Reid Carr: Most marketers are still experimenting with AI. Today's guest didn't experiment. She transformed an entire business school around it. Poets and Quants called it the most consequential AI transformation in business education. So what does that actually mean operationally? And what happens when a CMO decides AI isn't a tool, but infrastructure? Today, we're joined by Katya Popova, Chief Marketing Officer at the Kogod School of Business at American University. Katya, welcome to the show.

Katya Popova: Thanks for having me.

Reid Carr: Absolutely.

Reid Carr: Well, so you've led marketing transformations at Rice, at Fortune 500 Company, and now at Kogod. When you look back at over 15 years, what's the pattern and what problem do you keep hiring people to solve? Is it brand? Is it systems? Culture? What is it?

Katya Popova: Yeah, it's interesting. I've been reflecting a lot on this question just because I'm curious about it. And what I've found is that I always get hired to fix a website, which is so ironic because that is just a question that I always get asked, "Can you fix the website?" But I've always thought that this is just a front to understanding how an organization actually works and what problems this organization has. So I think it's like the way to think about it is they hire me for the website, they end up with a new culture on the other side of it.

And I'm not just talking about what a website looks like. I think that's part of it, but it also has to do with how it's structured, what's the data infrastructure, what's the intelligence behind it. They don't really think about that when they hire me. They just say, "I need a new website," but that is the process that takes you through solving for a website problem. I love these projects for that reason.

So the common thread is I've always been hired for the website. The common result has always been on the other side of it. There's a different culture that thinks differently about what marketing does and what role it has to do with technology and with just where it fits within the entire organization. I think it's kind of my back door into fixing organizational culture.

Reid Carr: Yeah. So from a website standpoint, is that because you have to open every door and uncover everything? You find history and legacy?

Katya Popova: Oh my gosh, yes. Every closet is in the website, somehow present. It is fascinating to think of as a concept of how easy it is to slide into an organization through that problem. But yes, every single problem and every single opportunity is obvious through a website project because you have to organize yourself in every way. Technology, strategy, what is the actual value proposition of your organization? Every one of those things needs to be very clear in order for you to deliver a good website.

So I think it's such a great excuse for you to diagnose everything that's wrong with an organization and then come back and say, "This is what I fixed."

Reid Carr: Yeah. So you heard it here first. The way to fix culture is by building a new website.

Katya Popova: Hahaha hot take, sure haha.

What did managing 200-plus brands teach you about scale and discipline?

Reid Carr: Okay. Well, so looking back at NOV or National Oilwell Varco, you worked across 200 plus brands in a complex industrial environment. Now that's not glamorous marketing by any stretch. I think that's operational marketing, I would say. I mean, what did that experience teach you about scale and discipline that maybe most CMOs in higher ed probably don't have?

Katya Popova: Oh, goodness. I do think CMOs in higher ed do deal with a lot of complexity too. It might be just of a different nature. So I would never want to assume that they don't have it.

What I think that it did was it gave me a crash course on how to show restraint because there's a very strange thing when it comes to brand, right? You have to have a very strong, solid foundation, but it has to also flex. There's so many edge cases that require a decision for a brand all the time, especially when you have 200 plus brands.

And what I like to think of—you don't want the brand to wobble. Very technical term for it, but you want it to be able to flex. And the sad part of that is that I don't think this is a scientific formula. A lot of it is, "I'll know it when I see it" kind of thing.

So being dropped into a very big project like that, working with very experienced partners and learning along the way of this is how you flex without wobbling, that was the skill that came out of that experience. And it is a very, again, it's not a very scientific process. So unless you've had a chance to see, okay, we pushed it too far. This no longer is brand additive. It does not honor the brand, but you won't know it until you've experienced it.

So I think that experience just gave me the instinct to know what's a wobble versus a flex for a brand. And I'm so sorry. I know this is not a technical term, but this is how I envision it.

