Chief AI Officer: What the Role Needs From Operations Before the First Use Case Is Funded

A chief AI officer (CAIO) is the executive who owns an organization’s AI strategy: which use cases get funded, how AI is governed, and whether the spending pays back. In most companies the first use cases come from operations, and before any of them is funded, the chief AI officer needs something only the operations leader can supply: a measured account of how the work gets done today.

Here is how that request usually goes. Picture a VP of claims, call her Dana, with 220 people and a board that wants a third of the cost out by next year. She builds the business case the way most of us were taught: average handle times from the claims system, a vendor’s estimate that most of document intake can be automated, and a workshop with her team leads.

The AI team reads it in an afternoon and sends back three questions. How many hours a week does intake actually take? How much of it is rework? What will we measure afterwards to show it worked? Dana can’t answer any of them from what she has, and her use case goes back in the queue.

If you run AI strategy, that exchange will sound familiar, and so will the frustration on both sides. This article covers the role and its responsibilities briefly, then the part the other pages on the role leave out: the five things a chief AI officer should require from operations before funding a use case, and why the top-down estimates operations usually sends keep failing that test. If you’re in that conversation right now, book a 30-minute strategy call and we’ll walk through what your AI team is going to ask for.

Key Takeaways

• A chief AI officer owns AI strategy, the use-case portfolio, governance, and the return on AI spend. In an IBM Institute for Business Value study of more than 600 CAIOs, 26% of organizations had one, and organizations with one reported 10% higher ROI on AI spend.
• Operations is where most first use cases come from, because that is where most of the people and hours are. The approval has moved too: the AI team now ranks operations projects against every other AI initiative in the company.
• Before funding, the CAIO needs five measured inputs from operations: hours by activity, volume and the spread between people, where work crosses systems or gets redone, a cost baseline, and what must not break.
• Top-down estimates (output data, exposure studies, and workshops) supply none of the five. In the same IBM study, 68% of CAIOs said they start AI projects even when they can’t measure the effect.

What Is a Chief AI Officer?

A chief AI officer is the senior executive accountable for how an organization uses AI: the strategy, the portfolio of use cases, the governance that keeps it safe and compliant, and the evidence that it pays back. Some companies hire a dedicated CAIO. Others give the title to an existing chief information officer (CIO), chief technology officer (CTO), or chief data officer.

The role is real and spreading. The IBM Institute for Business Value surveyed more than 600 CAIOs across 22 countries and 21 industries in early 2025 and found that 26% of organizations have a chief AI officer (IBM, 2025). In the US federal government it is now required: OMB memorandum M-25-21 gave every agency 60 days to retain or designate a chief AI officer, and lets an existing CIO, CDO, or CTO hold the role if that person has significant AI expertise (OMB M-25-21).

Titles vary. A VP of AI, a head of AI strategy, or an AI lead often does the same job with a smaller remit. On this page, “chief AI officer” covers all of them.

Chief AI Officer Responsibilities

Chief AI officer responsibilities fall into six areas. Two of them depend almost entirely on operations data: choosing which use cases to fund, and proving what they returned.

1. AI strategy: Deciding where AI should change the business, and in what order.
2. Use-case portfolio: Taking requests from every function, ranking them, and funding a few.
3. Governance and risk: Policies for responsible use, model risk, data privacy, and regulation.
4. Build, buy, or partner: Choosing vendors and platforms, and deciding what to build in-house.
5. Adoption: Getting AI used by the people whose work it changes.
6. Measuring value: Showing the CEO and the board what the AI spend returned.

Most pages about the role spend their words on strategy, governance, and adoption. The portfolio and the measurement get a line each, usually something like “prioritize high-value use cases by ROI.” That line hides the hard part. You can’t rank a use case by return without knowing what the work costs today, and in an operation that number lives with the operations leader, often in no system at all.

Why the First Use Cases Usually Come From Operations

Operations is where most of the people are, so it is where most of the hours, the cost, and the repeatable work sit. Picture a 400-person organization with 300 of them in operations. That’s the first place an AI team looks for savings, and the first place a funding decision goes wrong if the data underneath it is thin.

