CAIO and CEO alignment: Why it matters and what it takes

When IBM’s Alexandra Nasif asks chief AI officers (CAIOs) to describe their biggest hurdle, they tend to bring up a similar experience.

“It’s framed as ‘I’m kind of on this island by myself, and I have this really large, daunting, first-of-its-kind mission to accomplish, and I’m not connected in the ways I need to be,’” Nasif, AI Practice Leader, IBM Consulting Americas, told IBM Think.

That predicament is becoming more common as organizations transition from AI pilots to full-scale production. The decentralized experimentation that helped accelerate AI innovation early on has given way to disconnected systems and uneven oversight.

Departments are competing for the same resources, duplicating efforts and keeping their successes siloed from the wider enterprise. As a result, ROI hasn’t kept pace with projections. Only 25% of AI initiatives have delivered expected value and just 16% have scaled enterprise-wide, according to IBM’s Institute for Business Value (IBV).

Although senior executives might be tempted to place the blame squarely on AI leadership, CAIOs say that, without clear authority and ownership over AI decisions, they’re struggling to deliver measurable results. The widening rift between CEOs’ expectations and CAIOs’ operational reality strains alignment, stalling AI projects before they reach full maturity.

When generative AI first swept the enterprise landscape starting in 2023, CEOs gave their newly minted CAIOs a simple directive: Get as many teams on board as possible.

Now, as organizations scale up production, CAIOs are facing a new, more complex reality. Because different teams adopted different AI solutions and data sources during the pilot phase, enterprise ecosystems have become tangled, fractured and harder to manage, costing large enterprises an estimated USD 140 million each year.

“When it comes to scaling AI for enterprise impact, all that fragmentation can become a source of great mess and sprawl,” Jacob Dencik, research director at IBM’s Institute for Business Value, told IBM Think. “Our IT systems are supposed to support all these thousands of agents and assets? How’s that going to work?”

AI is also different than other technologies because, unlike with cloud, IoT or edge computing, most employees interact with LLMs daily—and “everyone has an opinion” about how they should be used, Dencik said. As a result, CAIOs often face more headwinds and pushback compared to other leaders.

Finally, because AI cuts across departments and use cases, the CAIO does not inherently own agentic deployments in the same way that a CFO controls finance or a CTO controls IT.

Without explicit enforcement mechanisms, CAIOs often lean on the power of persuasion—pinpointing the organization’s most urgent business objectives, identifying where AI can help and communicating in terms that colleagues understand.

If there’s one piece of advice that Roger Moore, an associate clinical professor at the University of Chicago, tries to impart on his students, it’s that AI-related deliberations are no different from any other business decision. This can be a challenge for current or soon-to-be CAIOs, who often come from technical backgrounds.

“If the first words out of your mouth to the CEO are ‘area under the curve,’ you’ve lost. You’re not going to go anywhere with that,” Moore told IBM Think. “You’ve got to communicate in their language, in the language of the business.”

That’s a challenge data scientist Jon Morra knows firsthand. He’s the CAIO of Zefr, an ad tech company that uses AI to classify social media content for advertisers. Morra doesn’t have to convince his colleagues that AI is useful; it’s a core part of their product offering. What’s more difficult is moving between the worlds of business and technology.

“It took me a little while to wrap my head around the fact that my job is no longer just to create the most value for my customers via the AI that my team produces, but it’s also to enable the business to be more productive via using these tools,” Mora said. “I’ve had to learn to speak multiple languages at the same time.”

When optimizing Zefr’s customer-facing models, Morra can point to reliable metrics like accuracy, recall and precision. But open-ended process questions—like whether Zefr should use third-party LLMs or build its own with open-weight models, or how far its developers should go in automating their coding with agents—are much harder to answer.

What has helped, Morra said, is maintaining firm ownership over the tech side (alongside the CTO) while being less prescriptive and more open-minded on the business side. That balance will look different for every company, he added.

At this point, most CEOs trust that AI can transform their business, but they still need help discerning which AI initiatives to prioritize, how to implement them and how to distinguish AI hype from current capabilities.

CAIOs can start by asking questions like “What keeps you up at night?” Moore, the UChicago professor, said. The answers can help CAIOs guide AI policy toward specific business problems, rather than contemplating technological capabilities first and reverse-engineering potential use cases.

“Take technology out of it. What is the strategic mission and objective of the organization and of the company?” Nasif, the IBM consultant, added. “If you are mapping against the objectives of the organization, then your priorities are going to be inherently aligned.”

The friction that can arise between CEOs, CAIOs and other executives isn’t inherently negative; in some cases, it might be a key driver of effective AI implementation.  

A CAIO can act as a provocateur, providing a necessary counterweight to the organization’s prevailing AI strategy. 

If an organization tends toward caution, its CAIO can temporarily morph into a chief transformation officer, nudging colleagues to take more risks. The CAIO might suggest, for example, to aggressively scale a high-impact, low-risk AI deployment, such as an automated lead generation tool or a note-taking agent, that shows early promise.  

Meanwhile, when CEOs pursue AI too aggressively—without having a clear business objective in mind, or without the KPIs in place to measure success—the CAIO can reset expectations, define more realistic goals and help identify which AI initiatives are worth pursuing.  

The CAIO might, for example, convince financial analysts to keep an older, neural-network-based prediction model for financial forecasting rather than adopting a trendier LLM that turns out to be less accurate and more expensive.  

This healthy tension only works if the C-suite can maintain a high level of trust: stakeholders, and especially the CEO, must be open to feedback and willing to evolve policies based on new evidence. However, even though some disagreement can help fuel innovation, the CEO and CAIO need to coalesce around a set of shared business goals and execute on them—or risk getting perpetually stuck in the experimentation phase.  

Aside from strategic alignment, CEOs and CAIOs might reconsider their operating structure because, as technology advisor Mike Mason puts it, “AI is really just shining a light on” the underlying issues that are already present inside organizations.

“If you have poor governance, if you have an unclear business-strategy-to-execution mechanism at your organization, AI is not going to fix it,” said Mason, former CAIO of global tech consultancy Thoughtworks. “I actually think AI is going to push on your sore spots.”

One place to start: Looking at whether high-level infrastructure is too fragmented and scattered. According to a 2025 IBV report, organizations with hub and spoke or centralized AI frameworks see 36% higher ROI compared to those with fully decentralized architectures.

Hub and spoke approaches aim to balance team autonomy and unified oversight: While departments maintain limited control and ownership over their AI deployments, a centralized AI team owns governance, provisioning and monitoring with support from developers, IT and cybersecurity. In addition, organizations that automate these processes are 13 times more likely to scale their AI initiatives, according to new IBV research.

These architectural shifts might require the CEO to hand more authority to the CAIO, including the ability to control AI budgets and enforce AI-related policies. The CEO must also communicate priorities to stakeholders and emulate business-focused AI decision-making through their own behaviors.

“If you can’t sell that vision broadly, you get paralyzed in that fragmented phase because you can’t get that broad buy-in and adoption that you need in order to scale,” Nasif said.

As AI capabilities evolve and expand, the CAIO role is shifting from AI evangelism to implementation and orchestration. That involves working alongside the CEO to keep teams laser-focused on business outcomes, aligned through a robust governance framework and attuned to AI’s emerging capabilities.

“That’s the role that potentially the chief AI officer is growing into—much more leading that transformation rather than just setting strategy and defining use cases,” Dencik said. “Having more executive alignment at the top, but also that alignment embedding itself within the organization and how it operates—that’s how you get more success with AI at scale and maintain continuous innovation.”