Reid Carr: No, I mean, I think that equates pretty well. You think about that in the sports idea, right? If you come from a strong foundation, your strength can come from that because it's very difficult in a wobble to have an athletic move come from that, that you don't break your ankles or something. So I mean, if you're building that strong foundation, I think that term, if it's not a technical term, it should be.

Katya Popova: There you go.

Reid Carr: Yeah, exactly.

What changes when you move from corporate marketing into higher education?

Reid Carr: So when you moved into higher education now at Rice and American University, did you feel like you were moving into a slower moving environment? I mean, what was the first kind of "this needs to change" kind of moment? And then how did you kind of accelerate through that with any kind of velocity?

Katya Popova: So yes, academia is slow. It's like whenever you move from corporate America into academia, you feel like the giant fish in a tiny pond.

But I will say upon reflection, it's slow for an interesting reason. So a corporation of the size that I used to work in for-profit was slow because there's just a ton of stakeholders that you need to bring along for big decisions. So we're talking about 60 countries' stakeholders to buy into what does one layout for an international page look like? That takes time, but it's a stakeholder management thing.

With academia where I ran into some issues is bureaucratic speed bumps. It's not the stakeholder speed bumps as much as it is bureaucracy. You got to fill out this form. You got to wait for this many RFPs to come back. You have to justify it in this way. The budget, let's talk about budget. How long does it take to approve money to justify the spend?

So what I've seen is that there are speed bumps in both, but the academic ones are bureaucratic, which are infuriating because you cannot manage your way around them. You just got to do it.

Probably one of the bigger aha moments for both of my academic experiences has been just the sheer lack of technology fluency. None.

Again, I get hired to do a marketing website and I say, "Well, we need a CRM." How are those two things created equal? But that is your way into understanding they don't have a marketing technology stack. Okay, so let's start there.

The first day that I had at American University, I asked the admissions team, "Okay, so how do you divide the enrollment funnel in stages?"

And I was given, "We don't have that."

So one of the first things that we sat down and did was, "Okay, let's talk about the enrollment funnel. What are the different stages? How do you define them? What do you measure as a conversion rate between them?"

These are the kinds of definitions that you need to start with.

But now we have a full stack of marketing automation, CMS, CRM. I mean, the sophistication has grown significantly, but it took a lot of work to build it from scratch because it just didn't exist.

Reid Carr: Well, I mean, and that's kind of the technical transformation...

What does AI transformation actually look like inside a marketing team?

Reid Carr: I think we also want to talk about the AI transformation over at Kogod as well. I mean, what you're talking about, there's the technical components of it, but you had to kind of change hearts, minds, and culture to get there.

Now, talking about AI transformation at Kogod, when we say AI transformation, are we talking about the tools layered on to marketing or redesign of how marketing actually functions? So maybe you can walk us through what changed structurally in the workflow, how you organize the team.

Katya Popova: So this is fun. We grew up along AI. So I think that the way we experienced AI implementation will probably be very different for someone who's just starting that journey right now, just because the sheer amount of tools available to you is mind blowing right now.

When we started, it was ChatGPT, right? "Oh, are you using ChatGPT?" "Not yet, let me start that."

But the way I think about it is there are three ways in which AI is very big for the Kogod School of Business.

Number one is the product itself. So we have really melted AI into everything that we teach our students, and that's a big product thing that we need to talk about to our students, to media, et cetera.

So the content area is one, but when it comes to us as a marketing team, there are two facets that are also very important.

Number one is obviously the tools that we use. So on average, I think that my team on a daily basis uses between 12 to 15 AI tools as we speak today.

And then there's the third stool leg, which would be marketing to AI. So accommodating for this idea that AI now is an audience you need to market to because the consumer behavior is shifting to that.

Reid Carr: I don't think people realize that as much either.

Katya Popova: Well, I mean, I think people are starting to realize that because I will tell you what, in my attribution analysis, I have AI chatbots as attribution to enrollments already. That's the original first-touch attribution that I see in my system, which blows my mind, but we are already seeing that.