The way these projects get approved has changed as well. For most of my career, an operations leader with a large enough budget could sign off an improvement project alone. Now the AI strategy team has to rank it against every other AI initiative in the company, from a sales copilot to a narrow research tool. When I describe a measurement project to an operations leader today, a common reaction is some version of this: “I need to get our AI person on the phone. We just hired someone to prioritize all our AI projects.”

If you’re reading this as that person, the case that reaches you from operations was probably built for the old approval path. It was written to persuade a budget holder. What you need from it is a baseline you can fund against and measure against later, and those are different documents.

What a Chief AI Officer Needs From Operations Before Funding a Use Case

Before funding an operations use case, a chief AI officer needs five inputs from the operations leader, all measured: the hours the work takes today by activity, the volume behind those hours and how much they vary between people, where the work crosses systems or gets redone, a cost baseline to judge the result against, and a list of what must not break.

1. Hours by activity, below the job title

A job title tells you who does the work. It says little about what they do all day. A claims examiner might spend a morning on intake, a lookup across three systems, a call to an adjuster, and a correction to someone else’s file. A use case targets one of those activities, so the CAIO needs hours at that level: how many hours a week go to document intake, to status checks, and to rekeying data between systems.

System reports can’t supply this. The systems operations run on record what happens at the end of the process: claims processed, tickets closed. None of them was built to record the work it took to get there, which is why operations leaders end up exporting reports and running macros to rebuild an estimate of it by hand.

2. Volume, and the spread between people

An average hides the opportunity. The same activity, done by two people, can take very different amounts of time depending on experience, the mistakes they make, and how much they redo. Andy, our COO, puts it this way: when you see the same activity captured across a team, it runs through different workflows depending on who does it, and that spread is where the opportunity is.

For the CAIO, the spread changes the case. If your slowest quarter of the team takes three times as long as your fastest on the same activity, part of the gain may come from standardizing the work before any model touches it. That usually costs less than an AI build, and it should be known before money is committed.

3. Where the work crosses systems or gets redone

Copying data from one screen to another, checking a status in a second system, and fixing work that came back are often the best first targets for automation and AI agents. They are also invisible in output data, because they produce nothing that gets counted. They show up only when someone looks at what people actually do at the desktop. Our guide to agentic AI use cases in operations covers which of these tend to be ready for an agent.

4. A cost baseline to measure against

Every use case needs a before. Without a measured cost of the work today, the AI team can’t show what a project returned, and finance can’t tell a real saving from work that moved to another desk.

In the IBM study, 72% of CAIOs said their organization risks falling behind without AI impact measurement (IBM, 2025). The baseline is the denominator that measurement needs. How to measure AI ROI in operations walks through the calculation.

5. What must not break

Operations leaders carry service level agreements (SLAs), compliance deadlines, and work that only experienced people can do. The default cut goes after the most senior and most expensive people, because they produce the biggest saving on paper. It is also the cut most likely to damage SLAs and customer service. The CAIO should ask for that list up front, because it rules some use cases out before anyone builds a pilot.

The hand-off in one table

If your operations team can’t fill in the right-hand column yet, that’s the conversation to have before the use case goes to committee. Book a 30-minute strategy call and we’ll look at what it would take to measure it.

Why Top-Down Estimates Keep Failing That Test

Top-down estimates fail the chief AI officer’s test because they start from totals (headcount, output volumes, industry averages) and divide down. None of them observes the work, so none of them can produce hours by activity, the spread between people, or a baseline that still means something after the project ships.

They usually come from three places:

• Output data: Cases closed and transactions processed count the end of the process. They say nothing about the effort, the rework, or the lookups behind each one.
• Exposure studies and vendor benchmarks: These score occupations or generic tasks. They can tell you a claims examiner’s job is highly exposed to AI. They can’t tell you how your examiners spend a Tuesday.
• Interviews and workshops: People describe the process as it was designed, or as they remember it. The workarounds, the rework, and the gap between your best and newest people rarely come up in a conference room.

