Chief AI Officer: What the Role Owns and When to Hire One

A Chief AI Officer (CAIO) is the executive who answers for how a company uses artificial intelligence: which projects get funded, how they reach the people who will use them, and what rules keep them safe and legal. The title has spread quickly, which leaves a CEO or board with a practical question: does the company need one, or does the work belong with the CTO, the CIO or an outside adviser?

This guide explains what the role covers, how common it has become, the signs that a company needs one, and how it differs from a CTO or an AI consultant. It ends with a one-page mandate to write before a search and questions for judging candidates.

DisclosureJake Jorgovan co-founded Alpha Apex Group, which is mentioned in this article.

What a Chief AI Officer is responsible for

IBM's 2025 survey of more than 600 Chief AI Officers describes the role's mandate as four tasks: defining the organization's AI strategy (opens in a new tab), directing the implementation of AI technologies, managing AI budgets, and developing change management strategies so people adopt it.

The US government has published a detailed definition of the role. Memorandum M-25-21 (opens in a new tab), issued by the Office of Management and Budget on April 3, 2025, requires each federal agency to name a Chief AI Officer and lists ten duties for the role. They include serving as the senior adviser on AI to the agency head, running a governance and oversight process, keeping an inventory of where AI is used, setting up review and monitoring for "high-impact" uses, advising on an AI-ready workforce and advising the agency head and CFO on AI investment. It binds only federal agencies, but it is a useful checklist for a company writing the role from scratch.

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The approach described in this article, shown as a visual guide.

How common the role has become

The title is new, and the numbers are rising fast. In IBM's 2025 study, 26% of the more than 2,300 organizations it contacted said they had a Chief AI Officer, up from 11% in 2023. In IBM's 2026 CEO study (opens in a new tab), a survey of 2,000 CEOs and equivalent senior leaders run from February to April 2026, 76% of surveyed organizations had one. The two figures come from different samples, and neither covers every company, but both point the same way.

The 2025 study also shows how the role is set up where it exists. 57% of the CAIOs surveyed were appointed from inside their organization, 57% report directly to the CEO or the board, and 61% control their organization's AI budget. Promotion from within is common, and more often than not the seat sits close to the top.

Do you need a Chief AI Officer?

IBM's study offers a useful test. The CAIOs it surveyed said the role was created for two main reasons: to drive AI strategy and to accelerate adoption. While a company is piloting AI projects, that coordinating role is "less critical". When it is time to turn pilots into enterprise-level investments, someone has to set a clear direction, make portfolio-level decisions and keep teams focused on shared goals.

Signs that you are at that point:

• Several functions are running AI projects, and nobody decides which ones get funded or stopped.
• AI spending is spread across budgets, and you cannot say what it adds up to or what it returns.
• Customers, regulators or the board are asking how you govern AI, and there is no single answer.
• AI is changing how core work is done, and adoption needs someone to lead the change, not just the technology.
• The CTO or CIO has too much on their plate to give AI the attention the board expects.

Signs that you are not there yet:

• AI work is a few small pilots that an existing technology leader can oversee.
• You cannot yet describe what the role would decide that someone else does not already decide.
• There is no budget or authority to give the role. Without either, the title gives the person little to work with.

If you are unsure, gather facts before creating a permanent C-suite seat. I've found that when a decision isn't clear, it's often because you lack the facts to make it, and the way to get them is to act. Here, that can mean naming one executive as the owner of AI for a set period, such as two quarters, with a written remit and a budget, or bringing in an interim or fractional leader for a defined period. Before the period ends, review the decisions the owner made, the authority they lacked and the work still uncovered. That evidence tells you whether a permanent role is needed and what its mandate should be.

Chief AI Officer, CTO, CIO or Chief Data Officer

The roles overlap, so be clear about the difference. A Chief AI Officer owns how AI is used across the business: which uses to pursue, how they are governed and whether people adopt them. IBM's study describes the CAIO depending on the others to do that. The CTO keeps AI aligned with enterprise IT and technology strategy. The CAIO works with the Chief Data Officer on data strategy, data quality, AI governance and analytics, with the CISO on security, and with the CHRO to build employee support.