And because the consideration cycle for my product is 18 months, I already see attribution for AI chatbots. So the consumer is there. So it's all about us keeping up with that.

And in terms of whether it's changed our workflows, the team has not changed its structure.

So we are using the same structure in terms of content creation, editorial.

What has changed are two things in my mind.

Number one is what it's done to unlock creativity for the team and just what the imagination does for the team.

Because what AI effectively does is it gives you access to cheap labor, right? It gives you access to scale that you may have never had before. I don't have five videographers. I have one. I don't have seven writers. I have two.

So having access to something that can just scale your productivity is fantastic.

But AI unchecked and without strong ideation behind it from a human is slop. I mean, this is nothing new that I'm sharing here. It is not valuable.

So our human-first approach still stands where we see AI as kind of relieving us from the strain of having to produce a ton with the limited humans that we do have.

So it finally opened up this area of possibilities of, "You know what? I can dream now. I can do some crazy stuff with AI and I have access to it for a very, very small fee."

That has just unlocked the potential of ideation so much.

And then the second thing that it's done is it's just kind of taking care of the mundane, crappy things that we don't want to worry about.

Note-taking, which sounds so stupid, but it's such a labor-intensive process.

We can have some brilliant ideas in a conversation and they'll be lost forever because nobody was taking notes.

That small example of, "My gosh, I had the tagline and I said it in the meeting and nobody can remember it," which is maybe not a good sign for a good tagline.

But the idea there is it captures so much that we'd lost in the imperfect human memory that we have.

And I think that this is a small example of how it can absolutely supercharge your capability of just being a human without worrying about the administrative part of it.

How do you integrate AI without breaking what already works?

Reid Carr: Yeah. Well, so I mean, now you were just talking about this, but maybe dig deeper into it.

CMOs, you want AI, but you don't want the chaos that can come from it.

How do you integrate AI into the existing... You talked about the people, but the tools, the CRM, the marketing systems without breaking what is already working?

Where did you start, and what did you not automate, I guess, is also important? And what resistance did you encounter along the way, whether technical or otherwise?

Katya Popova: I'm trying to think of the resistance.

Working at a university that produces a lot of original knowledge, there was that initial instinct of, "Oh no, I don't want AI involved in any of this."

But we've been very disciplined about the human-first piece.

So that's the one thing that always, I think, especially in the world that I live in, is always the first rule.

It needs to come from a human as an idea.

I do not outsource ideation to AI because I think it muddies the waters.

It naturally will lead you to the average answer and not the outstanding answer.

So AI is very powerful, but I just don't find it to be powerful enough for ideation and creativity.

The human holds the key to creativity and imagination, but then what AI can do is scale it for you and make it palatable for the 15 different audiences you want to approach that need that little tiny tweak for the idea that makes it more powerful for them.

So going back to where do you start in this world of overwhelm?

I think that the most important thing is to narrow the scope of a problem you're trying to solve.

Narrow it as much as you can so that you can experiment, put a hypothesis together, and test it out to see if it makes sense or not.

So that's the first approach.

One problem.

Note-taking, right?

Such a silly example, but it is an easy example to grasp.

Note-taking is a problem.

We suck at it.

We as creatives suck at note-taking.

All right, we got Notion. It's taking all the notes for us. It's giving us all our to-dos. So it feeds into our project management system.

We're done.

You don't have to worry about this.

But then when you pull back, now we are somewhat lucky that our technology stack is fully integrated.

I don't know if I'm allowed to say what it is, but I will tell you what it is.

It's HubSpot.

We can cut that out later on.

So HubSpot specifically rolled out AI tools within its suite, and we were able to enjoy that automatically without me freaking out that this is going to break something because the testing has been done before I've had to do it.

That said, I will note that before HubSpot did that, what I had been doing was extracting data from my CRM and then anonymizing it because I do have to work within certain parameters as a higher education marketer.

Anonymizing it and then working with the datasets within an AI agent or an AI chatbot to extrapolate some insights from it.

