Chief AI Officer or AI agency? Who should own AI in your company

Choosing between a Chief AI Officer and an AI agency is the wrong starting point. The useful question is who owns three things: the agenda (which problems artificial intelligence, or AI, should solve, and what revenue, cost or risk result each must deliver); delivery (working systems that run inside your operations, not demos); and the controls (what data AI may touch, which risks are acceptable, and how the people whose work changes are trained).

Each option covers different parts. A Chief AI Officer (CAIO), an executive whose job is AI across the whole company, can own the agenda and the controls but needs a team or partners to build. An agency builds but rarely owns the agenda. A strategy consultancy shapes the agenda but usually hands off delivery. Whatever you choose, someone inside your company has to own AI and answer for its results.

Why the Chief AI Officer role is spreading

• In a survey of more than 600 CAIOs in 22 countries, the IBM Institute for Business Value and the Dubai Future Foundation found that 26 percent of organizations had a CAIO. Those with one reported 10 percent higher return on investment (ROI) on AI spend, and 57 percent of CAIOs had been appointed from inside (IBM and Dubai Future Foundation, 2025).
• In 2026, IBM surveyed 2,000 chief executives and reported that 76 percent of their organizations had a CAIO (IBM, 2026). These surveys use different samples, so read them as a role that is spreading quickly, not as a single trend line.
• A survey of 3,739 senior technology decision-makers by Amazon Web Services (AWS) found that 60 percent of organizations had a dedicated AI executive such as a CAIO (AWS, 2025).
• The Office of Management and Budget (OMB), the White House office that sets management policy for US federal agencies, requires every agency to designate a CAIO to promote AI adoption, manage the risk of high-impact AI and keep an inventory of AI uses (OMB memo M-25-21, 2025).

Two cautions. Titles vary: Gartner found that 70 percent of chief data and analytics officers hold primary responsibility for AI strategy (Gartner, 2025), and Heidrick & Struggles found that many organizations have rebranded existing executive roles to include AI oversight (Heidrick & Struggles, 2025). And these surveys lean toward large enterprises; for a company of 200 people, the mandate matters more than the title.

Five ways to own AI, compared

1. A full-time Chief AI Officer

A senior executive dedicated to AI who sets priorities, builds a team and runs governance.

• Pros: full attention, authority across departments, and internal capability that grows over years.
• Cons: a costly hire that takes time to fill and still needs a team to deliver. The role can overlap with the heads of technology, operations or data. Researchers writing in MIT Sloan Management Review advised making it a fixed-term appointment that builds capabilities and hands them to the business (Fortune, 2024).
• Fits when: AI is central to how you compete and you can fund a team around the role.

2. A fractional or part-time CAIO

An experienced AI leader who works with you a set number of days per month.

• Pros: senior judgment without a full-time salary, a fast start, and help setting priorities, policies and a first roadmap.
• Cons: limited hours and usually no delivery team. Strategy can outpace execution, and the knowledge stays with one outside person unless it is written down.
• Fits when: you need direction and guardrails first and have a way to build.

3. An AI agency

A firm that builds chatbots, automations, integrations and agents, per project or on retainer.

• Pros: speed, specialized skills and working software. A 2025 report from a research initiative at the Massachusetts Institute of Technology (MIT) found that buying from specialized vendors and building partnerships succeeded about 67 percent of the time, while internal builds succeeded one-third as often (Fortune, 2025).
• Cons: an agency builds what you ask for and may not question whether it is the right problem. Without an agenda, you collect disconnected tools, and accountability usually ends at delivery.
• Fits when: the use case is clear, someone inside owns it, and success is defined up front.

4. A traditional strategy consultancy

A firm that analyzes your market and operations and recommends where AI fits in your strategy.

• Pros: rigorous analysis, cross-industry perspective, credibility with boards and investors, and strength in organizational change.
• Cons: the engagement often ends with recommendations, and implementation passes to someone else, where momentum can be lost.
• Fits when: the question is strategic, such as a new business model or an acquisition, and you can act on the answer.

5. A strategy and execution partner

A firm that decides with you where AI creates value, then builds and runs the systems alongside an internal owner. This is how we work, so weigh it accordingly.

• Pros: one team from decision to production, accountability tied to a business metric, and knowledge transferred to your people as the work proceeds.
• Cons: the same firm judges what to build and then builds it, so ask for measured baselines and be wary of a partner that never says no. It also needs an internal owner who has time for it.
• Fits when: you have few or no use cases in production and want measurable results before building an internal function.

Someone inside the company must own AI

Every option assumes an executive sponsor: a senior leader, often the chief executive, chief operating officer or head of finance, who sets AI priorities, holds the budget, makes the calls on data and risk, and answers for results. Outside partners can advise and build. They cannot own your priorities, your customers' data or your people.

Research points the same way. Gartner found that 91 percent of leaders at organizations with high AI maturity had appointed dedicated AI leaders (Gartner, 2025). The MIT report stressed empowering line managers, not only central AI labs, to drive adoption (Fortune, 2025).

Make knowledge transfer part of any outside contract: documentation, code and prompts kept in your own accounts, training for the people who run the systems, and a handover plan. You should be able to change partners without starting over.

A decision guide by stage

Early: no AI in production. Name an executive sponsor first. Pair them with a fractional CAIO or a strategy and execution partner to choose one or two use cases tied to revenue, cost or risk, and put them into production. A full-time CAIO is usually premature unless AI is core to your product.

Scaling: a few use cases running. The work now is repeatable delivery and honest measurement. Use an agency or partner for builds, keep the internal owner in charge of priorities, and write policies for data access, human review and vendor approval. Start developing an internal AI lead.

Mature: many use cases, governance needed. A full-time CAIO, or an existing executive with that explicit mandate, now earns the cost: portfolio decisions, governance, budgets and risk across the company. Outside firms shift to specialized builds and extra capacity.

1. 01Early: no AI in production
   • Executive sponsor inside
   • Fractional CAIO or strategy and execution partner
   • One or two use cases in production
2. 02Scaling: a few use cases
   • Internal owner sets priorities
   • Agency or partner for builds
   • Policies for data, review and vendors
3. 03Mature: governance needed
   • Full-time CAIO or explicit mandate
   • Portfolio, budget and risk
   • Outside firms for specialized builds

What this means for your business

Start with the three ownership questions, not the org chart. Who decides which AI work is worth doing? Who builds systems that run in your operations? Who controls data, risk and the people side? If the answer to all three is "an outside firm," you have a gap, whichever firm it is.

For most owner-led and mid-market companies, the practical answer is an internal executive who owns AI, supported by outside help matched to your stage, with a plan for what your own team will own next.

If you are deciding who should own AI in your company, book a meeting with us. If an outside firm is not what you need, we will say so.