Don’t automate what you can’t govern with David Levin

AI adoption isn’t being held back by a lack of engineers. In many businesses, the bigger problem is what those engineers are working with: fragmented data, unclear ownership, undocumented processes, and weak governance.

Kailin Noivo sits down with David Levin, Acting Chief Digital Officer at w. kleinberg, to explore what it really takes to deploy AI safely and effectively. Drawing on his experience leading digital transformation, ecommerce growth, and AI-driven automation across major organizations, David explains why businesses need to strengthen their data foundations before assuming more AI talent will solve the problem.

This conversation explores David’s “Understand, Not Invent” approach to customer-facing AI: using AI to understand customer intent while constraining its responses to approved information. He also explains why rigorous internal testing is critical for uncovering hallucinations and edge cases before AI reaches customers.

This episode is essential listening for ecommerce and digital leaders looking to move beyond AI experimentation and build systems that deliver measurable results without exposing the business to unnecessary risk.

David Levin is a digital transformation leader, ecommerce executive, operating advisor, and board member with a track record spanning 2 IPOs, 5 exits, and 1 MBO. He has led digital P&Ls, ecommerce growth, go-to-market strategy, AI-driven automation, and customer experience transformation across major organizations, including ZAGG, Inc. His expertise lies at the intersection of legacy systems, emerging technology, data, and commercial strategy, helping organizations turn digital transformation into measurable improvements in revenue, efficiency, and customer experience.

What you will learn:

How to identify whether digital can become a real growth lever by looking at customer lifetime value, margins, and ownership
Why clean data and clear definitions matter more than simply hiring more AI engineering talent
Ways to choose “clean enough” use cases that can prove value and fund broader AI adoption
How to set practical guardrails for customer-facing AI without sacrificing meaningful cost savings
Why successful AI deployment depends as much on change management, governance, and operator ownership as the technology itself

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