AI Is Making Everyone a Marketer. That Doesn't Mean Everyone Is Building a Brand.
We’ve been thinking a lot lately about something we’re seeing more and more. AI has basically given everybody the ability to make something. An account manager can write a script. A marketing manager can generate a campaign. A founder can build a landing page. An executive can ask ChatGPT or Claude to help develop an app, redesign a website, create a presentation, or build content.
And we think that’s a good thing.
The problem isn’t that everybody suddenly has access to these capabilities.
The problem is that we’ve started confusing the ability to make something with knowing what should be made.
And those are two very different things.
Everything is starting to look pretty good.
That’s actually what worries us. If AI produced terrible work, this conversation would be easy. But it doesn’t. It produces things that are increasingly polished. Websites look clean. Videos look cinematic. Copy sounds professional. Presentations look designed. Apps function. Social posts look like social posts.
Everything is becoming pretty good, and pretty good is becoming cheap. But we’re entering a world where everything can look better while fewer things actually mean anything.
There’s research behind that concern. A Science Advances study found that access to generative AI could improve an individual’s creative output while simultaneously making the overall collection of ideas more similar. In other words, AI can make each person better while making everybody collectively more alike. Another study looking at thousands of pieces of writing found a similar homogenizing effect.
That should matter enormously to anyone responsible for a brand, because the entire point of building a brand isn’t to become more like everyone else. It’s to become more recognizable as yourself.
We’ve seen a version of this before
Think about what Canva did for design. Canva was an incredible democratization tool. Suddenly a teacher, salesperson, office manager, or small-business owner could create a presentation, flyer, or social media graphic without knowing Photoshop, Illustrator, or InDesign.
That created enormous value. But having Canva didn’t suddenly give somebody 15 years of experience in typography, art direction, visual hierarchy, UX, photography, or brand development. You could make something that resembled professional design, and sometimes something very good, but there was still a difference between that and work created by somebody who really understood what they were doing.
AI is doing the same thing now, except at an entirely different scale. The difference is also getting harder to see because the output has become so sophisticated. The technical barrier is disappearing. The judgment barrier hasn’t.
We think we’re losing some of the most important conversations in marketing
We remember having a lot more conversations that started with questions like: Who are you? What does this company stand for? Why should somebody care? What can this brand credibly own? How should people feel when they interact with you? What does your customer really need? What should someone recognize immediately as yours?
Those conversations weren’t always exciting. They didn’t involve a demo of the newest technology, and you couldn’t turn them into 40 pieces of content overnight. But those conversations built brands.
Today, we increasingly hear different questions. Can we build an app? Can AI make this video? Can we generate more content? Can we automate this? Can we redesign the website with Claude? Can we create ten more versions?
Those are all valid questions. But somewhere along the way, “Can we make it?” has started replacing “Why are we making it?” And this isn’t just a theoretical concern about AI. We can already see what happens when established brands become overly focused on activity, promotions, collaborations and short-term attention while losing sight of the things that made the brand valuable in the first place.
Wendy’s is a timely example. In the second quarter of 2026, Wendy’s U.S. same-restaurant sales fell 7%, U.S. systemwide sales declined 8.2%, and Burger King moved ahead of Wendy’s in U.S. system sales. Wendy’s new CEO, Bob Wright, openly acknowledged that the company had weakened some of the fundamentals behind the brand.
What caught our attention wasn’t simply the sales decline. It was how Wendy’s described the problem. Wright said the company had allowed cost and efficiency decisions to weaken its quality differentiation, while its marketing had become “over-reliant on a calendar of one-off promotions and collaborations” instead of maintaining a consistent and relevant brand narrative. One of those initiatives, a Minions and Monsters promotion, failed to generate the sales the company expected.
That’s an important lesson because Wendy’s is not a brand with an identity problem on paper. This is a company with decades of brand equity, distinctive assets, a recognizable voice, and a famous product proposition built around fresh, never frozen beef. Yet even a brand that established can start losing its way when the marketing calendar becomes more important than the brand narrative.
And to be clear, Wendy’s problems weren’t caused by marketing alone. The company has also pointed to traffic declines, its value proposition, restaurant economics, operating decisions and quality as issues it needs to address. But that actually makes the example more relevant. Brand is not just advertising. The product, experience, value proposition, creative, operations and communications all reinforce—or weaken—the same promise.
This is where we see a direct connection to what is happening with AI.
AI makes it incredibly easy to create the next thing. Another collaboration. Another social post. Another landing page. Another campaign. Another video. Another “idea.” The danger is that organizations can become extraordinarily productive while becoming less intentional.
Technology didn’t create that problem. It just removed almost all the friction that used to slow it down.
