AI projects don’t fail because of algorithms—they fail because organizations aren’t ready. Glenn Russell, Global AI Practice Lead at Egen, joined Swapnil Bhartiya to outline the three biggest challenges enterprises face when adopting AI and how leaders can realistically assess readiness.

“The first problem is always people,” Russell explained. Executive buy-in is critical, especially since AI initiatives often depend on sensitive HR, operational, or security data. Without leadership alignment, projects stall before they begin.

Integration is the second challenge. Many enterprises still operate with data silos or lack endpoints to connect systems. Without seamless access, AI cannot deliver value across workflows. The third challenge is skills—but not in the way most assume. “It’s not about learning hardcore mathematics. It’s about understanding how to get the best out of the tools,” Russell said, emphasizing human-in-the-loop use cases that augment rather than replace people.

Assessing readiness, he argued, comes down to digital transformation maturity. If recovery from a system failure takes days, AI is not the problem—the entire infrastructure needs attention. “AI is not some mystical beast which is self-healing and can run itself. It needs that proper foundation,” Russell stressed.

Enterprises that want to succeed with AI must first ensure robust processes, manageable technical debt, and strong internal data and cloud platforms. Only then can they turn AI from hype into lasting business value.