Feature
Artificial intelligence may be everywhere, but figuring out who’s taking the lead on AI initiatives inside enterprises remains a complex and constantly evolving affair.
Once touted as the next C-suite essential, the Chief AI Officer (CAIO) title has not yet taken deep root in most organizations — a recent Data & AI Leadership Exchange survey found just one-third of organizations have a CAIO. Instead, leadership responsibility is splintered across titles ranging from CIOs and CTOs to heads of data, analytics and individual business units, depending on how AI is being implemented and what problems it’s solving.
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“Right now, IT leaders in tandem with business leaders are driving AI, more than newly appointed heads of AI or even CIOs,” said Tim Sanders, vice president of research insights at G2. “The money’s coming out of central IT and traditional departmental budgets. It’s no longer about ‘let’s try this out’; it’s ‘this is how we work now.’”
This shift in accountability is leading to the formation of new roles, with a recent IBM study finding the majority (54%) of CEOs said they are hiring for AI roles that didn’t even exist a year ago.
“Businesses are realizing that to unlock the full potential of AI technology, they need strategic leadership that understands how to redesign work itself,” said Manish Goyal, IBM Consulting’s vice president and senior partner of AI and analytics. However, this doesn’t necessarily mean they’re turning over executive responsibility to a CAIO. Many organizations still view the CAIO as a strategy-setter, not an executor.
“The real leaders of AI initiatives vary depending on the organizational structure,” explained Vamsi Duvvuri, EY Americas TMT AI Leader. “Where AI is core to the business, the CAIO or their delegates might oversee it. But when large platform builds are involved, technical leaders step in to take AI from pilot to production.”
At IBM Consulting, the trend points to operational leaders playing a central role. “Business leaders are increasingly accountable for driving outcomes,” said Goyal, adding that the CIO or CTO remains central to governance, compliance and platform design, but business functions — sales, marketing, supply chain — are responsible for where and how AI is deployed.
In practice, that means there’s no one-size-fits-all AI boss. AI strategy often begins with business units identifying the pain points — then IT builds the infrastructure. “The business owns the ‘why,’ and IT owns the ‘how,’” said Sanders. “That’s how you get real value. Organizations that treat AI like an R&D project are going to get left behind.”
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As AI moves into everyday workflows, new job titles are emerging rapidly; EY’s Duvvuri sees demand accelerating across several functions.
“We’re seeing more need for AI engineers and developers, as well as designers and testers,” he explained. “Another area transforming is data governance — this is now exploding into the AI domain, particularly around responsible AI practices.”
Meanwhile, the role of the Chief Data and Analytics Officer (CDAO) is being reshaped, with some of their responsibilities absorbed by CIOs or CTOs. But for organizations prioritizing AI adoption, the CDAO remains essential. “With machine learning becoming more central to business, CDAOs must lead integration efforts while also navigating ethics, privacy, and security,” Duvvuri noted.
Ram Palaniappan, CTO of TEKsystems Global Services, said collaboration between stakeholders is critical.
“While roles like CAIO may guide overall vision, cross-functional teams are responsible for the execution." Palaniappan added that, from his perspective, success depends on how well AI integrates into workflows — not just how impressive the models are.
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AI adoption often stalls when strategic vision and technical execution aren’t aligned. In fact, the IBM Consulting survey found just a quarter of AI initiatives delivered expected ROI over the last few years.
To better facilitate successful AI launches, organizations are embedding AI into their day-to-day operations, rather than treating it as an isolated initiative.
“We’ve seen a lot of clients struggle when IT leads with a pet project instead of the business driving the initiative,” said Sanders. “The best projects start with a business problem. For instance, if customer service costs are too high, AI can reduce ticket resolution from $18 to $6. That’s real ROI.”
Goyal sees similar patterns, noting the conversation has shifted from experimenting with models to building repeatable, scalable AI solutions. “To do that, companies need talent that understands data pipelines, cloud operations and how AI fits within their enterprise architecture."
The ongoing skills gap remains is a challenge, with an EY survey finding 84% of tech leaders plan to hire AI-skilled talent in the next six months.
Duvvuri pointed out upskilling and external partnerships are also part of the equation. “Tech companies are accelerating innovation through targeted training programs. Learning must be continuous.”
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CAIO: Master Strategist or Paper Tiger?
In a West Monroe survey, 40% of respondents said they expect the CAIO role to grow in influence and importance over the next five years.
So does the CAIO matter? Yes — but perhaps not in the way many expected. Rather than taking charge of every AI implementation, the CAIO is becoming more of a strategic coordinator.
“For large enterprises, CAIOs don’t lead projects directly,” explained Duvvuri. “They define the operating model and set expectations for how AI integrates into the business. But they need a strong band of lieutenants to execute their vision.”
Palaniappan added that in some companies, the CAIO is a high-level role without deep operational authority. “The real traction happens at the project and departmental level, where AI must deliver value fast." That decentralization may explain why new job postings for “Head of AI” roles appear far less frequently than those for AI engineers, MLOps specialists or domain-specific AI roles like NLP engineers or computer vision leads.
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“There’s more investment in doers than in overseers,” said Sanders. “Ultimately, AI success is determined by how well you solve business problems, not how impressive your org chart looks.”
As AI becomes core infrastructure rather than novel technology, leadership will continue to evolve. Instead of one central AI owner, most organizations will rely on a hybrid model that combines business-led vision with IT-led execution and governance.
“The trendline points to AI producing the most value for companies that give it a high level of autonomy,” according to Sanders. “Leaders need to connect business problems with AI capabilities and educate their teams to build trust.”
In short: AI leadership isn’t about who holds the title — it’s about who drives outcomes. And increasingly, that means teams of people with different skillsets working together, not one person calling the shots. “When all employees and teams have the tools they need to successfully use and deploy AI, everyone wins,” said Duvvuri.