So the very first thing that I would recommend if you're just dipping your toes in the water is grab as much CRM data as you can.

If you have to anonymize it, go for it.

If you don't have to—if you have a paid plan that protects that anonymity for you—fantastic.

But start playing with your target audiences.

Let AI find the patterns for you because if you have large datasets, a human cannot possibly do that for you.

So deploy the AI agent, see what it sees, see what it comes back with, and then start playing with that.

I think this is a small way of you kind of walking into systems thinking without endangering your architecture, without you coding anything—heaven forbid.

We've heard the bad cases happening right now.

Don't touch that.

Just extract some data, play with it within an agent or chatbot, whatever feeds your soul, and then see what that comes back with.

Then you can ask follow-up questions, et cetera.

The other place that I would recommend doing this is use the audio and voice mode of any of the tools that you have access to and ask it to interview you.

Because that is one thing I would say AI is very good at.

It's not very good at telling you what's creative and what's good taste, but it's very good about asking you questions.

And you can ask it to ask you a hundred questions, and then you're still the source of the ideas, but then it can help you figure out what to do with those going forward.

Reid Carr: So now my job is no longer safe here behind this mic hahaha.

Katya Popova: None of our jobs are safe behind these mics, I think. Let's be honest about that hahaha.

Reid Carr: That's fair. Well, it was fun while it lasted hahaha.

What operational gains have you actually seen?

Reid Carr: Well, through all this, I mean, obviously people are looking for improvements and gains. Not that it's just efficiency. I mean, we're talking speed to campaign, lead scoring accuracy, cost savings. What's standing out to you without getting into specific insights that are unique to you?

Katya Popova: Well, lead scoring would be a great one to talk about because we didn't have any before. So now we actually have access to lead scoring that is really empowering our admissions team to prioritize their time.

In a world of a shrinking market like higher ed, it is an incredibly competitive market. It's expensive to market, and every year it's getting tougher and tougher.

So your admissions team is your sales team. This is the most precious resource you have because it's that one-to-one communication that really leads somebody from, "I'm interested in you," to, "I'm depositing and coming to your school."

So us being very respectful of their time is my number one priority.

We do have AI prioritizing all that for us.

So yes, it asked us what parameters we need to use to prioritize those leads, but it does it for us and it gives a sales/admissions person a very clear objective of, "I need to talk to these five people today."

That is huge.

We had attempted a human-based model of predicting, "Hey, if they opened this webpage, we should give them this score. If they opened this email..." It was highly ineffective.

Because we as humans have a tendency to be myopic, right? That's what I think is valuable.

But when you deploy an AI to think about what is actually a valuable activity based on all the attribution that we have access to in our system, you realize that not everybody's the same. Not everybody values the same assets as you do.

And that was fantastic for us to deploy.

Reid Carr: Yeah. Well, I mean, it becomes much more audience-centric.

I think that's where a lot of people get hung up is, "What are these things I have done, and how do I score what I have done?" which is the page view, the whatever.

But at the end of the day, it's how are we going to be most valuable to them?

Katya Popova: Exactly.

What are marketers getting wrong about LLM visibility?

Reid Carr: Well, okay. So going back to the LLM side of things—brand visibility—because we talked a little bit about that, is answer engine optimization.

For marketers listening, what's the biggest misconception about showing up in ChatGPT, Gemini, Claude, and the LLMs? What are people getting wrong?

Katya Popova: So this is fascinating. It is evolving as we speak.

Maybe I would say 12 months ago, it was all about what you say about yourself.

That's where engines took most of their information.

About 70% of what they gave you was based on what you said about yourself.

And it's evolved based on real-life feedback.

Now it's a lot more valuable what other people say about you.

So in higher ed specifically, that means media. That means rankings.

These are the third-party validators that agree with you that you're the best at something—or very good at something.

And answer engines tend to follow that lead.

They have a lot of incentive to be as correct as possible.

They get a lot of criticism for not always being correct, and there's a lot of incentive for them to continue improving.

The only way for them to do this is through validation.

