Too many teams are rushing AI into CS without a clear hypothesis. It’s chaos, not experimentation, and the result isn’t just bad workflows. It’s a loss of control, with AI calling the shots instead of the people meant to guide it.

David Karp is the Chief Customer Officer at DISQO, a leading ad effectiveness measurement platform. For him, cutting through the noise requires a "ruthlessly intentional" mindset.

Lab coats only: “When you’re in a world of experiments, you get to choose: Am I the scientist or the lab rat? We’re going to keep being the scientists,” says Karp. Being the scientist in an era of constant tech upheaval means taking control, setting the terms, and using AI with intention. The alternative is to become a passive subject, reacting to change instead of directing it. In his view, the choice is clear: lead the experiment, or be shaped by it.

AIs and ears: Karp’s first principle flips the script: AI's primary role isn't boosting efficiency, but solving a deeply human problem most businesses fail at. "If I'm a customer, I want to know that everything we've ever told you, you've heard," he explains. "But what's really hard is to listen to all of your customers. You just run out of human bandwidth."

His first foray into AI wasn't about cutting costs, but about creating an organizational memory backed by a structured framework. His team uses AI to mine every conversation for four critical categories—customer risk, product feedback, advocacy opportunities, and growth signals—turning raw data into an intelligence engine that funnels insights across the business.