In your mandate, a useful split is to give the Chief Information Officer the company's business systems and IT services, the CTO technology development and architecture, and the CAIO AI priorities, governance and adoption. That division is this guide's suggestion. Agree the boundaries for your company, because the titles alone do not settle them.

The roles can also be combined. M-25-21 lets an agency head designate an existing Chief Information Officer, Chief Data Officer or Chief Technology Officer as the Chief AI Officer, provided that person "has significant expertise in AI". It also requires the CAIO to have the authority to carry out the duties and to sit senior enough to work regularly with the agency's top leadership. The same test works in a company: if an existing executive has the depth, the time and the authority, give them the remit and the title. If AI would be one more item on a full agenda, it will get the attention that implies.

Your options for AI leadership

A new full-time executive is one of several ways to put someone in charge of AI. The choice between a permanent Chief AI Officer, a head of AI, a fractional leader and a consultant comes down to how much leadership you need, and for how long.

In this guide's view, the difference between a Chief AI Officer and a head of AI is scope and seat. A head of AI runs the teams that build and operate AI systems, usually under a technology executive. A Chief AI Officer answers for strategy, governance and adoption across the business. An AI consultant delivers a defined piece of work, and the decisions stay with someone inside.

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The approach described in this article, shown as a visual guide.

For providers, see the comparison of fractional Chief AI Officer services and AI consultancies. For a permanent hire, see AI recruiters and executive search firms.

Cost belongs in the same decision. No primary source for the title's pay was found for this guide, so ask a search firm for current compensation data before you set the range or choose between a full-time hire and an interim or fractional leader.

Write the mandate before the search

One of the biggest hiring mistakes I've made, and seen others make, is finding someone impressive and then working out where they fit. As I put it in a 2020 episode on hiring: "If you have a great person that doesn't fit within your organization, you're just setting yourself up for a disaster." Define the role first. With a title as new and loosely used as this one, that matters even more: two candidates with the same title may have done very different jobs.

The second document I'd write, before the hire if you can and by their first day at the latest, is what success looks like at 90 days: what the person needs to have achieved, owned and put in place by the end of their first quarter. It gives the new leader a clear path, and it gives you a way to judge whether the hire is working.

A useful 90-day line is concrete: an inventory of the AI already in use, a short list of the uses worth funding, a draft governance policy, and one recommendation on the decision that triggered the hire.

How to judge Chief AI Officer candidates

The role is part technology and part business, so test both on your own situation. Give each finalist a real scenario from your company, such as a use case a function wants to fund or a governance question the board has raised, and ask how they would decide. Ask for evidence from past work: what an AI project they led changed, how they measured it, and what they stopped.

The last question tests something I learned in hiring: be wary of someone who wants the role mainly as a step toward a different one. A candidate who really wants to be the CTO may treat AI governance and adoption as a detour.

Involve the leaders whose functions AI will change, such as finance, operations, marketing, legal and HR, in the final interviews. Their questions show whether the candidate can explain AI decisions to people who are not technologists, which is much of the job. Line up approvals, compensation and the offer before the final round, so that once you decide you can move quickly and a strong candidate is not lost to delay.

Where Chief AI Officer hires go wrong

• The role overlaps with others. Without a written split of decisions, the CAIO, the CTO and the Chief Data Officer collide, and accountability blurs.
• The title comes without authority. M-25-21 requires federal CAIOs to have the authority to carry out their duties. Give a company CAIO explicit decision rights and access to the leaders who approve AI spending; the title alone supplies neither.
• Expectations run ahead of the work. Boards may expect fast results. Moving from pilots to daily use takes integration, data work and change management. IBM's study lists change management among the role's four core tasks.
• Adoption is treated as a technology problem. In IBM's 2026 CEO study, 83% of CEOs surveyed said AI success depends more on people's adoption than on technology.
• Governance lags behind use. NIST's voluntary AI Risk Management Framework (opens in a new tab), version 1.0, which NIST says it is revising, asks for documented roles and responsibilities for managing AI risk, and for executive leadership to take responsibility for decisions about AI risks. Someone at the top has to own that.

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NIST’s AI RMF 1.0 places governance across three ongoing functions: map, measure, and manage. The framework is voluntary, and these functions are iterative rather than a one-time checklist.airc.nist.gov (opens in a new tab)

If you wrote the mandate today, could you say what your Chief AI Officer would decide that nobody decides now?