A growing number of organizations are appointing chief artificial intelligence officers and seeing an average of 10% greater return on investment in AI spending and 24% greater innovation compared to their peers — but most organizations remain stuck in pilot mode and struggle to scale AI initiatives more broadly.

Those are among the findings of a new study of more than 600 CAIOs across 22 countries and 21 industries conducted by the IBM Institute for Business Value with the Dubai Future Foundation and Oxford Economics. The survey reveals that organizations with CAIOs see positive returns but face strategic, technical and organizational obstacles to optimizing the role’s value. Improved metrics, teamwork and cultural modifications are needed.

Growing momentum

CAIOs are still relatively rare, but they may not be for long. About one-quarter of the 2,300 organizations surveyed reported having a CAIO, up from 11% in 2023. Two-thirds of respondents expect most organizations will have a CAIO within the next two years. Organizations that have appointed CAIOs say the primary drivers are to accelerate AI strategy and adoption.

The role typically involves oversight of AI strategy, technical implementation of AI projects, managing AI budgets and developing change‑management strategies. CAIOs most often come from the organization’s existing talent pool, with 57% having been promoted internally. Nearly three-quarters have data backgrounds while a majority also have experience in business strategy (57%), innovation (56%) and enterprise technology (54%). Such broad expertise is essential for translating AI potential into business value, the report says.

CAIOs understand the strategic importance of their role, with 72% saying their organizations risk falling behind without AI impact measurement. Nevertheless, 68% said they initiate AI projects even if they can’t assess their impact, acknowledging that the most promising AI opportunities are often the most difficult to measure.

Also, some of the most difficult AI-related tasks an organization must tackle rated low on CAIOs’ priority lists, including measuring the success of AI investments, obtaining funding and ensuring compliance with AI ethics and governance. The study’s authors didn’t suggest a reason for this disconnect.

C-suite is on board

Executive sponsorship no longer appears to be an issue: Eight in 10 CAIOs said they have sufficient high-level support for their function and 57 % report directly to the CEO or board. AI spending increased 62% as a share of information technology budgets over the past three years, and CEOs expect 31% annual increases through 2027. Nevertheless, 60% of organizations are still investing primarily in pilots, and only 25% of AI initiatives have delivered the expected ROI since 2023.

ROI remains elusive in part because tooling is still in flux. A typical organization currently uses 11 generative AI models and anticipates employing at least 16 by 2026. A proliferation of vendor-proposed use cases creates integration and interoperability challenges. Half of organizations said these issues limit their ability to apply their proprietary data to AI projects.

The report delineates a clear shift in operating models as AI projects scale. Initial efforts tend to be decentralized, but advanced organizations shift to centralized hub‑and‑spoke models. That approach moves twice as many pilots into production compared to a decentralized structure and realizes 36% higher ROI. That’s because centralization provides clearer ownership, according to Mohammed Al Mudharreb, CAIO of Dubai’s Road and Transport Authority.

Collaboration mandate

Though CEO sponsorship is critical, the authors also stressed the importance of close collaboration across the C-suite. Chief operating officers need to redesign workflows to integrate AI into operations while managing risk and ensuring quality. Tech leaders need to ensure that the technical stack is AI-ready, build modern data architectures and co-create governance frameworks. Chief human resource officers need to integrate AI into HR processes, foster AI literacy, redesign roles and foster an innovation culture.

The study found that the factors that separate high-performing CAIOs from their peers are measurement, teamwork and authority. Successful projects address high-impact areas like revenue growth, profit, customer satisfaction and employee productivity. The most effective teams combine AI specialists, machine‑learning engineers and business strategists, with AI experts embedded across functions to avoid the emergence of shadow AI operations.

The report stresses that appointing a CAIO alone doesn’t guarantee AI success. Organizations need a centralized operating model, integrated technology stacks and metrics that map to business value. Without HR support, robust data pipelines or secure IT infrastructure, even the most innovative AI projects will likely stall.

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