The AI accountability gap: CEOs own strategy, CIOs carry decisions

CEOs claim AI ownership, but can’t be in every AI decision

The top-line numbers are striking. In our new report, a strong majority of CEOs (70%) say they are the primary driver of AI strategy within their organization, well ahead of other roles they identify, including CIOs (16%), Chief Data Officers (6%), and IT departments (5%).

On paper, that might sound like alignment since AI has executive sponsorship, the CEO is engaged, and the mandate is clear. However, the next layer of data tells a different story. Only 60% of CEOs say they participate in more than half of AI-related decisions, and just six percent say they are involved in nearly all of them.

That gap is significant. AI strategy is shaped by a long series of choices: platform selection, governance models, data access, security controls, deployment standards, approval workflows, and operating responsibilities across teams. If the executive who owns the strategy is at all removed from those decisions, accountability becomes uneven.

Ultimately, the CEO still owns the outcome and the CIO ends up managing the reality.

Why the AI accountability gap creates pressure for CIOs

The AI accountability gap is not a symbolic issue. It changes how enterprise AI gets built.

For instance, CIOs are already carrying the operational load in many enterprises. They are expected to move quickly, support business innovation, manage architecture decisions, and prevent governance from becoming an afterthought. When CEO involvement is directional rather than consistent, that burden grows.

The report offers a useful signal here: 67% of CEOs say they have questioned or challenged AI vendor or platform decisions made by their CIO or other team members in the past year, including nearly a quarter who have done so multiple times.

That suggests a pattern many technology leaders recognize. The CIO and their teams do the work of evaluating tradeoffs, aligning stakeholders, and making implementation decisions. Then executive review comes later, sometimes after momentum has already formed around a platform, tool, or use case.

That late-stage involvement can slow decisions. It can also create friction between strategic intent and execution reality.

For CIOs, this looks like an organizational concern. They have to create structure in an environment where ownership is centralized at the top, but decision-making is spread across the business.

The CIO role is getting broader

AI has expanded the CIO brief. It’s about more than infrastructure, applications, or data platforms.

CIOs are increasingly expected to:

• Translate executive AI ambition into an operating model.
• Align business, data, and IT teams around shared priorities.
• Set guardrails for experimentation and production use.
• Manage vendor and platform complexity.
• Ensure AI initiatives remain traceable, governable, and connected to business value.

That is a larger leadership role than many organizations planned for, but someone has to hold the system together while AI spreads across functions, tools, and teams.

AI decision-making is becoming more distributed

The accountability gap becomes even more important when more people across the enterprise can build and deploy AI.

According to the report, 94% of CEOs say low- or no-code tools are critical to scaling AI creation across the workforce. That reflects a major shift in how AI is entering the enterprise. More business users, analysts, and operational teams now have access to tools that let them create, test, and apply AI directly.

That is a good thing when it is managed well. It can speed up adoption, increase business relevance, and expand the number of teams contributing to AI-driven outcomes. It also increases the number of decisions being made outside a small executive circle.

Every new builder introduces more choices around data, models, prompts, workflows, review processes, and acceptable use. As adoption broadens, governance can no longer live in a policy deck. It has to be embedded in the way work gets done.

That is why this conversation matters so much for CIOs. If AI creation is spreading across the workforce, then the decision surface is expanding too. The central question that emerges is whether the enterprise has a clear model for how AI decisions get made and governed at scale.

CIOs require clear decision rights

Executive sponsorship remains essential. After all, AI programs rarely scale without it.

Still, sponsorship alone is not enough. Enterprise AI works best when decision rights are clear. Who approves what? Which decisions sit with the CIO, and which require executive involvement? Where do data leaders, security teams, business owners, and procurement teams enter the process? What happens when teams want speed but the risk profile is still unclear?

Without clarity, enterprises drift into a pattern that looks like this: broad enthusiasm at the top, fragmented execution in the middle, and reactive governance once issues surface.

That pattern creates avoidable risk. It also slows down the very progress leaders say they want.

The report points to another sign of how much this is changing: 56% of CEOs believe that within the next two years, experience managing a successful AI strategy will become a core competency required of the role itself.

That expectation raises the bar for everyone. CEOs will be judged more directly on AI outcomes. CIOs will be expected to deliver the systems, controls, and coordination that make those outcomes possible.

What CIOs should do next to close the AI accountability gap

For CIOs, the immediate priority is not to absorb more responsibility by default. Instead, it’s to make AI accountability explicit.

That starts with a few practical questions:

Who is making AI decisions today?

Many organizations assume they know where decisions sit. Fewer have actually mapped them. Vendor selection, use case prioritization, governance approvals, and deployment standards often live in different places.

Where is executive involvement required?

Not every AI decision needs CEO participation, even while some absolutely do. The key is distinguishing strategic decisions from operational ones before a program reaches a bottleneck.

How can governance be built into enablement?

If low-code and no-code adoption is a strategic priority, governance needs to scale with it. That means review processes, access controls, model oversight, and traceability have to be part of the workflow.

What’s needed to create a shared operating model for AI?

CIOs should not be left to mediate every conflict informally. The stronger path is a documented model that clarifies responsibilities across the CEO, CIO, CDO, IT, security, and business teams.

How do decisions tie back to business outcomes?

AI accountability becomes clearer when decisions are linked to measurable goals. That keeps strategy grounded and reduces the chance that platform or vendor debates drift away from business value.

The future of enterprise AI leadership depends on operating discipline

The main takeaway here is that AI leadership has become more complex than traditional executive ownership models can handle on their own.

AI cuts across technology, operations, governance, and business performance. It touches more systems, more teams, and more decisions. That makes shared accountability necessary, but it simultaneously makes clear operating discipline non-negotiable.

For CIOs, that creates both pressure and opportunity.

The pressure is obvious — they are often the ones holding together an AI strategy that is highly visible at the top but unevenly managed across the enterprise.

The opportunity is equally important. As AI becomes central to enterprise performance, CIOs are increasingly in position to shape how strategy becomes execution by defining the structures that make AI scalable, governable, and durable.

That is precisely where the accountability gap can start to close.