For us, that’s the bigger concern. A brand can become very busy without actually becoming stronger. It can produce more content, launch more ideas, follow more trends and experiment with more technology while slowly moving further away from the reason customers cared about it in the first place.
The question shouldn’t only be: What can we make next?
It should still be: Does this make the brand more recognizable, more meaningful and more valuable?
And in an AI world, we think that question becomes more important, not less.
The easier content becomes to create, the more important brand becomes
Generative AI is spreading creative capability across entire organizations. Again, we think that’s a good thing. Marketing managers should use AI. Account managers should use AI. Executives should use AI. Creative teams should absolutely use AI. We use AI every day.
The issue starts when hundreds of decentralized decisions begin entering the marketplace without a strong brand system governing them. One person writes something with ChatGPT. Someone else generates a visual. Another person creates a presentation. Someone in product redesigns a section of the website. Someone in sales makes a video. Someone else uses another model to create an email.
Individually, every piece may look fine. Collectively, the brand slowly stops sounding like one company.
That’s something businesses need to pay much more attention to. You don’t destroy a brand overnight. You dilute it one perfectly acceptable piece of content at a time.
Consumers notice more than we think
Recent advertising research has shown something interesting. Consumers don’t necessarily hate AI-generated creative. In many cases, they can’t even tell the difference. That isn’t necessarily the victory it sounds like.
AI-generated work can look completely credible while still being less distinctive, less imaginative, and less emotionally effective. That distinction matters because AI is becoming extraordinarily good at understanding what advertising looks like. That doesn’t necessarily mean it understands why a great advertisement works.
The same applies to branding. AI doesn’t automatically understand your tone, your history, your customer, your competitive context, your values, or the distinctive assets you’ve spent years creating. You have to put those things into the system.
The real risk is not bad creative. It’s organizational drift.
For a mature company, the challenge is much bigger than whether an AI-generated ad uses the right colors or sounds exactly like the brand voice. The real question is what happens when AI dramatically increases the number of people, departments, agencies, platforms, and systems capable of making decisions that affect the customer experience.
Marketing creates a campaign. Ecommerce changes the website. Sales develops its own materials. Product builds a new experience. A regional team adapts the messaging. Someone experiments with a new AI platform. Another team builds an internal tool. Individually, each decision may be reasonable. The problem emerges when nobody is looking at what all of those decisions are adding up to.
That is how brand dilution happens at an enterprise level. Not because someone used the wrong font, but because the organization gradually stops making decisions from the same strategic center.
For leadership, that matters because brand is not simply a marketing asset. It influences what customers believe you are worth, why they choose you instead of an alternative, whether they come back, how easily a new product can be introduced, and how much money has to be spent continually reacquiring attention. A company can improve operational efficiency while simultaneously weakening the differentiation that makes those efficiencies commercially valuable.
Wendy’s is a useful example precisely because its problem wasn’t that the company suddenly forgot how to make advertising. The larger issue was that a series of decisions around promotions, value, product quality, cost, and communication stopped reinforcing the same proposition. What looked like separate operational and marketing decisions eventually became one brand problem.
AI has the potential to accelerate that dynamic considerably.
AI introduces a new governance problem for CMOs and CEOs
Until recently, there was natural friction in the system. Creating a new campaign required a creative team. Building a new customer experience required developers. Producing twenty videos took time and money. Launching a new platform required meaningful investment.
That friction sometimes slowed companies down, but it also forced decisions.
AI removes much of it.
A company can now generate hundreds of pieces of content, create campaign concepts overnight, rapidly modify customer journeys, build prototypes, spin up landing pages, personalize communications, and develop internal technology at a fraction of the historical cost.
That is an enormous competitive advantage when the organization knows what it is trying to accomplish.
Without that clarity, it can become an enormous fragmentation engine.
The question for leadership therefore isn’t simply, “How do we get our organization using AI?” It is also, “What decision-making system governs what AI helps our organization produce?”
That includes what the brand stands for, which customer problems matter most, what differentiates the business, what should remain consistent, where teams have permission to experiment, which decisions require human judgment, and what the company simply will not do.
This is no longer just a brand-guidelines conversation. It is becoming a management and governance conversation.
Technology creates the same tension
We are seeing a similar pattern in technology.
The discussion has quickly moved from using AI to create content to using it to build software, ecommerce experiences, dashboards, websites, internal platforms, media tools, and customer-facing applications. The speed of development can be extraordinary, and companies should absolutely take advantage of it.
But faster development does not eliminate the questions that experienced technology organizations have always had to answer. It makes those questions more important.
Should we build this at all? Is this capability actually differentiated? Is there an existing ecosystem that already solves 80 or 90 percent of the problem? What will this need to integrate with? Who maintains it? How does the data flow? What happens when the business scales? What is the security model? What happens when the person who built it leaves? What is the opportunity cost of maintaining proprietary infrastructure instead of investing that capital somewhere the company has a real competitive advantage?
