Integrated Marketing Communication

The Better AI Gets, the More Students Need to Strengthen Their Thinking

Picture of Student mind maps MiDE Studio
~ K. Quesenberry ~ Leave a comment

Imagine a marketing student who hands in an A level case study. It has a solid situation analysis, competent competitive set, sound positioning, and reasonable recommendations.

Now imagine that same student 3-6 months later. They graduated with a high GPA and landed their dream job. Their manager asks them to analyze why sales have been declining the last year and make a recommendation.

The student freezes. Not because they’re not smart. But because something essential was never built. In the busyness of interviewing, getting ready to graduate and enjoying their senior year the temptation to get the quick answer from an AI prompt was too tempting.

The professor didn’t notice the first time. AI is getting better, AI checkers aren’t always accurate and AI use is more difficult to prove with tools that humanize AI writing. So the student used AI to do all the work for all case assignments. They thought they found the easy way to their dream job.

The thinking that should have happened was quietly outsourced to AI.

But the answers AI provides for well known text and HBR cases aren’t transferable to the unique current situation the company faces. The student didn’t learn to research, synthesize, draw insights, and apply critical thinking. They never learned to empathize with customers. They didn’t learn to use AI in ways to increase their value as an employee.

This is hypothetical, but something I think about as I consider how we teach in an AI-assisted world. The issue wasn’t using AI, it was using AI in the wrong way.

Right now, higher education is pulled between two camps. Prohibitionists see AI as a threat to academic integrity. Accelerationists think traditional learning is obsolete. Both sides are arguing about the wrong thing. The more useful question? When students use AI, is it making their thinking stronger or weaker?

Two books helped me see this more clearly: S.I. Hayakawa’s Language in Thought and Action and Angus Fletcher’s Primal Intelligence. Read together, they point toward a framework that’s more useful than a simple “allowed” or “not allowed” policy.

The Map Is Not the Knowledge

Hayakawa’s reminder, “the map is not the territory,” can apply to how students use AI. In a college course, the final deliverable is just a map. The territory is the cognitive struggle. It’s the connections made while wrestling with a real problem, the moments of confusion that eventually resolve into genuine insight.

In the student hypothetical, the case analysis is the map. The manager’s question about the decline in sales is the territory.

When a student writes a case analysis, the learning happens in the hard questions. Who’s this brand actually talking to? What do they feel when they see the ads and use the product? Are there new competitors? Has the market changed? Does the positioning hold up?

If AI answers all those questions, the student gets the coordinates without building the navigation skill. When that gap appears in the real world, it feels like personal failure. What happened is the thinking was outsourced at exactly the moment it needed to happen.

The grade is the map. The cognitive struggle is the territory. AI can help you understand the map, but only you can travel through the territory.

Your Brain Is Not a Recommendation Engine

This is where Fletcher’s work in Primal Intelligence becomes useful for how we think about student learning.

AI runs on correlation (A = B). It looks at what’s already been written and calculates the most probable next word, the most common next move. It’s a Data Brain that’s incredibly fast, but fundamentally a high-speed echo of the past.

Your brain runs on conjecture (A → B). You don’t just see two things are related. You imagine how one causes the other asking “Why?” and “What if?” in ways a correlation engine cannot.

AI can analyze 500 brand campaigns and tell you the most common recommendation. That’s correlation A = B. But only a student who’s spent time in the original data to draw insights from real consumers can ask: “Why are brands that lean into vulnerability outperforming ones that lead with aspiration?” That’s conjecture A → B. That’s the thinking that builds a marketer.

There is a kind of thinking (imaginative, causal, empathic) that AI cannot do for students. If they don’t practice it, they don’t develop it.

When you focus on the grade using AI to avoid the struggle, you lose the capability.

The 5 Levels of Classroom Integration

Instead of “using AI” or “not using AI,” there’s a more productive question. What level of integration serves the learning objective? Here’s a framework I’ve been developing:

A Five Level Multi-Value Approach to AI Integration in Student Learning
A Multi-Value Approach to AI Integration in Student Learning. Click on image to download a PDF.

Not every assignment should allow the same level of AI use based on objective and context.

Make the Invisible Visible

A useful tool that could have helped the hypothetical student is an AI Audit Log. Students record which tool they used, what prompts they gave it, what output they received, and how they verified, modified, or built on that output.

An AI audit log makes AI use visible instead of hidden. It makes students slow down and ask, Am I using this to avoid the thinking, or to deepen it? It also shifts the conversation from “gotcha” enforcement to a learning conversation.

You might ask students to log how they used AI to research a target audience, then trace where they went beyond the AI output. What did they verify? What did they challenged? What human insight did they add? The log becomes evidence of the cognitive work.

