Stop Hiring a Chief AI Officer to Do Your Job

AI cannot be delegated away. You can hire someone smart, give them a title with “AI” in it, and hand them a mandate. What you cannot outsource is the judgement about where AI belongs in your business, how much you spend on it, and how fast you expect it to move. That judgement sits with the CEO and the executive team. Push it down the org chart and it stops being direction. It becomes a side project.

This matters because AI is not a niche tool for one department. Economists treat it as a general-purpose technology, in the same category as electricity or the computer, with the reach to reshape how work gets done across an entire economy, according to NBER research.

One AI title can become a holding pattern

It offers false reassurance

Once a Chief AI Officer exists, everyone else gets to relax. The board ticks a governance box. Line managers assume the AI question is handled. The CEO points to an org chart when asked what the company is doing about it. None of that changes how work actually happens. A name on a slide is not the same as teams testing tools and shipping better processes with real team workspace controls.

It splits AI from the people responsible for outcomes

The person who owns customer support outcomes should own how AI changes customer support. The finance lead should own how AI changes reconciliation and reporting. Put a central AI owner between them and the tools and you create a gap. That owner does not carry the P&L for those functions, and the functions never feel ownership over the AI. Work falls into the gap and stalls.

It turns AI into a permanent pilot

A dedicated AI office tends to run experiments that never quite land in the business. There are demos, workshops, a roadmap. Meanwhile the operating teams keep doing things the old way, because the new way belongs to someone else. This is how AI turns into a side project that eats budget and produces activity without moving the numbers that matter.

JD Trask, who co-founded Raygun and Autohive, made this point bluntly in his keynote closing the Aotearoa AI Summit. He called the Chief AI Officer model “an anti-pattern.” His argument was that a single anointed owner is the wrong shape for something this general. That is Trask’s account and argument from the keynote, not independent evidence. Treat it as one experienced operator’s view, not a settled fact.

Leadership has to come from the top

If AI is a general business capability, the people who set business direction have to set AI direction too. That is not a metaphor. It is a list of concrete things only the executive team can do.

• Priorities. Which problems get attention first, and which can wait. Only leadership can rank them against everything else the business is trying to do.
• Investment. AI work needs funded time, not volunteer evenings. Someone has to decide the money is worth spending before the results exist to prove it.
• Pace. How fast is fast enough. A CEO who expects quarterly progress gets quarterly progress. A CEO who expects a demo “sometime” gets nothing.
• Curiosity. Executives who actually use the tools ask better questions and make better calls than executives who only read summaries about them.
• Permission. Teams will not experiment if they think a mistake ends a career. Leaders grant the cover for responsible testing and set the limits around it.

Trask described doing exactly this at Raygun. According to his keynote, leadership invested in staff, paused normal business for a week in May 2023 so people could build agents, and led the change directly instead of delegating it. He also said Autohive grew out of the internal systems Raygun built to run its own business, before it spun out as a product. The business impact behind it remains his own account, not an independently audited figure.

The line that sticks from that talk: “Motion is not progress.” Committees, steering groups, and endless governance sessions can look like momentum while nothing in the business actually changes.

What leaders should own

• The business problems. Name the specific outcomes you want: “cut the time to resolve a support ticket,” or “reduce month-end close by two days.” Not “explore AI.”
• Budget and resourcing. Fund the pilots and the people. Decide what other work pauses to make room.
• Standards for customer trust. Set the rules for where a human reviews AI output before it reaches a customer, and where AI can act on its own. These are trust decisions, and trust is an executive responsibility.
• Measurement. Define what a win looks like in dollars, hours, or error rates. Insist on measuring value, not activity.
• The expectation of learning. Make it clear you expect teams to try things, get some wrong, and improve. Say it out loud so people believe it.

Where a specialist still helps

None of this means you should have nobody focused on AI. An AI lead, a technical leader, or a small enablement team can be genuinely useful. They can run internal education, evaluate platforms, set security standards, and build shared patterns so every team is not solving the same problem from scratch.

The distinction is support versus substitution. A specialist who helps twelve teams move faster is an asset. A specialist who becomes the reason twelve teams stop thinking about AI themselves is the anti-pattern Trask warned about. The test is simple: does the role make business-wide ownership stronger, or does it let everyone else off the hook.

A leadership checklist

1. Named outcomes. Can you point to specific business results AI is meant to improve, each with a person accountable for them?
2. Executives using the tools. Are your leaders actually using and understanding the tools, or only being briefed about them?
3. Permission to test. Are teams genuinely allowed to trial new workflows without fear of blame for a reasonable failure?
4. Funded pilots. Is there real budget and protected time for pilots, rather than expecting them to happen off the side of a desk?
5. Measuring value. Are you measuring outcomes in money and time saved, rather than counting demos, workshops, and presentation activity?

Trask’s practical version of this is what he called 30-day ROI pilots: pick a real problem, fund it, run it for a month, and judge it on the results. It forces the measurement discipline that most AI programmes skip.

FAQ

Does this mean we should never appoint anyone to lead AI?

No. A specialist lead or enablement team can be genuinely useful. The problem is treating that role as the owner of AI outcomes across the business. Direction, investment, and accountability stay with the executive team and with the leaders who own each function.

We are a small business without a large team. Does this still apply?

Yes, and the smaller you are, the more directly it applies, because there is no layer to hide behind. The owner or managing director sets the priorities, funds the time, and decides what gets tested. The checklist above works the same whether you have five people or five hundred.

How do we start without a big transformation programme?

Pick one problem and go. A 30-day pilot on a single, real operational task teaches you more than a strategy document. Fund it, give it an owner, measure the result, then decide whether to expand.

The takeaway

AI is a general business capability, so it belongs to the people who run the business. A single title cannot carry it, and a permanent pilot cannot deliver it. The teams that win are the ones where leaders set direction, fund the work, use the tools themselves, and hold the standard for what good looks like.

Bring one operational AI opportunity to the next executive meeting. Assign an outcome owner, a budget, and a review date.