The more validation there is about the statements that you're making, the better.

So one of the biggest strategies is yes, we are drafting the narrative of what we want to be known for, but then the heavy lift happens on PR.

The heavy lift happens on rankings for us in terms of validation.

We have a U.S. News & World Report ranking coming out in two weeks.

It's a huge deal for us.

I'm very excited about the result that we got this year, and we're going to make a big deal about this.

So that is the number one step.

But then the other step—there is a technical component to it.

You cannot ignore it.

These are machines that are reading your content, but they don't read like you and I read.

Having a basic understanding of how LLMs work is very, very helpful.

I will not be the one teaching you that because I'm not a professor in that regard.

There are lots of videos out there if you want to learn about how LLMs learn, because I think it's important for you to know how they learn.

What it really means is that you need to start writing a little bit differently to accommodate their style of reading.

So that's the number one thing that I took away and that we've been implementing across our website and all of our content areas.

That includes social media content, by the way, because that is a huge search engine these days.

So how do we write?

It takes a specific kind of writing.

Every paragraph needs to be fairly self-sustained.

So there are some technical components to it.

And then there's the schema piece, which is again a more technical term, but it's particularly the backend of the page.

It tells the LLM what this page is about.

That also needs to be optimized, and that has given us huge gains in our visibility into answer engines—not search engines.

I'm even correcting myself.

Reid Carr: Well, I mean, both technically.

Katya Popova: Absolutely.

A lot of SEO approaches work in AEO.

But I think that third-party validation is this new giant beast that if you're not ahead of it, you'll struggle to show up in chatbots.

And the last thing that I would say is chatbots love comparison queries.

They don't like talking about "the best."

They like to weigh their answers.

Compared to this.

Compared to that.

So investing in a lot of content that does that comparison for them—so they don't have to do it—is very helpful as well.

That's just a little trick of the trade that's making sense now.

It might not make sense when this podcast goes live.

Reid Carr: Hahaha yeah, exactly. We better get this out as soon as possible.

But then we're not talking about comparison engine optimization hahaha.

How should teams measure answer engine visibility?

Reid Carr: Well, then obviously on the backend of all this is the measurement component.

How are you—or how are teams—measuring LLM visibility?

Katya Popova: There are a few tools out there that can help you with that.

We use HubSpot AI Optimizers ourselves, but there are a few tools.

I think that you guys provide a tool too, which to me is so precious and so important.

We tend to do this on a monthly basis.

We track how we rank on three engines right now.

We look at Perplexity.

We look at Google Gemini.

And we look at OpenAI, so ChatGPT.

These are the three that we follow the most.

It's fascinating to see how we increase in one, decrease in another.

You're not really gaming it.

You're really trying to improve across all three.

So seeing that movement is where I think you have to focus.

There are tools out there.

I highly recommend people look into them.

It is something that we have folded very systematically into how we evaluate success.

I also look at our referrals on the website.

It's growing right now.

I'm getting a ton of traffic from OpenAI.

This is by a thousand times compared to the other engines.

So I have a lot of work to do on Google.

A lot of work to do on Gemini.

Reid Carr: Yeah. I mean, that's the game we're playing.

We used to play that game a lot more in SEO, and then Google became the dominant force.

Now we're back in that world again.

It's also interesting because you're probably, to some degree, also battling accuracy.

People can come in and be referred to you by one of these engines, but also arrive based on inaccurate information if it's not managed properly.

So are you doing anything to monitor the accuracy of how these engines are talking about you?

Katya Popova: Oh, that's a great question.

Not so much on the agents, I'll be very honest.

I think it's a fantastic question.

But I will tell you a funny story.

We do have an AI agent on our website, which is technically trained on all of our webpages.

There's a bunch of backend documentation for it.

It's hypothetically a very well-trained chatbot.

Just today I got a note saying, "Hey, your chatbot said your Amazon marketing program starts in the spring—and it doesn't."

Reid Carr: Oh, no.

Katya Popova: So okay.