An AI Audit Log makes the invisible visible. It shows whether a student is building their thinking or outsourcing it.

Moving from “Gotcha” to “Growth”

The detect-and-punish model is understandable, but fights the wrong battle. What’s more beneficial is assignment design that makes the learning objective transparent and specifies which level of AI integration is appropriate.

Instead of: “No AI allowed on this assignment” (vague, unenforceable, adversarial)

Try: “For this brand audit, you may use AI at Level 1 (concept clarification) and Level 2 (brainstorming competitor categories), but Levels 3–5 are off-limits because the objective is to develop your own consumer insight framework. Document in an AI Audit Log.”

What Higher Education Should Develop

The hypothetical student in their first job isn’t underprepared in the traditional sense. They can define positioning and list the steps in the strategic marketing process. What they lack is the practiced habit of executing that process.

They also lack the habit of asking “Why?” when looking at market data. They never learned and practices the imaginative skill of moving from the abstraction down to the lived human experience of the consumer.

Picture of Student mind maps MiDE Studio
In Markets, Innovation& Design (MiDE) we teach marketing students Design Thinking in Business. They learn to navigate “messy” real-world situations sketching out concepts, processes and ideas to solve complex problems and foster a human-centric, empathic approach to innovation. Balancing analytic rigor with creative confidence increases career value with human skills less threatened by AI automation.

That’s when marketing, management and communications education is at its best. When students develop the ability to look at a spreadsheet and see the human story. When they have capacity to read a consumer insight report and sense what’s missing from it. Students who simply use AI to get the answer will never build the skill to make the imaginative leap from what the data shows to what the brand should do next.

AI can tell you what usually works in a category. It can’t tell you what your specific consumer is feeling right now, or why a campaign that followed every best practice still missed. That’s territory. And it requires a brain that has practiced traveling through it.

AI can tell you what usually works (correlation). Only you can imagine what should work next (conjecture).

For students: Look at your last assignment. Did you use AI to avoid cognitive struggle, or to sharpen your thinking? Your thinking skills are either getting stronger or weaker.

For professors: Look at your next assignment. What’s the learning objective? Which level of AI integration serves it? Can you write the instructions to name the level, explain why, and ask for an AI Audit Log?

The goal isn’t to police AI use. It’s to help students understand when they’re building their human brain skills and when they’re weakening them.

In a world where AI handles correlation, the students who know how to conjecture, imagine causal stories the data hasn’t seen yet, are the ones who will be valuable.

About This Post’s Creation

This post was developed in partnership with Claude. I provided the frameworks from Hayakawa and Fletcher, experience from my teaching, and the 5-level scale adapted for education. Claude helped organize and refine.

Stop Managing Marketing. Start Designing It.

~ K. Quesenberry ~ 1 Comment

We’ve been told the wrong story about marketing. A story of rigid funnels and siloed departments, where “strategy” is a slide deck of data while “design” is the final task of making things look good.

In practice, this model is flawed.

Management implies control, but in marketing, the factors out of your control far outnumber the ones you can manage. This narrative of control traps us in a product-oriented mindset that Theodore Levitt called Marketing Myopia. You’re so focused on current products, you don’t see the market changing and can’t imagine new futures.

You forget Philip Kotler’s sage advice: “Marketing is not the art of finding clever ways to dispose of what you make. It is the art of creating genuine customer value.”

The wrong story is marketing just sells things. The better story is great marketing designs solutions. It’s not a sales pitch to a faceless target demographic. It’s a well-crafted narrative to a persona that solves real human needs, what Clayton Christensen calls Jobs to Be Done (JTBD).

As copywriter Howard Gossage said, “People don’t read ads. They read what interests them. Sometimes it’s an ad.”

I believe design thinking is the best approach to keep this perspective. The most successful marketing follows more of a design process.

IDEO CEO Tim Brown explained, “Thinking like a designer can transform the way you develop products, services, processes—and even strategy.”

My Journey to a Design Approach.

I didn’t learn this in a textbook. I learned it through experience.

My dream was to design cars, but two semesters into an engineering degree, I realized it was all math and no magic. I didn’t know “industrial designer” was a job, so I searched. I tried business, but didn’t find much creativity there. I even snuck into an advanced poetry class, looking for a home.

I finally found it in an advertising copywriting class—the intersection of art and commerce. But when I graduated, I hit a wall. My program was siloed. The art department didn’t integrate with the ad department. I was a writer trained without design collaborators. My portfolio wasn’t good enough for Madison Avenue.

The solution was portfolio school. At Portfolio Center (Now Miami Ad School) the magic was built on an iterative process, imagination, and integration. As a writer, I was paired with art directors, designers, and strategists.