The failure data points the same way. Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs, or unclear business value (Gartner, 2024). RAND interviewed practitioners about why AI projects fail and put two root causes first: stakeholders misunderstand or miscommunicate what problem needs to be solved, and the organization lacks the data it needs (RAND).

And CAIOs are funding anyway. In the IBM study, 68% said they initiate AI projects even if they can’t measure their effects (IBM, 2025). That’s a reasonable choice for an experiment. It’s a risky one for a project whose business case is a headcount number.

Here is what the difference looks like in practice. Say a payments operations team of 80 asks for funding for an invoice-matching agent. The estimate says matching takes about half the team’s time.

A measured baseline shows matching takes much less, and that most of the hours go to chasing exceptions across email and two older systems. The agent still makes sense, but for the exceptions, and the funded scope changes before a dollar is spent.

How the Operations Leader and the AI Team Close the Gap

The gap closes when operations measures the work before the AI team ranks the use cases. Measuring first gives both sides the same numbers: the operations leader can defend them to the board, and the chief AI officer can fund against them and measure the result afterwards.

That is the work Summit Trails does. Our Patent Pending AI captures activity at the desktop (the region around each click, with no keystrokes and no full-screen recording) and classifies what people are doing at the activity level.

So the output reads “entering customer data into the account form” rather than “six hours in Salesforce.” Over a 90-day engagement it builds a baseline of the operation: time by activity, workflow maps, the spread between people, and an automation score for each activity. The first findings arrive at weeks three to four, and the final readout is held with the operations leader, the AI team, and the COO in the same room. See how the Summit AI platform works.

We call it the AI before the AI, and it is the position we take with AI teams. We sit ahead of the use cases on your list rather than among them. We measure the work so you can rank them, and so you can measure them once they’re live.

We don’t choose vendors, run implementations, or redesign the organization. The baseline is the input those decisions need. The category is called workforce intelligence, and our approach describes the engagement step by step.

Once the baseline exists, how to prioritize AI projects in operations covers the ranking itself, and the AI readiness assessment for operations covers the wider readiness picture.

Back to Dana. With a measured baseline, her second conversation with the AI team goes differently. She arrives with intake hours measured, the spread between her fastest and newest examiners, and the SLA work she won’t put at risk. The AI team funds a smaller first use case than the board’s number implied, and both sides know exactly what it will be measured against.

FAQ: The Chief AI Officer and Operations

Is chief AI officer a real role?

Yes. In an IBM Institute for Business Value survey published in 2025, 26% of organizations had a chief AI officer. In the US federal government the role is required: OMB memorandum M-25-21 directs every agency to retain or designate a CAIO.

Who does the chief AI officer report to?

It depends on how the company frames AI. Where AI is run as business strategy, the role tends to report to the CEO. Where it grows out of IT or data, it often sits under the CIO or CTO. For federal agencies, OMB requires the CAIO to be senior enough to engage regularly with agency leadership, including the deputy secretary or equivalent.

How is a chief AI officer different from a CTO?

A CTO owns the technology the company builds and runs. A chief AI officer owns how AI is used across every function: which use cases are funded, how they are governed, and what they return. In smaller organizations one person can hold both roles, and OMB lets federal agencies give the CAIO role to an existing CTO.

How do you measure AI success in operations?

Measure the work before the AI goes live, then measure it again after. That means hours by activity, cost, quality, and SLA performance, captured the same way both times. Without a before, a reported saving can’t be separated from work that simply moved to another team.

What data does a chief AI officer need from operations?

Five measured inputs: the hours the work takes today by activity, the volume behind those hours and how much they vary between people, where work crosses systems or is redone, a cost baseline to judge results against, and a written list of what must not break, such as SLAs and compliance deadlines.

Book a 30-Minute Strategy Call

If you’re a chief AI officer with operations use cases waiting for funding, or an operations leader about to send one, the conversation goes better with the same numbers on both sides of the table.

In 30 minutes we’ll look at where you are in your AI work, what your AI team is being asked to fund, and what a measured baseline would need to cover. You’ll leave with material you can take to your AI strategy team and your operations leaders. Book a 30-minute strategy call.