When ServiceNow reported its third-quarter earnings in late October, executives emphasized how its focus on agentic AI is paying off in projects, from the launch of AI Experience — a user interface for enterprise AI — to the Zurich platform release, which enables agentic AI to move faster and scale securely.

To find out more about ServiceNow’s strategy, CIO Upside interviewed Amit Zavery, the company’s president, chief product officer, and COO. He discussed the roadblocks to agent adoption, scaling methods, why *agents *need *people *and not the other way around, and what the future of agentic AI may look like.

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This interview has been lightly edited for clarity.

As enterprises and your customers are adopting agents, what are the main hurdles that they’re encountering?

Most of the customers we talk to are trying to get help in terms of understanding where to start, what to do, and how to approach AI in general for their use cases. In a lot of the conversations, we help them with a package. We have more than 100+ agentic workflows and processes built out of the box. You can just get going. And once you start with that, they start seeing value, they get a little more confident. They also get outcomes, which they can now replicate with other areas — they can start seeing more patterns.

The technology has come a long way. What we are able to do now — with thousands of customers using AI capabilities on the ServiceNow platform — the ability to go live, the ability to get value and some ROI quickly is there. With many of the customers, the volume of agentic usage quarter-over-quarter has gone up 6x. That’s really showing us that customers are not worried about the technology itself. It’s a lot of times just helping them through their thinking and the use cases and making it more prescriptive.

**How can enterprises that are using these agents go from pilot programs to scale? **

Before, a lot of customers were experimenting with “spare bots,” taking pieces of technologies from multiple places: some from open-source, some from other vendors. But it was never thought out as an end-to-end solution, so it became very hard for many of these customers to really keep up with the evolution in this space.

A lot of the failures, which we saw with customers who talk about not getting value out of AI, were really due to the spare-part approach. The ones who’ve been successful going into production and getting all these things working for them are using the platform approach: going with a partner who can really provide the full building blocks with built-out solutions so that they don’t have to keep up with all the technology changes, and then really not be able to drive value out of it. That has been a big change, going with the platform-first approach where AI is built in, not built from the side: It’s really inside the product itself.