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Build vs. Buy in AI: What Enterprise Teams Are Getting Wrong With Eric Williamson

Revenue Engine Podcast

Release Date: 07/03/2026

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Eric Williamson is the Chief Marketing Officer at CallMiner, an AI-powered customer experience automation platform that analyzes customer interactions to uncover insights and improve service performance. In his role, he oversees global marketing across brand, demand generation, events, and growth strategy. Eric brings more than 20 years of experience across B2B technology, consumer marketing, agency leadership, and content strategy.

In this episode…

AI is changing the economics of customer service, but the real shift is not just replacing people with bots. Enterprise teams are being forced to rethink what they automate, what they build internally, and where outside platforms can still create value. When everyone can access powerful AI tools, how do companies decide what is worth building themselves?

As a B2B technology marketing leader with expertise in AI, automation, brand strategy, and enterprise growth, Eric Williamson explains that the answer starts with intelligence. Companies need to understand which customer interactions are best suited for automation before they deploy AI agents at scale. Eric emphasizes that many teams underestimate the long-term cost of building AI tools in-house, especially when those tools need to match the depth, reliability, and insight of established platforms. The stronger approach is to start small, identify the right use cases, create a seamless handoff between AI and humans, and use customer interaction data to guide automation decisions.

In this episode of the Revenue Engine Podcast, Alex Gluz talks with Eric Williamson, Chief Marketing Officer at CallMiner, about build-versus-buy decisions in AI and what enterprise teams often get wrong. Eric shares how AI agents are reshaping customer service, why SaaS pricing is moving toward consumption models, and what brand visibility means for B2B growth. He also touches on agency resourcing, LMO, and avoiding AI-generated content that lacks quality.