I mean, it is to some degree a black box.

Which is why there's always a statement that says, "Check with a human after you get this answer."

So we do have some parameters for keeping track of whether our AI chatbot is telling the truth or not.

We're told when it's not.

But the accuracy on the external engines—I don't have a solution for.

I don't know if you do, but not at this point.

Where is the modern CMO role headed next?

Reid Carr: But I think this starts to speak a bit to where things are going. You're operating at the intersection of marketing, technology, and academia, which tends to be a place where a lot of this stuff is studied or dreamed up.

Where do you think the modern CMO role is headed? Is this about revenue operations, system architecture, AI ethicist, change manager? There are all these different roles.

Katya Popova: The Swiss Army knife of everything, right?

Reid Carr: Well, I mean, I think that's what a lot of CMOs have historically seen themselves as.

But it was interesting because some of the earlier stuff you said was that there's all sorts of infusion of classic marketing—PR, messaging, building the website. All those things remain critically important.

But now we're headed in this new direction where there are underpinnings of technology, AI, management, and culture.

You brought that up with the website.

So you can't expect the CMO to wear all the hats, but it sounds a lot like they are.

Where do you think it's headed?

Katya Popova: Yeah. Okay. Another hot take.

I actually think it's going to be more about brand than anything else.

AI is making things cheap.

It's commoditizing everything.

So the fact that you are able to produce a hundred variations of your long-form content piece makes content cheaper, and there's going to be a lot more of it available.

So when you strip away that commodity, what's left?

That becomes where the humans are.

What do humans buy?

Watch Simon Sinek's TED Talk.

They buy why you do things—not what you do, not what your differentiators are.

They buy why you do things.

Value-based marketing.

So what your brand story is is going to become even more important in this world.

Now, I'm not saying any of the other things you mentioned aren't important.

If you don't know how to put together a marketing technology stack, okay, maybe you don't as a CMO—but hire somebody who does.

The technology component needs to be there.

You need the intelligence to understand what's working and what's not working.

You need the technology.

But I would say if you don't have a good story to tell, no technology is going to save you.

So I personally think that we are moving more and more toward branding—not just overall as a CMO responsibility, but also in paid marketing.

I think we're moving away from "Fill out this form and I'll put you into my email campaign."

I think we're moving more toward authentic storytelling that's really rooted in brand truth.

That's what's going to make the difference.

So as a CMO, you better find all those people who speak that truth and be able to capture it.

I think that's where it's headed.

I don't know if I'm right, but I do think that's the human part of a brand, and that's what's going to make it or break it going forward.

Reid Carr: Well, I couldn't agree more.

The human part of it is the one thing we continue to talk about.

The human in the loop.

The humans who buy.

The emotional components.

Technology is solving for a lot of everything else.

It probably depends on the size of the organization and what skill sets you have.

Some people will hire for these skill sets.

There are a lot of marketers out there who are kind of a one-person shop saying, "Boy, I've got to learn all this stuff."

The way they're going to execute will probably be through AI that's good enough for the moment.

Whereas when you're dealing at the upper levels of marketing, you know who your competitors are.

You're facing off like it's the difference between pee-wee football and the NFL.

You really do need all the tools at that point.

And I think one thing I've learned from the marketers we work with at larger companies is that so much of it comes back to understanding the consumers they're speaking to, as well as the people on their own team and supporting them to do their best work.

Katya Popova: Absolutely.

Reid Carr: Underneath all of that will be technology and tools.

Strategy.

And now we've got note-taking tools so everyone can spend more time using their minds and dreaming bigger dreams.

Execution becomes the challenge, and that's going to be done through people.

Katya Popova: One hundred percent.

I think one of my favorite parts of running a team has been creating a safe space to fail.

And I think you'll relate to this because I know this is such a huge component of your organization.

Reid Carr: Yeah.

Katya Popova: Obviously, I'm not saying make the same typo five times.

We're talking about having a hypothesis, testing it out, and failing.

