CTO or CAIO? Before You Add a Second Seat

A technology leader can approve a system’s architecture while a business leader decides whether to fund it. That division can be deliberate and useful. The problem starts when each can approve their part, but nobody has to bring the evidence together for a decision on the whole. Before adding a seat, distinguish an unassigned decision from work the current leader lacks the time or judgment to do.

How a pilot survives

Consider a support assistant that drafts customer replies. Suppose it produces drafts quickly, but checking them absorbs the time it saves. The technical lead can improve the model or the information it uses. The head of support can change how agents review replies. Finance can challenge the savings calculation.

Each has a legitimate piece of the decision. But if none has responsibility for bringing that evidence together and deciding whether to keep spending, the pilot can continue without anyone having to defend its value. Renewing it preserves the possibility that somebody else’s next improvement will make it work.

To judge this pilot, compare the complete AI-assisted workflow with the current one on comparable support requests and the same quality standard. Count review and correction, including drafts discarded and written again. The business case also needs the tool and operating costs. Timing only the generation step lets you make the system look faster by moving work into review.

Even a genuine reduction in handling time can release capacity without reducing spending. If the support team uses it to clear a queue with the same payroll, the result is more service for the same labor spend. If the proposal promises lower costs, finance needs to identify an expense that will actually fall. Either benefit can justify a trial; they need different evidence.

An owner with authority could choose another test, narrow the use case, or stop. Stopping would be a useful result if the trial exposed an uneconomic workflow. Counting every abandoned pilot as a leadership failure would reward keeping bad ones alive.

The Gartner forecast from July 2024 helps explain why abandonment alone tells you little. It predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, rising costs and unclear business value. That was a forecast, not a measured abandonment rate or a finding about executive titles.

A clear owner can still encounter bad data or a tool that costs more than the work it replaces. The ownership question is whether someone can act on that evidence, including accepting that the experiment has answered the question.

Keep the investment and the implementation together

If the CTO can compare AI proposals with other technology investments and has time to oversee their delivery, our preference is one AI-fluent technology executive. Keeping the work together lets the same person weigh a proposed automation against the integration, security and maintenance it will require.

The support assistant makes the tradeoff concrete. A faster model might produce answers that need more checking. Better access to customer records might improve accuracy while creating a permissions problem. A technically promising change can consume the savings it was supposed to produce. Separating AI strategy from responsibility for the running system means those tradeoffs need agreement across two executive remits.

That coordination can work. But when the same leader can competently hold both decisions, creating another executive boundary needs a reason beyond the novelty of AI.

One accountable executive still needs other people to do their jobs. The head of support owns the operating change; finance checks the claimed return. The technology leader brings those judgments into a funding decision, or takes a recommendation to the person who holds the budget. The CEO has to make that authority explicit. Existing security and compliance powers to stop unsafe work stay in force.

Keeping one seat also concentrates advocacy and judgment in the person who may have championed the pilot. Support needs to be able to reject an unacceptable workflow, and finance needs to test the benefit using operational records rather than the project owner’s summary. Fewer executive handoffs buy little if the same person can declare the experiment a success unchallenged.

Nor does assigning the work create time for it. If your CTO is already handling incidents, hiring and delivery commitments, adding AI accountability without removing work gives you a name to blame. Promoting an engineering manager or hiring an AI specialist may address the bottleneck more directly than adding a peer executive. The distinction is whether the missing work is execution or company-wide prioritization.

When a CAIO earns the job

IBM’s July 2025 report on Chief AI Officers, built around a survey of 624 AI leaders, reported 10% greater return on investment (ROI) from AI spending at organizations with a CAIO.

The survey captured responses at a point in time and included equivalent AI leadership roles. The association leaves open whether appointing a CAIO produced better returns or whether other differences, such as resources already available, explain them. It also leaves room for an existing executive to hold that remit. The result warrants considering a CAIO; it doesn’t settle whether you need a second seat.

IBM’s own advice is more demanding than adding a title. The report calls for a clear mandate and warns against creating a separate AI technology department that duplicates the existing one.

If your company has several business units competing for AI investment, setting common priorities and standards may become a substantial job of its own. A capable CTO can run the shared platform without having the authority or capacity to redesign those units’ operations. Regulatory obligations can add coordination and governance work. In that setting, a CAIO can own the cross-company program while the CTO owns the technical platform, provided the business leaders also commit people to changing the work.

The boundary has to survive a disagreement. Work through the support assistant with a proposed mandate: the CTO can block access that breaches the agreed security policy; the CAIO can choose whether a compliant, narrower version still merits AI budget; the CEO resolves a dispute over business priorities. If your actual budget or risk authority sits elsewhere, name that person instead. Before hiring, both leaders should be able to describe the same route to a decision.

What keeping one seat costs

Keeping one executive seat requires genuine AI competence and time for the work. If an otherwise capable leader needs help judging a model or designing an evaluation, a senior AI practitioner may fill that gap without taking over strategy.

Use a live proposal to assess the gap. Have the leader work through it with the people who would use the system, showing how the workflow evidence supports another trial, a limited rollout, or a stop. Knowing which specialist to involve is part of that judgment. Doing every evaluation personally would turn a leadership role into another execution bottleneck.

If the person has the authority and capacity but can’t develop that judgment, keeping one seat means confronting whether they belong in it. That can be a worse afternoon than approving a new chair. Hiring a CAIO leaves the CTO’s remaining technical responsibilities in place; someone still has to be competent to carry them.

If that’s the reason for the new role, put the capability gap in front of the board before you put a second job description there.