Who Owns AI? A Look at Leadership, CAIOs, and Centers of Excellence

Ask 10 companies who owns their AI strategy and, until recently, you likely would have gotten 10 different answers. That’s changing fast. Most companies now have, or plan to have, a single executive in charge of AI companywide, and that structure correlates directly with getting results.

The ownership decision extends to more than just AI strategy, and those involved carry significant influence. In fact, companies’ AI organizational structure directly relates to whether their investment in the technology pays off. The companies in Metrigy’s Research Success Group (those posting above-average business improvements from AI) are consistently the ones that have named an owner, built a team, and recognize the difference between who sets strategy and who funds the investment. .

The Rise of the AI Title

Go back a year and just 26.8% had a single executive in charge of AI strategy at all. Now, that number has reached 35.7% (the Success Group is already up to 48.1%), with another 37.7% still planning to add this year. Among those with a single executive in charge, 48.1% have specifically tapped AI-titled leaders, including Chief AI Officers, AI directors, AI managers, and AI strategists. 

Previously, the CEO or COO added AI strategy ownership to the already-full plate of CIOs or CTOs. That has now leveled off at 35.9% of companies who have single person leading AI strategy. As stated, the success group is further down this road, not only with more AI strategy leaders today, but with 41.2% more planned this year. Designated AI strategy leadership correlates with success.  A dedicated owner has the authority to kill weak projects, standardize tooling, and drive organizational excellence. Those are exactly the decisions that separate companies seeing solid ROI from companies burning budget on pilots that never ship.

There’s a quieter benefit, too. When one executive owns AI strategy companywide, employees know where to take an idea, a concern, or a failed pilot. Without that, promising proposals die in the gap between departments, and weak ones may sail through because no one has the standing to challenge them. A successful CAIO creates a clear decision path for AI projects, whether they’re in pilot or expansion mode.

Strategy vs. Technology Decisions

Naming a CAIO doesn’t mean handing that person the AI vendor decision-making. In most companies, the AI executive owns strategy while the CIO or CTO organization owns the technology purchasing decisions.The funding can come from either group, or a separate business unit.

That split can be beneficial, where the AI leader sets direction: what problems to solve, what’s in bounds, and what good looks like. The technologists who live with integration, security, and support handle selecting the right technology provider. Companies that blur the two roles tend to either buy tools nobody asked for or set strategy they can’t implement. Keep the strategist strategizing and the technologist selecting products, and you sidestep both traps.

Centers of Excellence: The Missing Muscle

If a single owner is the brain, a Center of Excellence (CoE) is the muscle, and most companies haven’t built it yet. Fewer than 30% have an AI CoE today, which is a thin showing for a capability this central to the business.

Here, the Success Group pulls ahead again, with 43.5% running a CoE, vs. 27.1% of the Non-Success Group. A CoE is a cross-functional team that supplies expertise, best practices, training, and governance—the connective tissue that keeps AI decisions consistent instead of every department reinventing the wheel. Companies without one aren’t doomed, but they become much less efficient and streamlined. They’re asking individual project leads to sort out governance, tooling, and skills on their own. That’s slow, and it’s wildly inconsistent.

What does a CoE do all day? In the companies that run one well, the CoE:

• Prioritizes the business problems and opportunities AI should address
• Establishes requirements and success metrics
• Sets standards for AI models, data requirements, and technology advances
• Vets tools before teams buy them
• Maintains the security, governance and prompt standards
• Establishes parameters for training so AI literacy isn’t limited to a handful of enthusiasts
• Establishes high-level budgets or spending guidelines

That work may be unglamorous, but it’s also the difference between AI that scales and AI that stays stuck in a few motivated pockets of the company.

The reporting line matters, too. Most commonly, the CoE reports to the CIO or CTO. That keeps it close to the technical resources it needs while the CAIO carries the strategic agenda.

Why Structure Shows Up in the Results

Skeptics may call this bureaucracy dressed up as progress, but the data says otherwise. Consider contextual awareness, which measures whether a company’s AI systems carry what they learn from one workflow or interaction into the next instead of starting cold every time. Only 35% of companies overall have that kind of full continuity. But 51.0% of companies with an AI CoE do, and 48.1% of those with full continuity have a single AI exec in charge of strategy. Metrigy’s research shows that this organizational discipline results in  AI that works better–because a team, backed by named leaders, are accountable for making it work better.

What CX and IT Leaders Should Do

If AI ownership at your company is still spread across three executives and four committees, you have a structure problem, and it’s probably costing you. Where I’d start:

•    Name a single owner. One executive accountable for AI strategy companywide, with the authority to set standards and stop projects.

•    Separate strategy from technology decision-making. Let the AI leader set direction and the CIO or CTO own the provider decisions.

•    Stand up a Center of Excellence, even a small one, and give it responsibility for best practices, training, and governance across teams.

•    Report the CoE into the CIO or CTO, with dotted line to the CAIO or AI strategy leader, so it stays close to technical resources.

•    Measure whether it’s working continuously. Track context retention, project completion, and ROI, and hold the owner to them.

The companies winning with AI didn’t get there by buying more tools. They got there by deciding who’s in charge, then giving that person a team. If you’re still asking, “Who owns AI around here,” that’s your first project.

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Author(s)

Robin Gareiss