Is the Chief AI Officer Role Here to Stay? After Placing Several, I Don't Think So.

Almost every CAIO brief I've taken has looked significantly different from the last. The market still doesn't seem to know what it wants from its most senior AI executive.

The CAIO is the newest C-Suite technology related role in the top-team, and its a hot topic. Not really a surprise, AI is changing the face of business (and everything else, for that matter). And much like AI, I'm finding that many hiring managers seem not to know exactly what they want this new executive to do. I thought it might be useful to share what I'm seeing in the market and the different versions of the CAIO I am being asked to find.

Boards across the FTSE 100, the Fortune 500 and large parts of corporate India have spent the past two years competing to appoint a new kind of executive. According to LinkedIn data, roughly 250 people held the title of Chief AI Officer in 2020. By early 2024 the figure had passed 780. IBM now estimates that around a quarter of organisations globally have made the appointment, more than double the proportion recorded two years earlier. In the UK, 48% of FTSE 100 companies have a CAIO or equivalent in place. In India, the figure is approaching saturation, with 83% of large enterprises reporting an appointment and most of the remainder planning one within the year. It is one of the fastest C-suite role expansions in recent corporate history. I have no idea, how many ASX companies have appointed a CAIO.

I have spent that same two-year period placing several of these executives, and the thing that strikes me most about the work is how little consistency there is in what the role actually involves. One client will brief me for a deeply technical leader, ideally a former head of applied machine learning with a doctorate and a track record of taking models into production. The next brief will be closer to the opposite: a former top tier strategy consultant whose value lies in board influence and operating-model design, and who has never trained a model in their life. A third, more awkwardly, will be for someone who can credibly play the part of a Chief AI Officer in front of investors and analysts, with relatively little specificity about what that person will be expected to do once they arrive.

The variation is not a quirk of a small number of unusual clients. It runs across the market, and it points to something more structural than the teething problems of a young role.

Vivek Mohindra (CAIO at Dell), when asked in a recent interview how he saw the role evolving, he described it as "finite by design": its purpose, he said, was to launch and integrate AI until the technology becomes inseparable from how the company operates, at which point a dedicated executive seat is no longer required. Few people in any other C-suite role describe their own job in these terms. That he did suggests something important about the structure of the role itself.

This isn't the first role to emerge this fast

When a new technology executive role expands this quickly, the obvious comparison, for me, is the Chief Digital Officer. The CDO emerged in the early 2010s as boards scrambled to respond to cloud computing. The title spread rapidly, peaked in the middle of the decade and then began to fragment. Many CDO roles were absorbed into the office of the Chief Information Officer or the Chief Product Officer. A number were quietly dissolved once digital transformation had become business as usual.

The Chief Digital Officer is the most commonly cited precedent, but it is far from the only one. The Chief Y2K Officer of the late 1990s was a senior, well-funded role that ceased to exist within months of the millennium. The Chief Knowledge Officer, ubiquitous in large companies at the turn of the century, was largely absorbed back into HR, learning and what eventually became the data function within a decade. The Chief Mobile Officer, appointed by companies including PepsiCo, Citi and Visa in the early 2010s, barely survived five years before mobile became simply how every business operated. The Chief Web Officer of the late 1990s lasted less time still. The Chief Quality Officer, once a fixture of large industrial firms during the Total Quality Management era, was reorganised out of existence as quality methodologies became embedded in operations. In each case the work done under the titles was real and necessary; the dedicated executive seat was not.

A recent paper on the topic, asked the question whether the Chief AI Officer is the new Chief Digital Officer. Its conclusion is that there is good reason to expect a similar outcome for the CAIO. Tim Crawford of the research firm AVOA has drawn the comparison more directly, noting that many early CDO appointments struggled because the executives brought in from outside were not closely connected to the business. Randy Bean, who authors the annual AI and Data Leadership Executive Benchmark Survey, frames the question as the central open issue in the field: whether the CAIO is a transitional role that will eventually be folded back into existing portfolios, or a durable addition to the C-suite.

