4 recs for CIOs as they implement agentic AI

We’re seeing signs of stress, such as in Gartner’s 2025 AI Implementation Survey, where 87% of Indian global competency center leaders have made public statements about their gen AI initiatives, yet only 23% have deployed solutions that materially impact business outcomes. And KPMG’s Voice of the CIO paper says that issues and angst are mounting among CIOs as they’re goaded into accepting tech provider forays into gen AI functionality.

So to get a sense of how CIOs are approaching their agentic AI implementations, a number of tech leaders describe here how they’re proceeding, whether they’re taking it fast or slow, and their thoughts on a reset of expectations as we see more real-world implementations and issues coming to light. Based on these conversations, here are four recommendations for CIOs on how to approach an agentic AI implementation.

Decide how fast you should go, not can go

According to Dan Garcia, CISO at software developer EDB, he and his team recognize that different agentic AI use cases require varying degrees of pacing. They move fast in areas where the business ROI is clear, where there’s mature data infrastructure, and where governance allows. And they have to be more deliberate in areas where automation, hallucinations, safety, or misuse of data can impact the value proposition they’re seeking. “We see these common threads among our customers,” he says. “Delivering agentic AI promises requires sovereign infrastructure that provides control of your data, logic, and business outcomes. So the question isn’t how fast you can go, it’s how fast you should go, and where you still need a human in the loop to maintain trust and accountability.”