AI adoption hinges on product-led growth, not just sales

“We get asked this question all the time actually with investors where they’re like, ‘Oh, you should do other industries.’” - Varun Anand

The core challenge of scaling AI applications into the enterprise was dissected by Decagon and Clay founders during an AMA with Andreessen Horowitz. Kimberly Tan, Investing Partner at Andreessen Horowitz, spoke with Varun Anand, Co-founder of Clay, and Jesse Zhang, Co-founder of Decagon, at OpenAI DevDay about enterprise AI adoption, why many enterprise AI pilots fail, and how the speakers scaled their companies to become unicorns.

One critical insight is that, despite the allure of AI, product-led growth remains paramount. Anand noted, "In go-to-market, what we see actually is that... you have traditional orgs operating in silos." He explained that this siloed approach hinders progress, emphasizing the need for go-to-market teams to function like product teams, enabling them to build scalable systems.

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Another vital consideration is balancing innovation with practicality. Zhang articulated the importance of quantitative ROI, stating: "If we get to the end of a pilot and like people are unclear like what actually happened or how much money are they going to save or how much more money are they going to make, then it’s going to be a really tough sell." The implication is clear: AI deployments must demonstrably improve the bottom line.

The conversation further explored the significance of data as a differentiator in the crowded AI landscape. Anand observed, "We win because we aggregate all the vendors and we use AI in creative ways to find net new data points that you can’t get elsewhere." This underscores the value of AI's ability to unlock novel insights from data, providing a competitive edge that transcends mere automation.