Having experienced much of this myself, my own view is that the answer depends on which kind of CAIO is being discussed. There is no single role to evaluate, and the various forecasts of permanence or obsolescence tend to suffer from that conflation.

Five archetypes inside a single title

The five archetypes set out below are an attempt to describe what I have observed, cross-referenced where possible against categorisations published by executive search firms and consultancies. The most-cited frameworks are pltfrm's "Savant versus Shepherd" binary, drawn from its FTSE 100 study, and Christian and Timbers' four-way split into Corporate Visionary, Technical Program Builder, Innovation Catalyst and Commercial Strategist.

Archetype	Core mandate	Typical background	Shelf life	The tell
The Builder	Ship the AI capability: infrastructure, models, MLOps, applied teams	Data science or ML engineering leader, often promoted from VP Data or Head of Applied ML; production experience essential, doctorate optional	Survives, though the title is likely to be reabsorbed into the CTO function within five to seven years	Can articulate latency, cost and quality trade-offs in production; has killed a model that was not working
The Transformer	Reimagine the operating model so that AI becomes how the business works, rather than something the business uses	Strategy consulting (McKinsey, BCG, Bain), large-programme transformation, sometimes an ex-CDO	Finite by design; the Mohindra archetype, dissolving into COO or CEO once AI is embedded	Talks in capability maturity, change programmes and operating models; can name three use cases with measurable P&L impact
The Commercial Strategist	Monetise AI by turning it into product features, new revenue lines and defensible moats	Product leadership or commercial/GTM, often an ex-CPO; common in companies where AI is the product	Temporary; folds back into Chief Product Officer or Chief Revenue Officer once AI products mature	Frames AI in ARR, win rates and customer adoption rather than model capabilities
The Shepherd	Governance, risk, regulatory compliance, ethics and auditability; keeping AI from becoming a liability	Data governance, risk, legal or compliance, sometimes an ex-CISO or a governance-leaning CDO	Survives and grows; the EU AI Act, sectoral regulators and board audit committees have created a permanent workload	Familiar with the operative provisions of the EU AI Act; maintains a working risk register

The two archetypes I would expect to survive in recognisable form by the end of the decade are the Builder and the Shepherd. Though, they survive for different reasons. The Builder survives because the technical work is genuinely permanent and benefits from senior ownership, even as the title itself migrates back into the technology function. The Shepherd survives because the regulatory burden around AI is intensifying rather than diminishing, and the resulting compliance workload now requires permanent executive attention. The closest contemporary parallel is the Chief Sustainability Officer, a role that looked around 2010 as if it might follow the Chief Digital Officer's arc, but which has been entrenched rather than dissolved by ESG reporting requirements, regulatory pressure and investor expectations. Once regulation becomes the dominant driver, the role tends to stay.

The two archetypes I would expect to fade into history — the Transformer and the Commercial Strategist — share a structural feature: their mandates have a defined endpoint. The Transformer's task is to complete the transformation. The Commercial Strategist's task is to bring AI products to market. Each is doing real work today, but neither is doing work that obviously requires a dedicated C-suite seat once the work matures.

A rough estimate, offered as a working judgement rather than a research finding: between half and two-thirds of current CAIO appointments fall into the two temporary categories. If that estimate is even broadly correct, a meaningful majority of those currently holding the title are doing work that will eventually conclude, be absorbed, or be exposed.

Should you promote from within?

Another detail from the recent data is worth considering. Altrata's 2024 executive intelligence report found that most CAIOs are appointed internally, and that the majority lack senior-level experience in functions outside technology. IBM's research points in the same direction: nearly three quarters of CAIOs come from data backgrounds, and most were promoted from existing roles within the company.

That finding cuts two ways. Internal promotion of a strong head of data or applied machine learning is often the right move. The candidate knows the business, understands what is genuinely in production and has the internal relationships needed to drive change. These are credible Builders, and the appointment is well founded.

