Lee McCance didn’t wander into martech looking for hype or a shiny founder badge. He started where the ad world used to actually live: the Yellow Pages. The real one. The brick. The phone-book-meets-ankle-weight that people used as both a marketing directory and emergency home defense tool.

He likes to shrug and say, “you can imagine how long ago that was,” but the point is sharper: he’s been modernizing marketing systems since long before dashboards were even a thing.

From there he moved through security, gaming, Essence, GroupM, Choreograph, and now Adverity. The environments changed, the logos on the building changed, but the mission stayed stubbornly the same: solve user problems, fix messy data, and use innovation to make work easier, not more theatrical.

At Adverity, that stubborn mission has turned into something he describes with almost comedic consistency: trust, transparency, traceability. They’re practically the subtitles under his name at this point. But he’s not chanting them for branding. He’s chanting them because the entire AI renaissance has created a bigger trust gap than the industry wants to admit.

“People like what AI can do,” he said in Cannes, “but they don’t necessarily trust why it’s doing it.”

That’s the theme of his whole worldview. The AI output is fine. The black box beneath it? Not so much.

Lee isn’t impressed when AI tools brag about being clever. He’s impressed when they can show their work.

“Show the reasoning. Show the logic. Show where the data comes from,” he said, and it wasn’t a request. It was a quiet indictment of every platform that hides its math behind marketing language.

To him, AI shouldn’t be some mystical oracle. It should be a co-worker who can explain itself.
If it can’t? Don’t ship it.

And this is where he digs into his favorite uncomfortable truth:
Marketers aren’t resisting AI because they’re slow. They’re resisting AI because they don’t trust the foundation underneath it. Fix the data and the trust follows. Get lazy about the data and the whole system collapses like a badly built sukkah in a windstorm.

He’s clear that Adverity won’t compromise the unglamorous part of the stack. They’ll keep building the clean, unified data foundation that keeps analytics honest. But static companies die, so he’s pushing forward too.

“We’re launching conversational and agentic AI to help teams get faster insights and collaborate better,” he said. The key, again, is making the tools behave intuitively — not forcing marketers to learn new rituals just to ask basic questions.

AI isn’t here to put marketers through boot camp.
As Lee puts it: “AI should help people be more effective, not make them learn three new skills they never asked for.”
It’s one of those lines that sounds simple until you realize how few martech companies actually follow it.

If you ask Lee whether dashboards are toast, he doesn’t dance around it.
He’ll tell you that dashboards aren’t disappearing tomorrow, but conversational AI has already started eating away at their monopoly.

Teams want fewer dependencies. They want to ask questions directly. They want answers without waiting in line behind three analysts and a BI backlog.

But again — his caveat lands with the weight of someone who’s actually built enterprise-scale systems:
New approaches only work if the underlying data is trustworthy.

And when he says trustworthy, he doesn’t mean sanitized.
He means traceable. Auditable. Understandable.

It’s the opposite of martech’s favorite black-box trick.

Lee’s empathy is almost disarming when he talks about marketing teams.

He knows the skill gap isn’t about intelligence — it’s about maturity.
“New tools can go a long way,” he said, “but we need real data literacy. Not just using big data, but using it appropriately.”

He’s watched too many companies bury critical information in silos: spreadsheets in one department, slides in another, random exports floating in someone’s inbox like orphaned prophecy.

If marketers want to tap the full value of AI, he says the prerequisite is non-negotiable:
Fix the data. Then use the data. Then let AI help.

Skipping the first step is how you get hallucinations.
Skipping the second is how you get bad decisions.
Skipping both is basically martech’s origin story.

Lee condenses his product philosophy into three deceptively simple marching orders:

This isn’t performative thought leadership. This is what happens when you’ve been building things long enough to know what breaks and why.

Cannes surprised him this year.
A couple of years ago, he said, brands talked about AI in theoretical terms — the “art of the possible” era. Now they’re actually doing it. At scale. In real workflows. In real decision-making.

The question he cares about now:
Will the industry actually follow through?

“In a year, did we move the needle? Did we turn these conversations into something meaningful?”
It’s the kind of question asked by someone who’s spent decades building things people actually have to use.

Lee isn’t the loudest voice in martech.
He’s the adult in the room — the one insisting that “innovation” doesn’t mean chaos and “AI” doesn’t excuse opacity.

He’s betting on a future where data is clean, AI is accountable, and marketers can finally stop guessing whether their tools are bluffing.

And honestly?
That might be the most radical vision in the entire ecosystem.