Don’t Be Misled By Data

 

Somewhere along the way, marketing forgot how greatness actually happens.

We started treating dashboards like oracles. We mistook metrics for meaning. And we convinced ourselves that if we just listened harder to the data, the comments, and the focus groups, we’d stumble into brilliance.

That’s not true. It never has been.

Your audience is not the source of the next breakthrough. They can only describe what already exists. They can’t imagine what they haven’t felt yet.

Data doesn’t create culture. It documents it … after the fact.

When you invite data into the creative process too early, it doesn’t sharpen ideas. It sands them down. It rewards familiarity, penalizes risk, and quietly pushes everything toward the center. Safe. Polite. Forgettable.

That’s how brands spend millions and still disappear.

The brands people believe in don’t ask for permission. They don’t optimize their way into relevance. They decide what they stand for, build a world around it, and let the right people find them.

Liquid Death didn’t win by playing it safe and Apple didn’t win by consensus. They won by conviction.

Here’s the uncomfortable truth: If everyone likes your brand, no one loves it.

Love requires edges. It requires taste. It requires the courage to be misunderstood. Cult brands aren’t built by chasing approval, they’re built by expressing belief so clearly that the right people feel seen and everyone else self-selects out.

That’s not arrogance, it's leadership.

Metrics have a role, but not where most brands put them. Data belongs in distribution, not creation. Use it to amplify what you’ve made, not to decide what’s worth making.

Because algorithms don’t start movements. People do.

If you want attention, optimize. If you want devotion, decide.

Stop asking what the audience wants. Create from belief.

Stop chasing relevancy and start creating gravity (and gravity doesn’t ask for permission).

How to Respond Like a Career Politician

 

There’s a special dialect spoken in the marble hallways of power and the carpeted conference rooms of the C-suite. It’s fluent, confident, and utterly empty. 

It sounds smart. It feels responsible. It gives the comforting illusion that someone, somewhere, is in charge … while carefully saying absolutely nothing. 

This is the language of professional non-answers: a beautiful, aerodynamic form of bullshit designed to glide past accountability without ever landing on a real position. If you’ve ever listened to a politician or business executive talk for five minutes and realized you learned nothing, you’ve already heard it.

Below is a field guide to that language ... a greatest-hits list of phrases that masquerade as thoughtful leadership while doing the far more important job of protecting the speaker from risk, commitment, or reality. Memorize these and you too can sound wise, measured, and deeply engaged … without having to actually think, decide, or say anything at all.

"I'm not saying I'm for or against this - quite the opposite". 

 

"We need to have a serious conversation about this moving forward."

 

"I think we can all agree that this is a complex issue with valid concerns on both sides."

 

"At the end of the day, we have to circle back to our core values and leverage our strengths."

 

"Let me be clear: this isn't about choosing between A and B, it's about finding the right balance."

 

"We're committed to a holistic, 360-degree approach that takes all stakeholders into consideration."

 

"I'd caution against false choices here. The real question is how we move the needle on outcomes."

 

"Look, I think the data speaks for itself, and we need to let the process play out."

 

"This requires a nuanced approach that doesn't lend itself to soundbites or simple solutions."

 

"We're laser-focused on delivering results while ensuring we do this the right way."

 

"I'm not prepared to speculate, but what I can say is that we're exploring all available options."

 

"The bottom line is we need to be strategic and thoughtful as we navigate these headwinds."

 

"I hear what you're saying, and I want to be transparent: this is something we're actively monitoring."

 

"We're taking a data-driven approach while also listening to the voices that matter most."

 

"I think it's important we don't get ahead of ourselves here. We need to let the facts guide us."

 

"This is about building sustainable frameworks that create long-term value for everyone involved."

 

"We're cautiously optimistic, but we recognize there's still work to be done."

 

"I'm not going to litigate the past. What matters is where we go from here."

 

"We need to have guardrails in place while also not stifling innovation and agility."

 

"At this point in time, we're focused on aligning our priorities with stakeholder expectations."

 

"Let's not lose sight of the bigger picture while we're dealing with the tactical realities on the ground."

 

"I think reasonable people can disagree, but we all want the same thing at the end of the day."

 

"We're committed to transparency and accountability as we work through this process."

 

"This isn't a binary question … we need to thread the needle between competing imperatives."

 

"I want to be very careful not to prejudge the outcome, but we're cautiously encouraged by early indicators."

 

I’d Rather See the Mess

Periodically a client asks, “Should I run my draft through AI before I send it to you?”