You'll learn more by failing than anything else.

I think that kind of environment is something every CMO should create.

That feeling of safety.

People need to feel like they can experiment and try new things.

Because that's the defining moment of what AI is bringing to us as professionals.

There will be a lot to try.

You won't get anywhere unless you're willing to fail along the way—because you will fail along the way.

Not threatening people with that is what incentivizes them to experiment.

Reid Carr: Well, it's interesting.

Academia is one of those places where I think that's already part of the culture.

Katya Popova: That's right. That's why it works with faculty.

Reid Carr: Exactly.

They're working with students every day.

They're not expected to get everything perfect.

That's why they're there.

Katya Popova: That's right.

And by the way, that was the aha moment for me as a professional working in academia.

The moment I stopped trying to be the expert telling everyone what to do and instead framed things as questions I was trying to answer.

"This is my hypothesis. Let's see if it works."

Sometimes I'm wrong.

And honestly, there's no happier moment than saying, "Man, I was wrong about that."

I relish those moments because they're so eye-opening.

That was the breakthrough for me in terms of engaging with stakeholders.

Faculty understand hypotheses and testing.

The moment you frame it that way—even if it's a crazy idea that you have plenty of—it becomes something you're willing to test.

If you have a plan for how to test it, then even if you fail, it's still a celebration because you've learned something important.

Reid Carr: An important nuance there is that sometimes you're successful for a completely different reason than you originally thought.

Katya Popova: There you go.

Reid Carr: You have something to test against.

You realize, "Well, good news—we were successful."

And even better news—we learned why it actually worked.

It wasn't for the reason we originally thought.

We questioned ourselves.

We kept an open mind.

Again, academia is such a fantastic place for that.

Absolutely.

What is overhyped, and what are marketers still not paying enough attention to?

Reid Carr: What do you think is genuinely overhyped in this space, whether it's AI or marketing in general? And what are marketers still not paying enough attention to? We talked about brand—that's a big bucket—but are there other things that are over- or underhyped?

Katya Popova: Something that broke my personal heart was that I grew up thinking polish and gloss are what sell.

If you look professional, if you look like you're punching above your weight, that's the recipe for success.

We have absolutely graduated from that.

If your audience sniffs marketing at this point—especially the audience that I'm talking to, which is 17-, 18-, and 19-year-olds—they sniff marketing a mile away and run away.

That is so counterintuitive to me as a millennial.

It has been drilled into me that everything has to look incredibly polished.

Don't get me wrong—there's a place for polish.

A legitimate organization needs some polish.

But when it comes to what actually sticks as a message, polish is the least effective thing.

We saw this five or six years ago.

Which emails performed best?

The beautifully designed marketing emails?

Or the plain-text emails?

We got a lot of data from that.

We know plain-text emails often outperform beautifully designed marketing emails.

Why?

Because they feel genuine.

People think, "Oh, there's a human talking to me."

Take that idea and multiply it by ten in today's world.

Especially in higher education.

UGC—user-generated content—is king.

That is the only thing we'll be investing in for a very long time because it's the true testament of what the product actually is.

Nobody believes the marketer's message.

Everybody believes somebody who goes on TikTok and says:

"This is what my dorm looks like."

"This is how I clean my dorm."

"This is what my roommate is like."

"This is what the dining hall is like."

That's what prospective students want to see.

I cannot give them a glossy picture of an empty dining hall.

That is not what sells the experience.

That has been a tough lesson for me because of how I was trained to think about marketing.

But it's been refreshing because I was wrong.

I've learned that this is simply the wrong approach.

It's been so much fun working with students to produce the kind of content that performs well with their peers.

Every brand can learn from that.

Every beauty brand you can think of starts on TikTok nowadays.

That's where they thrive.

It's not the glossy advertisement.

It's the content creator trying on the lip stain and loving it.

That's what sells the lip stain.

Nothing else.

Those lessons have been tough, but incredibly important.

Lots of kudos to Gen Z for bringing us home on this.

Reid Carr: Yeah.

