Where Should AI Sit? CAIO, CDO or CIO?

As AI becomes integral to business success, companies wrestle with where to place AI leadership in the org chart. Should AI be led by a dedicated Chief AI Officer (CAIO), folded under the Chief Data Officer (CDO), or managed within IT under the CIO/CTO? There is no one-size-fits-all answer​, but understanding the options’ pros and cons will help you choose a structure that maximizes AI’s strategic impact.

The Case for a CAIO (Chief AI Officer):

A CAIO is a C-level executive solely focused on AI strategy, implementation, and governance

This role has emerged as companies invest heavily in AI across functions—from automation to customer insights​. Having a CAIO signals that AI is a top priority. If the CAIO reports directly to the CEO, they can drive a cross-functional AI agenda and align AI initiatives with business goals (source).

Pros: Dedicated focus on AI innovation, enterprise-wide coordination, and a strategic voice at the top table. In fact, experts argue that adding a CAIO can’t come soon enough (provided the role has proper support and structure).

Cons: Introducing a new C-suite role can create overlap or confusion. If not clearly defined, a CAIO could step on the toes of CIO or CDO responsibilities or lack the clout to be effective. It’s essential that a CAIO be more than a technical expert; they must have business acumen to integrate AI into strategy and avoid becoming siloed.

Embedding AI Under the CDO:

Many organizations fold AI into an existing Chief Data Officer or a hybrid “Chief Data & AI Officer” role. This can work well since AI depends on quality data, and a CDO’s purview is to manage data assets.

Pros: A unified data and AI leader ensures that data strategy and AI projects are aligned. It streamlines leadership and avoids “too many chiefs.” In sectors like finance and healthcare, we see combined CDO/AI roles emerging, signaling that data and AI are two sides of the same coin.

Cons: The CDO already oversees data governance, quality, and analytics; adding AI could overstretch their focus. If the CDO’s background is primarily data management, they may underemphasize cutting-edge AI innovation. Some companies find merging the roles effective, but others risk diluting AI’s prominence.

The decision should hinge on whether your data function alone can drive the transformative AI agenda or if a distinct skill set is needed at the helm.

AI Under the CIO/CTO:

Traditionally, new technologies fall under the Chief Information Officer or Chief Technology Officer. Many firms initially task the CIO or CTO with AI initiatives.

Pros: It keeps AI within the technology organization for easy integration with existing IT infrastructure and security. The CIO/CTO already understands the tech stack and can incorporate AI projects without creating a new silo.

Cons: The risk is that AI gets treated as just another IT project rather than a cross-company transformation engine. A CIO’s focus on operational IT might limit the strategic, innovative scope of AI. Moreover, CIOs juggle many priorities—enterprise systems, cybersecurity, infrastructure—which could mean AI doesn’t get the dedicated leadership it deserves.

Strategic Influence Matters:

Whichever structure you choose, ensure the AI leader has a mandate to work across silos. AI’s value comes from transforming processes company-wide—from marketing and HR to operations (source).

If AI leadership is buried too low in the org chart, you miss out on strategic influence. For example, reporting directly to the CEO empowers the AI leader to collaborate with all business units and shape high-level decisions. Reporting under a CIO can also work, but only if that partnership still gives AI a “seat at the table” when setting strategy.

Key Takeaways – Pros and Cons at a Glance:

CAIO (Dedicated AI Chief):

Pros: Focused vision and accountability for AI; elevates AI to C-suite importance; cross-functional reach.

Cons: Potential role overlap; needs strong business understanding to be effective.

Under CDO (Data & AI combined):

Pros: Aligns data strategy with AI; leverages existing data leadership; fewer C-level roles to coordinate.Cons: CDO’s attention is divided; AI might take a backseat to other data priorities if not explicitly emphasized.

Under CIO/CTO (Tech-led):

Pros: Ensures technical integration; leverages existing IT governance; clear existing reporting lines.

Cons: Risks siloing AI as an IT project; may lack focus on business innovation; competing IT priorities could overshadow AI initiatives.

What’s the Best Choice?

It depends on your organization’s AI maturity and strategic goals. If AI is mission-critical to future growth or disruption in your industry, a dedicated CAIO reporting to the CEO might be warranted to underscore that importance. Indeed, about 11% of large firms have already designated a CAIO or equivalent, and 21% are actively seeking one. Even the U.S. federal government recently mandated all agencies appoint a chief AI officer.

On the other hand, if your AI efforts are still nascent or primarily an extension of data analytics, empowering an existing CDO or CIO with AI oversight can work—so long as you periodically reassess as AI capabilities grow.

The core principle is strategic alignment: place AI leadership where it can most effectively drive adoption, manage risk, and deliver value for your organization (source).