You started out as a computer science + math grad. How does that technical lens give you an edge as a marketing leader in a highly technical AI space?

I was a CS and Math major at Colby College, but I always loved storytelling, especially explaining complex things in ways that feel approachable and don’t make people feel intimidated. Bill Bryson’s book A Short History of Nearly Everything is the perfect example of that. The technical foundation gives me some credibility with engineers and product leaders, which matters a lot in AI. More importantly, it lets me translate value across worlds: engineering to marketing, product to legal, AI teams to finance and risk. I can be creative without hand-waving or sacrificing accuracy. The biggest advantage is that I can be curious. A technical lens pushes you to keep asking “why” until you get to what users actually care about—which is where real differentiation lives.

You’ve launched dozens of products across B2B and B2C. What’s the most overlooked element of a successful GTM launch?

Alignment. Everyone loves the big launch story, and everyone loves shiny tactics, but connecting the two is where launches live or die. What does “launch” actually mean? Is the product ready, or just the press release? Are pricing, packaging, enablement, and support aligned to the same date and goals? Getting engineering, product, marketing, sales, and customer support operating off the same assumptions and timelines is the hard, unglamorous work. Most failures come from things left unsaid or ignored, not things done poorly.

Everyone’s hyped about GenAI, but governance and lifecycle automation aren’t exactly jazzy. How do you make the case that these are the real enablers, not afterthoughts?

Stories beat stats every time. Numbers are helpful, but everyone thinks their situation is “special.” Customer stories and hearing how governance or lifecycle automation launched someone’s career, unblocked a team, or saved a peer organization from a painful experience—that’s what lands. The story also changes by persona: governance matters deeply to risk leaders; lifecycle automation matters to AI and platform owners. And it all comes back to the “why.” ModelOp exists to unlock the transformational power of AI. When you lead with that, the how and the what sound like accelerators.

ABM is often hailed as the gold standard in enterprise marketing. How do you actually make it work at ModelOp beyond its shiny status?

Let’s be honest: true ABM is expensive and time-intensive. The original ITSMA definition was basically “treat every account like its own market,” which is brutal to execute. Over time, ABM got watered down to mean “personalized emails” or, if you had the budget, sending gifts from platforms like Sendoso. Those are tactics. The good news is that tools like Clay and GenAI make deep account research and personalization more scalable than ever. But even with better tools, ABM only works when you’re engaging the right person, at the right time, with something that builds trust. In B2B, trust still wins—AI has the potential to have you earn it faster (or lose it faster if done carelessly) when you combine it with real connections and interactions.

You’ve built teams at both startups and public firms. What’s harder — scaling zero-to-one marketing functions, or reimagining them at large enterprises?

It depends. Zero-to-one is scrappy and resource-constrained, but you can move fast without much overhead. Large enterprises usually have the resources, but also the politics, inertia, and fear of breaking what already “works,” even when it clearly doesn’t. One is a scarcity problem; the other is a gravity problem. Both are hard in very different ways.

Do you see the persuasion quotient in AI governance marketing going up or down in the next few years?

Way up. No hesitation.

Finally, what’s tougher — keeping up with algorithms or keeping up with audiences?

The real challenge is that you have to keep up with both—at the same time—and they don’t always want the same thing. Algorithms reward data signals. Audiences want trust, emotion, and value. In AI, especially, you’re constantly balancing hype versus reality. And even more difficult, creating messaging that can survive AI compression and still make sense in real customer conversations. Keeping up with either one is hard. Doing both simultaneously, without losing credibility, is the job now.