We've been around since before social media as an agency.

I remember trying to convince clients to get into social media.

Back then it was Twitter after MySpace.

Clients would ask, "What if people say something bad about our product?"

They were already saying it.

Now we're finally answering that question they had back then.

There are going to be problems.

How you respond to them—and because of that authenticity—that's actually what creates a better relationship with your brand.

Katya Popova: One hundred percent.

Lean into it with authenticity.

Reid Carr: Lean into it—and actually respond.

Not just with, "I'm sorry you had a bad experience."

Actually fix the problem.

Clean the restaurant bathroom.

Change something.

The brands that did that are the ones that are still here today.

Katya Popova: Because they're authentic.

I'm always thinking about RyanAir's TikTok account.

It's such a masterclass in leaning into your own narrative.

They love poking fun at themselves.

It's such a strange and bold thing for a brand to do.

"Let's show everybody how much we suck sometimes."

The humor...

It's not for everybody.

But it's such an extreme example of knowing exactly who you are.

That self-awareness.

It's just like a person.

If you're comfortable in your own skin, whatever that looks like, that confidence is attractive.

I think it's the same thing with brands.

RyanAir is very comfortable with who they are.

It's attractive.

You're like a moth to a flame.

You love seeing that confidence.

Reid Carr: Well, it comes back to being human.

Which AI tools are actually worth using right now?

Reid Carr: A couple quick questions for you.

What's one AI tool you can't live without right now?

Katya Popova: I was just talking about this because it changes so often.

Can I give you three?

Reid Carr: Sure.

Katya Popova: I've been really fanning over Claude because it's just so good.

I have Claude installed everywhere, and it's fantastic.

A very close second is NotebookLM.

That Google product is so good.

Anytime I need to prepare for something, I throw everything into NotebookLM.

I even listened to a few things on the drive here in podcast form.

The third would probably be a toss-up between Claude Code and Google AI Studio.

My team has been building apps.

I have zero developers on my team.

And they've built several functional apps that we've deployed to customers.

That absolutely blows my mind.

I'm so excited about it.

None of us are developers.

Those three are very critical for me.

But I'm sure the next time we talk it'll be different.

Reid Carr: Well, that's the expectation.

What skill or practice will matter most over the next 24 months?

Reid Carr: What's one skill marketers need to build over the next 24 months to set themselves up for success?

Katya Popova: Is it a skill—or is it a practice?

Because I don't think what I'm about to say is really a skill.

I think we all have it.

It's putting it into practice consistently.

And that's having a clear definition of the problem you're trying to solve.

AI is now making it so easy to solve problems.

The problem is we're often not very clear about what we're solving for.

The more clarity you have—and the more granular you are about the problem—the more unstoppable you'll become.

Putting that into practice daily or weekly.

What am I trying to solve this week?

What am I trying to solve this month?

I don't think many of us have that habit.

That's the one thing I'd focus on.

Dedicate time to thinking through the actual problem.

Because now you have the tools to solve it.

Reid Carr: That's interesting because most people come in thinking they already know the problem.

Usually they show up with a tactic.

Katya Popova: A website.

Reid Carr: Exactly.

That's how we started this conversation.

Then you realize what the real problem is.

Or the process uncovers five more problems.

Sometimes you get lucky.

Most of the time you have to step back and ask,

"What's the actual problem?"

"Why do you think that's the solution?"

Katya Popova: A website is the perfect tool for uncovering that.

Reid Carr: Exactly.

Final Thoughts & Key Takeaways

Reid Carr: Well, this has been a rare look inside what it actually takes to build AI infrastructure inside a marketing organization—not just use the tools, but redesign the system.

Katya, thank you for sharing what's working and what comes next.

If today's conversation gave you something to think about, check out the show notes at www.redoor.biz/learn.

Subscribe to The Marketing Remix wherever you get your podcasts, and follow Red Door Interactive on LinkedIn for more.

Thanks, Katya.

Katya Popova: Thanks for having me.