Maturing AI, business impact, and agents galore: How the CAIO role has evolved over the past year

AI technology has come a long way in one year

Shifting the focus from the tech to the business impact

AI agents take center stage

Sage Lazzaro is a technology

In the fast-moving world of AI, a lot can change in a few days, let alone a year. Chief AI officers (CAIOs) across industries are feeling the whiplash pace of AI development and are facing new questions and challenges in their jobs. While the core of their role—to oversee the company’s AI development, internal AI usage, and overall strategy in the new AI landscape—remains the same, many CAIOs say that a lot of what they’re focused on has changed from a year ago to today. Now, they’re working with more mature AI tech, prioritizing business impact, and navigating the hype and exploration of AI agents.

“The chief AI officer is no longer just a technologist,” said Michelle Bonat, CAIO at AI Squared, which helps companies integrate AI models into their business applications. “They’re a business strategist with an AI toolkit. And our job is to not necessarily build models ourselves—although we may do that—it’s to really completely redesign the company, how it thinks, operates, and grows with a focus on AI.”

AI is undoubtedly still a work in progress, but the maturing of the tech is a leading difference many CAIOs cite in how their work has changed from a year ago to now.

Ali Alkhafaji, chief AI and technology officer at Omnicom Precision Marketing Group, says the past year has felt like a front-row seat to one of the fastest evolutions in technology he’s ever witnessed. 

“The pace at which foundational models, agent architectures, and enabling tools have matured is nothing short of astounding,” he said, adding that this has transformed his priorities from just building AI tools to building confidence and fluency in them throughout his organization. 

Zocdoc cofounder and chief AI officer Nick Ganju said this past year has been particularly intense because products and uses of AI that were “immature last year are now really working.” This goes for both how the company is using AI internally, as well as the AI products it’s developing. For example, he said, internal wikis have existed forever, but now engineers can truly have a conversation with these internal re

“It’s rapidly shifting from an early adopter tool to ‘It’s your obligation to use this,’” he said, adding that while the skepticism around AI’s actual worth made sense last year, recent gains show the tech is delivering.  

“I think that was fair last year when all the tools were still coming to maturity,” he said. “Then this year, you have salespeople who were hitting X number of leads per month are now hitting 2X leads per month. Undoubtedly, the value is here.”

Accenture chief AI officer Lan Guan said her role has “changed significantly” from last year, specifically citing a change in customers’ focus from the tech of AI to the business of AI. Deloitte U.S. head of AI Jim Rowan, who like Guan, works with a range of clients on their AI strategies, has similarly seen the rising attention on the business impacts among his client base. 

“The ‘Where do I generate value? How do I measure value?’ conversation, I would say, was in the top five [questions from clients] before. It’s now [number] one or two in every conversation that I’m in, because it’s like, ‘We’ve been at this for a little bit. We’ve been implementing it. Are we measuring the right things? Should we measure something different? Are we getting the return that we’re expecting?’” he said. 

For example, Uri Yerushalmi, cofounder and chief AI officer at Fetcherr, which uses AI for predictive pricing in the airline industry, said he’s recently increased awareness of what AI can do within the business side of organizations. This has opened up new opportunities for the company to expand into more industries and broader decision-making needs, including logistics, retail, supply chain, and ticketing.

“A year ago, my focus was on proving that autonomous AI decision-making systems could exist,” he said. “Today, it’s about delivery and scale.”

Mastercard’s chief AI and data officer, Greg Ulrich, similarly said that last year was “early innings” and that a lot of his time then was spent helping the business understand where it could responsibly experiment with AI, but since then his role has evolved from architect to operator.

“We’ve moved from exploration to execution. Today, I’m spending more time scaling what works—whether that’s onboarding agents, agentic commerce pilots, or internal copilots that are now embedded in our workflows. We’ve gone from asking, ‘Can we do this?’ to ‘How do we do this securely, at scale, and with measurable impact?’” he said.

Just as agents have dominated the AI discourse, they’ve become top-of-mind for CAIOs over the past year, too. For some, this means diving headfirst into AI agents. For others, it means trying to navigate the hype.

Guan said the companies she works with at Accenture are shifting focus from generative AI to agentic AI in significant ways. While agent efforts have so far revolved around simpler tasks, customers are increasingly asking for more reasoning abilities and help setting up multi-agent systems to tackle complex enterprise workflows. 

“I’m very impressed with this kind of sophisticated thinking from a lot of business leaders, because they are not satisfied. They are not too satisfied with generative AI now,” she said. “Very quickly, they are not even satisfied with just setting up a single agent to go after existing workflows. They’re asking for real impact with multi-agent systems.”

This echoes what Rowan is experiencing at Deloitte, as he notices client questions shifting from the topic of generative AI to agentic AI. AI agents have also shot to the top of his internal to-do list as the company pushes agent use internally. 

“We understand agents now; we kind of understand how they’re supposed to work. It is a full-on press to make sure the velocity of agents we’re releasing is fast and meaningful. And so, we’re really trying to get new solutions out to our practitioners and doing large releases—so thousands of users—weekly, or every two to three weeks. That’s the push we’re making,” he said. 

Bonat of AI Squared has seen this play out as well, noting that chatbots have basically become table stakes and that she’s hearing about companies developing thousands of agents. The agent push was on full display at a recent data and AI conference she attended, which she said turned out to focus almost exclusively on agentic AI. 

“The conversation I was having with the attendees over the coffee break was, ‘Did we sign up for an agentic conference? I thought we signed up for the data and AI conference,’” she said. “We were trying to figure out if that is what the organizers thought we wanted to hear, or is that what everyone wants to tell us? So it’s very much swung toward agentic.”