That means visibility alone tells us less than it once did.

The more consequential question is:

What does someone conclude about us from everything they encounter before we ever get to explain ourselves?

That conclusion may be shaped by a search result.

Or an AI summary.

Or a physician quoted elsewhere.

Or a creator.

Or a Reddit thread.

Or a review.

Or the absence of corroborating evidence entirely.

What would make an audience hesitate to trust an organization online—even when the marketing appears polished?

That is increasingly a search question, too.

AI changes more than the interface

AI’s role in this environment is not simply that it answers a question instead of returning a list of links.

Its real advantage is compression.

Comparison, summarization, categorization and explanation can occur almost instantly.

That is tremendously useful.

It also means users can arrive at confidence faster than they arrive at verification.

That difference matters.

A clean answer feels easier than ten blue links.

A concise explanation feels more settled than an open browser containing six competing sources.

But ease is not the same as certainty.

The growing challenge for organizations is therefore not merely making information available to an AI system.

It is making expertise, evidence and relationships clear enough that what travels across these systems retains its meaning.

AI clarity and human clarity are not identical.

But increasingly, they require many of the same disciplines.

Who are you?

What do you know?

What evidence supports it?

Who else verifies it?

Who is this for?

What should happen next?

If an organization cannot answer those questions clearly for a person, optimizing the language for a machine will not solve the deeper problem.

Where this becomes practical

The question is no longer simply:

How do we rank?

It is:

How are we being interpreted?

A company can rank, appear in AI results, earn social mentions and still fail to shape the decision.

The more consequential issue is whether those environments tell a coherent story about who the organization is, what it knows, why someone should believe it and what someone should do next.

That is partly a search problem.

Increasingly, it is an interpretation problem.

And interpretation is influenced by far more than placement.

It depends on how easily information can be understood, whether independent signals reinforce the same story, whether the organization appears consistently across environments and whether a user can move from discovery to confidence without unnecessary friction.

In many cases, it also depends on how well other people explain you.

Bringing decision and action closer together

There is a meaningful opportunity here for leaders willing to rethink the sequence.

Not more channels.

Better alignment.

In practice, that often means reducing the distance between discovery, validation and action.

Sometimes that happens on your site.

Sometimes it doesn’t.

I have worked with clients to partner with creators who shape early understanding of a category, align messaging with adjacent brands in co-marketing efforts that reflect how users actually explore, strengthen third-party credibility and experiment with formats in which information and action are more tightly connected.

Not because every organization needs to chase a new platform.

Because behavior has changed.

Platforms such as TikTok and Instagram helped train users to expect discovery, explanation, validation and action to sit closer together.

AI has compressed that distance further.

What business leaders can take from this

What I find encouraging is that leaders are beginning to ask better questions.

Not simply:

“How do we drive more traffic?”

But:

“Where are decisions actually being shaped?”

“What role is each environment playing?”

“What is someone learning about us before they reach us?”

“Which signals are they using to decide whether we are credible?”

“Are we expecting our investments to do the right job?”

That last question matters especially with paid search.

SEM remains valuable.

But it no longer operates in isolation.

If AI is answering questions before a click, social is shaping expectations before search, reviews are validating claims and third parties are affecting confidence, then paid search has to be evaluated in that context.

Not as a standalone lever.

As part of a decision system.

Final thought

What my students first revealed was how differently platforms can answer the same question.

What another summer of teaching clarified is that the more important story may be how seamlessly people now move between them.

Some compare.

Some scroll.

Some ask AI.

Some verify everything.

Some accept the first plausible answer.

Most are simply trying to make a decision without thinking very much about the architecture underneath it.

For marketers, that changes the job.

The competitive question is becoming less about winning a single channel and more about whether the entire information environment helps someone understand you, believe you and act.

Because increasingly, the most important moment isn’t when someone clicks.

It’s when they decide.

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