Those aren’t coding questions.
They’re business questions.
And this is where the availability of AI can create a subtle problem. Something becoming technically possible does not automatically make it strategically intelligent.
A three-person development team can now create something surprisingly sophisticated. That is impressive. But producing the first version of a platform is very different from operating a durable system that sits inside a larger business ecosystem.
The same applies to marketing. Producing an advertisement is different from understanding media economics. Building a website is different from designing a customer journey. Generating content is different from creating brand memory. Building a dashboard is different from knowing which decisions the data should drive.
AI is compressing the gap in capability. It is not eliminating the gap in expertise.
The build-versus-buy decision is becoming more important, not less
This deserves particular attention from founders and CFOs.
AI makes custom development dramatically cheaper, which naturally makes building internally more attractive. But the cost of writing the software has never been the entire cost of owning software.
There is implementation, integration, maintenance, security, training, analytics, documentation, platform changes, technical debt, and the opportunity cost of continuously supporting something the company has decided to own.
Sometimes owning that technology is absolutely the right decision. If the capability creates genuine competitive advantage, contains proprietary knowledge, materially improves the customer experience, or allows the business to operate in a way competitors cannot easily replicate, building can create significant value.
But rebuilding commodity infrastructure simply because it has become easier to build can have the opposite effect.
A retailer abandoning an ecosystem such as Shopify, for example, isn’t simply choosing between two websites. It is choosing between participating in an ecosystem that continuously invests across payments, checkout, fraud, analytics, integrations, commerce infrastructure, AI and distribution, versus assuming responsibility for some portion of that ecosystem internally.
The strategic question isn’t “Can our team build this now?”
It is “Is owning this capability where we create competitive advantage?”
That is a very different standard.
This changes what companies should expect from agencies
It also changes what an agency needs to be.
If an agency’s primary value is its ability to produce assets, AI is going to put enormous pressure on that model. Production is getting faster, internal teams are becoming more capable, and many things clients once needed agencies to physically make can increasingly be produced inside the organization.
We don’t think agencies should fight that transition. We think they should move up the value chain.
The opportunity is increasingly in helping leadership make better decisions across a much more complicated marketing environment: understanding the customer, identifying the growth opportunity, defining the positioning, finding the insight, connecting brand and performance, determining where technology creates leverage, deciding what should be automated, and ensuring that hundreds of executions still build toward one commercial objective.
That changes the relationship.
The question stops being, “Can you make this for us?”
It becomes, “Can you help us determine what deserves to be made, where we should invest, what the data is telling us, what the customer is telling us, and whether all of this activity is actually making the business stronger?”
That is a much higher-value role.
Execution is becoming abundant. Judgment is becoming scarce.
We think this may ultimately be one of the biggest changes AI brings to marketing.
For decades, much of the cost of marketing came from execution. Producing the commercial was expensive. Building the website was expensive. Creating all of the variations was expensive. Analyzing huge amounts of information was difficult. Testing ideas required significant resources.
AI is reducing those constraints.
As execution becomes abundant, the economics begin to shift toward the quality of the decisions behind that execution.
Which market should we enter? Which customer matters most? What should we stand for? Which problem deserves investment? What insight is actually meaningful? Which technology should we own? Which capabilities should we rent? What deserves human attention? Where can AI create leverage? What should remain consistent for the next five years rather than change next quarter?
Those are not prompts. They are management decisions.
And the quality of those decisions will increasingly separate companies that simply become more productive with AI from companies that actually become more competitive because of it.
The answer isn't less AI. It's more clarity.
We don’t believe companies should slow down their adoption of AI. Quite the opposite. The productivity improvements, creative possibilities, analytical capabilities, and speed of experimentation are too significant to ignore.
But speed needs direction.
The companies that benefit most from AI won’t necessarily be the ones generating the most content or building the most internal tools. They will be the companies that are clearest about where they create value and use AI aggressively around that understanding.
For marketing, that means strong positioning, real customer insight, differentiated brand assets, a clear commercial proposition, consistent measurement, and an organizational understanding of what the brand is trying to build over time.
For technology, it means being equally disciplined about where proprietary development creates competitive advantage and where established platforms provide better economics, scale, integrations, or infrastructure.
And for leadership, it means recognizing that AI strategy cannot exist separately from business strategy.
We are going to hear a lot about AI transformation over the next few years. We think the more important question for leadership may be: What becomes more valuable when everybody has access to the same extraordinary technology?
Our answer is increasingly clear.
Not access to the tools.
Clarity of strategy. Quality of judgment. Depth of expertise. And the ability to make thousands of faster decisions without losing sight of what makes the business different in the first place.
That is where we believe the real competitive advantage will be.