CIOs and CTOs are making high-stakes decisions with incomplete information, IBM survey reveals

Most CIOs and CTOs are accountable for systems they don't fully control, as AI adoption outpaces current governance capabilities.

A new global study of 2,000 C-level technology executives by IBM has found that two-thirds are forced to take responsibility for AI systems that they can't realistically supervise, with only 11% saying they're completely prepared for the scale of AI agent deployment.

Respondents said that by 2027, they're expecting a 38% increase in the number of AI agents deployed; and while eight-in-ten report being given transformation mandates driven by the CEO, 77% said their AI adoption is already outpacing current governance capabilities.

"For CIOs and CTOs, the challenge now is scaling AI systems that operate continuously and autonomously, often within governance models and architectures designed for a far slower, more predictable environment," said Matt Lyteson, CIO at IBM.

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"It is no longer just about deploying AI faster. It's redesigning how organizations control, govern, and invest in it and embedding control and visibility from the start, so they can scale with confidence."

The firm's analysis revealed that in organizations relying on manual governance, incident risk increases as AI adoption scales, whereas those that embed control directly into their AI systems experience 25% fewer incidents.

Nearly six-in-ten of the CIOs and CTOs surveyed cited security and compliance concerns as top barriers to scaling AI agents.

Organizations experienced an average of 54 AI agent incidents last year in which an unintended and/or harmful occurrence required human correction. And of those, 17% were high severity, requiring more than four hours to contain: 37% resulted in data exposure or security breaches, 33% caused cascading system failures, and 17% triggered compliance issues.

"The pressure is only intensifying. CEOs want AI scaled faster. Business leaders want autonomy. And expectations for accountability continue to rise," said Lyteson.

"Yet many organizations are still operating with architectures, controls, and funding models designed for human-speed decision making, in systems that now operate at machine speed."

While AI spend is projected to grow from just under 15% of IT budgets in 2025 to nearly 25% by 2027, 84% of tech CIOs and CTOs haven't fully operationalized AI financial management, and 85% still lack full visibility into real-time AI spend.

However, organizations that build control into their AI systems deploy 16 times as many AI agents as those relying on manual governance, deliver 18% higher operating margins, and spend a quarter of their AI budget.

Those with strong financial discipline deploy 2.4 times as many AI agents, without driving up their AI/IT budget, and are three times as likely to say they are fully prepared for AI scale.

Meanwhile, those that have designed for adaptability early – keeping workloads portable and models replaceable rather than locked into hard dependencies – reported a 10% higher return on AI investment in 2025.

"The key constraint is adaptability. Most organizations were built for control, standardization, and efficiency," said Lyteson.

"But in an AI-driven landscape, it is now architecture, governance, and investment decisions that establish how fast they can move, what risks they can absorb, and which opportunities remain viable."