As AI adoption moves at breakneck speeds, tech chiefs have had to adapt how they own and deploy tools in their organizations.
Many executives feel pressure to find productivity gains in pilots and make sense of projects financially, said Babak Hodjat, chief AI officer at A consultancy Cognizant. CIOs, CTOs and chief AI officers must now think beyond technology and innovation, proposing companywide roadmaps and strategies.
The shift has brought C-suite tech leaders more into the spotlight, he said.
“How do you stay ahead of the game in a world where AI innovations and disruptions are coming fast and furious?” Hodjat said.
Accountability for AI deployment and success most often lies with CIOs, CTOs or other tech leaders, a recent Altimetrik report found. As strategies can often change by the week, C-suite tech leaders’ roles and responsibilities have evolved.
CIOs have shifted from being a backroom C-suite member that empowers people to run their SaaS platforms to being front and center in identifying AI use cases, Hodjat said.
Most CIOs are highly motivated to pursue AI projects, and they tend to operate as enablers for the rest of the organization, said Brian Jackson, principal research director at Info-Tech Research Group. They often create methodology and become an expert in a technology to integrate it into workflows.
Some of the pressing work for tech leaders is assessing their organization’s maturity, asking themselves if their data and infrastructure is ready for AI pilots, or if they have to prepare their IT to gain value, Jackson said.
“You’re not necessarily trying to dictate how the technology is going to be used and what it should do,” Jackson said. “But you’re going to teach the organization about the technology so you improve the literacy, you demonstrate the capabilities and you facilitate the ideation around how to use it.”
The CTO role was once focused on research and development, but is now almost exclusively looking at the “next big thing in AI that’s coming,” said Hodjat. They are likely much more central to the strategy of a company, and they may guide the board to make future predictions.
In the AI adoption era, these tech roles may be working more closely with the financial side of the C-suite than they did previously to measure the growth and spend of new projects.
Owning governance
Tech leaders are not just responsible for deploying AI, but also for collaborating on policies and guardrails for how it will be used within an organization.
The process is continuous, Hodjat said.
Until recently, guardrails served as one-time audits, providing a false sense of safety, he said.
“You cannot afford to do that with AI systems today, at the rate at which they’re being adopted and the autonomy that they bring along with them,” Hodjat said.
He added that it can be helpful to view an enterprise as a modular multiagentic fabric that keeps expanding. Instead of viewing it holistically, tech leaders can create governance for each part in a way that works best for their organizations.
This approach is especially necessary when working with enterprises that have multimodel and multiagent tech stacks, Jackson said.
“It’s about really figuring out this new architecture, this new governance layer,” Jackson said. “AI is so much more than just a piece of software that you drop into a company.”
While there are universal checks that tech leaders should have in place, each organization will need to tailor their safeguards to their AI projects. AI governance will happen at several levels of the organization, Hodjat said. But governance is best built when there’s a clear and proven use case for AI, and when a tech leader can identify that AI is not needed.
“We say to clients, put the brakes on for a minute and think — is your absolute vision that your business is going to be a bunch of agents running around and doing things semi-autonomously?” he asked. “How do you get there? There’s a path that’s safe and there’s a path that’s unsafe.”
