CIOs face new demands as AI ownership shifts strategy
CIOs, CTOs, and chief AI officers are moving from behind-the-scenes technology management into front-line business strategy roles. A recent Altimetrik report found that accountability for AI deployment and success most often lands with these tech leaders, whose responsibilities now include proposing company-wide AI roadmaps rather than simply enabling existing platforms.
The pace of change is accelerating. "How do you stay ahead of the game in a world where AI innovations and disruptions are coming fast and furious?" said Babak Hodjat, chief AI officer at Cognizant. Executives feel pressure to find productivity gains in pilots and justify projects financially while strategies can shift week to week.
CIOs have shifted from empowering teams to run SaaS platforms to identifying and vetting AI use cases across the organization. Most CIOs are highly motivated to pursue these projects and operate as enablers, according to Brian Jackson, principal research director at Info-Tech Research Group. They create methodology and become experts in new technology to integrate it into workflows.
Infrastructure readiness comes first
Before launching pilots, tech leaders must assess their organization's maturity. The critical question: Is the data and infrastructure ready for AI, or does IT need preparation to extract value?
"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 has transformed entirely. Once focused on research and development, it now concentrates almost exclusively on identifying the next major AI advancement. CTOs are becoming central to company strategy and may guide boards on future predictions. These tech roles increasingly collaborate with the financial side of the C-suite to measure project growth and spending.
Governance requires continuous oversight
Tech leaders must do more than deploy AI-they must also build policies and guardrails for its use. This work is ongoing, not a one-time event.
Until recently, guardrails functioned as single audits that created a false sense of safety. "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.
One approach treats an enterprise as a modular multiagentic system that keeps expanding. Rather than viewing governance holistically, tech leaders can create safeguards for each component in ways that suit their organization. This is especially necessary for enterprises with 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 universal checks exist, each organization must tailor safeguards to its specific AI projects. Governance happens at multiple organizational levels, but it works best when there's a clear, proven use case and when tech leaders can identify when AI isn't needed.
Hodjat urges leaders to pause and ask: Is your business vision one where agents run semi-autonomously? If so, what's the safe path to get there versus the unsafe one?
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