When you are working on anything related to AI, one of the challenges is how fast the ground moves beneath your feet. Everything about AI is happening at an unprecedented pace. I faced the same challenge as I started working on the book. But it wasn’t as much with the content, the thesis analyzes the implications of abundant AI rather than AI itself as a capability. It was more about identifying the profile of my target readers who would be interested in the book’s arguments.
That turned out to be hard because that profile does not have a fixed job title. And what is that profile? The person who is accountable for making abundant AI capability generate value for the enterprise. At some companies, that is the chief AI officer and at others it could be the chief data officer. Elsewhere it could be head of AI governance, a VP of AI, or the CIO who has quietly absorbed the mandate. While the work is real and specific, the label itself is still forming.
To be clear, my audience is not limited to only those people who have this formal accountability. It is wider than people who already hold the job. People who are thinking about extracting tangible value from AI but have not been given the accountability for it are also target readers for me. In addition, many companies have no single person for this work at all and accountability might be split across functions, sit with the CEO by default, or sit nowhere yet. That is not evidence against the work but one of the reasons why I wrote the book. This essential work of keeping an enterprise coherent as it deploys intelligence does not wait for a job title and just goes undone without named people accountable for it.
So it caught my attention last week when Bloomberg reported that business schools are racing to train people to become chief AI officers, even when companies are trying to figure out what exactly the role will do. And the pay has arrived ahead of the job description too. Prior reporting from Bloomberg put the salary at banks near $3.5M a year, high enough that firms are poaching talent from one another. And here is the best part – some people already in that position think it will not exist for long.
So, a role with no precise mandate, no settled title, seven-figure salaries, with fierce competition among companies for talent, and insiders who expect it to vanish. This is not how a market treats a job it understands, it is a function for which the market feels the need but hasn’t been able to define. I had to name the work to write the book.
Strategy vs execution
According to a BCG survey of 2,360 executives from earlier this year, roughly three-quarters of CEOs said they were their company’s main decision-makers on AI. That is double the share from a year earlier. AI strategy has moved to the top, which is where it belongs.
But owning the strategy is not the same as owning its execution. Deciding to deploy AI is one thing but making the organization able to absorb what the decision sets in motion is entirely different. The gap between them is where clarity tends to blur in companies.
On JPMorgan’s earnings call, Jamie Dimon described almost a thousand use-cases across the bank, with the firm’s platform rolled out to more than 200,000 employees. That is what owning the strategy looks like. But what it doesn’t say is whether those thousand systems work well together. Who owns the delivery of coherence across those thousand systems?
Dimon also said something sharper – that his bank does not uniquely benefit from AI because everyone is now using it. One of the most quoted CEOs in banking conceded that models are no longer the advantage. What he didn’t say is what replaces it instead. That is the question the missing role is supposed to answer.
The disappearing act
Now to the strangest part – some of the people best positioned to know what the job entails say it will not last.
Ranil Boteju is the first chief AI officer at the Commonwealth Bank of Australia. He expects AI to become invisible within about a decade, just the way electricity is, and the chief AI officer to shrink to a “very small role.” David Hardoon, who was the global head of AI enablement at Standard Chartered, said any chief AI officer should operate on the premise that they should not have a role in the future, asking if any company today has a chief Excel officer.
If AI capability becomes truly ambient, a dedicated role for it is as odd as a chief electricity officer. The prediction does represent a real pattern and the argument is correct on its own terms. But it proves my premise. The reasoning is that specialized titles fade away as new technologies become part of daily infrastructure. That is the commoditization, and the same argument of Nicholas Carr’s ‘IT Doesn’t Matter’ playing out faster. What felt like a moat becomes a utility everyone has.
Where the argument breaks is the analogy. Electricity does not build more electricity when you start using it. But agents do. Every AI deployment adds new systems, dependencies, handoffs, verification demands etc. and the burden of keeping them coherent grows as the capability becomes cheaper. The chief Excel officer joke works because a spreadsheet has no autonomy or agency. Electricity does not act on its own either. But agents act, connect to other agents, and take on scope until outputs feed decisions no one traced. The capability becomes invisible but what accumulates is incoherence.
So the skeptics are right about the title but wrong about the function. The label “chief AI officer” may very well disappear if the work is “manage the AI.” But the real work isn’t that, it is keeping the enterprise coherent while abundant intelligence becomes ambient.
Forrester predicts that 60% of the Fortune 100 will appoint a designated head of AI governance in 2026, with several companies already there. But I will concede that the function may not live in a named chief at all. It may fold into an existing role such as the CDO, COO or CIO. My claim is not that a particular title survives but that the function is real.
The structure varies
While the banks in the Business Insider survey did assign the work somewhere, there is no agreement on where the accountability should sit. The article reads as a set of incompatible bets on who should own coherence.
Wells Fargo runs a hub-and-spoke model with a small central AI team and leads embedded in each business as spokes. Citi took a bottom-up approach training four thousand employees as AI stewards. And JPMorgan restructured its firmwide data and analytics office and reshuffled its leadership after its AI chief retired. One centralizes ownership, another distributes it across thousands of employees, and the third seems to be mid-reorganization still deciding.
These are not variations of an answer but opposite theories and represent an industry trying to figure out what works. I want to be clear that nothing in these articles show any of the banks are incoherent. Nothing shows they are failing to build coherence. Several may be doing the exact right work. The point is that what these firms choose to measure and publicize – the usage rates, the productivity gains, the deployment counts – has little to say about whether the organization as a whole holds together. What the survey shows is silence in the evidence, and no consensus on what the accountable structure even is.
Two sides of the same coin
There is a missing metric. In six of the most sophisticated banks in the country, every figure reported measures capability, usage, or task-level productivity. None measures if the systems, taken together, serve the enterprise.
And there is a missing owner. A role with no agreed description, and no agreement on whether it will exist.
Both are the same problem. Coherence goes unmeasured because it is unowned. And it stays unowned because the role whose job it is to measure it has not been defined.
I wrote about the measurement side in a companion piece, on how the big advisory firms are circling around coherence, and why structure beats supervision. That post is about what a company has to build. This one is about who has to hold it once it is built.
And the answer to the question is a person. Building an enterprise’s ability to see what its systems are doing together, to know whether they still serve the overall business, and to correct a system that has drifted before the drift spreads – that capacity is what lets a company deploy hard and fast without coming apart. My book describes that work as coherence architecture, and the person doing it as a coherence architect. I do not offer that as a title the market will settle on. The market has not settled on one and the profession is still learning its own name. What I offer in the book is a description of the work so companies can recognize what the job entails before deciding what to call the person doing it.
Companies that recognize it and find that person early are the ones that will still make sense while everyone else is counting use cases. That is the argument of my book, Coherence, arriving this Fall. If you want to follow the thinking as it develops, join the list at coherise.com. The one-page decision tool from the book is the first thing I send.