We solved marketing problems through consumer empathy, defining problems, creating ideas, sketching out concepts and testing them. By designing solutions and crafting engaging stories I landed my dream job at BBDO.

For 17 years as a copywriter and creative director I worked with top marketing managers and CMOs at startups to Fortune 500s. What I learned is the best marketing is born from deep human insight. A creative leap that does more than follow data, but leaps ahead to lead the market. Something we obtained best through a design process.

I’m excited to now be a part of the Markets, Innovation, & Design (MiDE) program at Bucknell University’s Freeman College of Management. It’s the culmination of my career—an innovative integration of business, marketing, creative, and design.

With the increase in AI, a human-centered design mindset is more important than ever. There’s an increase in jobs requiring design thinking and salaries for marketing managers with design thinking skills are higher. There’s also been a doubling of job listings seeking the skill of  “storyteller.” A needed antidote to AI slop that lacks genuine human connection.

A New Map for Marketing.

To stop managing marketing programs and start designing consumer solutions we need a new map. I created the visual framework below to teach my Marketing Principles students this unique perspective.

A visual marketing strategy process from a design thinking perspective.
I’m not against textbooks. I’ve written two! I use Philip Kotler’s Principles of Marketing for this class, but I tease out and layer in the design perspective that aligns well with Kolter’s original intent for the practice of marketing. Click on the image above to download a PDF.

This map isn’t a rigid set of steps. It’s a process that helps ensure every part of your strategy is grounded in a deep human insight by:

  • Inserting Empathy. Understand the human in the market as you analyze data from the market to inspire new solutions. Use tools like observation, empathy interviews, journey maps, bug lists, and POV framing.
  • Pivoting on Key Insight. Synthesize research into an “Aha!” moment that defines the problem in a human-centered way. The “job” they’re hiring the product to do (JTBD), or a cultural shift the brand can tap into as the plot for your plan.
  • Making a Creative Leap. Find inspiration. Ideate to undercover a Big Idea—the magnetic theme that makes your brand matter. Prototype, test for feedback, and iterate quickly. Share in an engaging Story.
  • Treating your Integrated Marketing Mix (4 Ps) as a System. Your product, price, store, and ads are not tactics. They’re all opportunities to live out the big idea and are chapters in your Brand Storytelling.

A Real-Life Example: The Airport Challenge.

What’s this look like in action? We were once tasked to fill seats on a new flight at a regional airport. The brief was simple: “Sell tickets.”

The problem? Consumers always looked for the lowest price and ended up driving hours to bigger, cheaper airports. A traditional, product-first approach would have been a losing battle.

Instead, we started with empathy. A cross-disciplinary team went to the airport and just observed. We noticed how easy it was. We parked across the street. Security took ten minutes. People were calm, not stressed.

Our key insight was that people weren’t hiring an airport just to get on a plane. They were hiring it to begin their journey. The value of that “job” was more than just the ticket price.

This led to our Big Idea, which came from our agency operations manager! The local airport code was MDT. She said, “It stands for the Money, Distance, and Time you save.”

That Big Idea became the core of our Story. Our digital team put a calculator on the website that showed the true cost of driving to the other airport. Our ad, PR and social teams created an engaging “MDT Challenge.”

Two local DJs raced to Chicago for a scavenger hunt—one from our airport, one from the big city competitor. Every live social media and on air update was a mini-story of hassle vs. convenience.

The result? Ticket sales on the new flight increased and overall ridership at the airport soared to its highest levels ever. We didn’t just sell tickets. We redesigned the way people thought about the value of their local airport. We led the conversation in the market to a new narrative versus following competitors into a price war we’d always lose.

Now It’s Your Turn.

The next time you’re tasked with a marketing challenge or opportunity, open a spreadsheet, but don’t forget a whiteboard to image new narratives. Marketing’s greatest power isn’t in the managing, but in the making.

Your work becomes infinitely more interesting when you stop asking “How do we sell this?” and start asking “What are we solving?” Instead of acting in the past you’ll begin imagining new futures and designing marketing actions to make them happen.

Your strategy will be better for it. Your career will be better for it. And the humans you’re designing for? They’ll thank you for it. For insights on how AI can help you in this process see my post “AI for Professionals: Deepen Your Expertise With AI, Don’t Outsource It.”

This Was 90% Human Generated Content! 

The initial ideas were my own, and so were all the life experiences and stories! I used regular Grammarly for proofing, Google Gemini 2.5 Pro Thinking and Anthrophic Claude Sonnet 4.5 for feedback – kind of like an idea partner and an editor. I created the graphic myself.