I understand the impulse. It’s the same instinct that makes people tidy up before the cleaning service arrives. You want to be polite. You want to make the professional’s job easier.

But in writing, that instinct is backwards ... I’d much rather see the mess.

Give me the scattered notes. The half-sentences. The paragraph that starts with one idea and wanders into three others like a dog that just spotted a squirrel. That stuff is gold. It’s where the real thinking lives.

When people run their writing through AI, what I get back is something … smoother. Straighter. Like someone ran a steamroller over a dirt path.

Yes, it’s technically cleaner, but the footprints are gone.

And the footprints are the interesting part.

Raw writing tells me how someone thinks. I can see where they hesitated, where they got excited, where they doubled back. Sometimes a throwaway line in a messy draft is the best idea in the whole piece. AI tends to sand those off, the way ocean water rounds a jagged rock until it looks like every other rock on the beach.

Perfectly nice rock, but completely forgettable.

The other problem is that AI writes like it’s trying to win a politeness contest. Everything is balanced and reasonable and mildly enthusiastic. It’s the literary equivalent of elevator music. Nothing wrong with it. Nothing alive in it either.

Human drafts, on the other hand, are gloriously uneven. A great line followed by a clunky sentence. A sharp insight next to a weird metaphor that probably shouldn’t work but somehow does.

That’s the good stuff.

My job when writing or editing isn’t to start with perfection. It’s to find the spark in the pile of kindling and build a fire around it.

AI, bless its algorithmic heart, is very good at arranging the logs neatly, but it’s less interested in the spark. Which often makes editing AI-polished writing is harder. When something has already been smoothed into generic competence, you spend half your time trying to figure out what the writer originally meant before the machine turned it into something safe and beige.

It’s like restoring an old painting after someone painted over it with house paint. Possible, but annoying, and chances are some good bit are gonna get missed.

So if you’re working with a writer or editor, send the raw material. Send the notes that look like they were written during mild turbulence. Send the paragraph that ends with “I’m not sure where this is going.”

That’s OK. Writing isn’t supposed to start polished. It starts strange, lopsided, and a little chaotic … like most worthwhile ideas.

The Future of AI Might Depend on a Very Human Skill

Watching marketers, writers, and founders celebrating the fact that with AI they can generate 47 pieces of content before their coffee gets cold feels like handing someone a Swiss Army knife and watching them only use the toothpick.

AI has made information absurdly easy to access. One prompt and suddenly you’ve got ideas, summaries, campaign drafts, competitive analysis, a mildly inspirational quote about disruption, and 3 taglines that sound suspiciously like they came from a 2016 startup pitch deck.

It’s impressive. But it’s also revealing that the real difference isn’t the tool … it’s the thinking behind the prompt.

Two teams can use the exact same AI model. One asks it to “write a social post.” The other asks what emotional triggers actually move the audience, what language patterns dominate the category, what competitors keep saying that everyone has stopped noticing.

One gets filler. The other gets insight. Same machine. Different curiosity.

That’s why the idea of AI as marketing autopilot always makes me laugh. AI isn’t autopilot. It’s more like a telescope. It lets you see a lot farther than you could before. It can pull in huge amounts of information, connect patterns, surface ideas faster than any intern or agency brainstorm ever could.

But you still have to decide where to point it.

And that’s where things get interesting. Because curiosity, real curiosity, the slightly annoying kind that keeps asking “why does this actually work?” turns out to be the one skill technology can’t automate very well.

And curiosity is messy. It wanders. It asks the slightly inconvenient question after everyone else has already moved on to the slide deck.

It’s also the difference between using AI like a vending machine and using it like a thinking partner.

The brands getting the most out of these tools aren’t the ones with the most dashboards or the most prompt templates. They’re the ones treating AI less like a shortcut and more like a giant, slightly caffeinated research assistant.

They poke it, challenge it, and ask better questions until the output gets weird in a good way. New angles. Unexpected connections. Ideas that actually move something instead of just filling content calendars.

Which brings us to the slightly uncomfortable truth about this whole AI wave.

Soon, everyone will have access to the same tools. The novelty will wear off. The productivity charts will flatten. The “AI-powered” label will become about as meaningful as “internet-enabled.”

At that point, the only real advantage left will be how people think. Not how fast they generate answers, but how curious they are about the question.

After all the hype about artificial intelligence reshaping marketing, we may end up rediscovering something embarrassingly human: The people who win will just be the ones who never stopped asking better questions.