The same path of least resistance, however, is also the easiest route to a rebranded version of an adjacent role. The Head of Digital becomes the Chief AI Officer because someone has to be, and they happen to be the closest match on the organisational chart. The board gets a credible announcement. The executive gets a better title. Nobody is required to undertake an external search. The mandate ends up vague because the appointment was political rather than structural.

What is interesting about this pattern is that it represents almost the opposite failure mode to the one that undermined many CDOs a decade ago. The CDO failure was typically an expensive external hire who did not understand the business. The CAIO failure is more often an internal incumbent who does not understand AI deeply enough. The failures look very different from the outside but tend to produce the same outcome: a role that does not deliver against expectations and is folded into another function within a few years (or months).

Implications for boards making the appointment

For boards or chief executives weighing up the decision, the practical question is no longer whether to appoint a CAIO. The growth data suggests most large organisations will make the appointment regardless. The more useful question is how to avoid making it badly, and three patterns have become clear from the briefs I have seen succeed and the ones I have watched fail.

The first is that this role almost always needs to be an external hire. The exception, and it is a narrow one, is the candidate who is already operating as a de facto Chief AI Officer inside the business and who commands genuine respect at every level — especially from the engineers who would have to follow their technical judgement to the board members who would have to back their strategic calls. Those people exist, but they are very rare, and in most organisations the person who looks like the obvious internal candidate is not actually that person. They are the closest available match on the organisational chart, which is a very different thing.

Promoting the closest available match tends to produce a particular kind of failure. The appointment lands flat. The technologists in the business, who can usually tell within a single meeting whether the new CAIO genuinely understands the work, disengage or openly revolt - there is at the very least, a lot of eye-rolling. The board, having made the announcement, assumes the problem is solved and stops paying attention. The "Ticked that response". The new CAIO finds themselves with a title but without the authority that should have come with it, because authority in this role is earned through credibility rather than conferred through reporting lines. The right test for whether to promote internally is simple: when the appointment is announced, will the reaction across the business be a mixture of excitement and nervous anticipation about what this person will bring? If the honest answer is that people will mostly shrug, the appointment is not strong enough to do the work the role requires. AI is going to sit at the centre of how businesses operate for the foreseeable future. The person leading that work needs to arrive with either an existing reputation that commands respect, or the outsider's licence to ask uncomfortable questions and reset assumptions. Internal promotions that have neither produce the appearance of action without the substance of it. Remember, 40% of leaders fail regardless of whether they are externally hired or promoted internally. I am yet to see a failure rate for CAIO's, but there is anecdotal evidence to suggest a significant number quit within 6-8 months of starting.

The third pattern is the failure that most concerns me, because it is the one I see most often in people who have all the surface markers of credibility. Too many senior leaders in this space arrive locked into an action imperative. They have an answer before they have understood the question. They repeat exactly what worked at their previous company, set expectations the business cannot meet, attempt too much in the first six months, and overstate what current AI capabilities can actually deliver. They underinvest in the cultural work, building literacy, managing fear among employees who reasonably suspect their jobs are being redesigned around them, because culture is slower and less visible than launching a flagship initiative. The worst version of this archetype, and it is unfortunately common, is the executive who arrives as a self-styled white knight, parachuted in to slay the dragon, with a "get on the bandwagon or get out of the way" disposition that bulldozes the very colleagues whose cooperation will determine whether anything actually works.

A good Chief AI Officer is, in temperament, almost the opposite. They are curious about the specific business before they are prescriptive about the solution. They spend their first months listening, mapping where AI can genuinely add value and where it cannot, and identifying the cultural and operational obstacles that will determine whether implementation succeeds. They are comfortable saying that a particular initiative is not yet ready, or that a particular use case has been oversold. They build coalitions rather than enemies. The hiring process should test for this disposition directly, because the candidates who lack it can be very persuasive in interviews and very damaging in post.

A note for all you aspiring Chief AI Officers

For executives who want to move into this seat, the practical question is which capabilities to invest in and which to retire. The market is moving quickly enough that the answer is meaningfully different from what it was even eighteen months ago.

The capabilities worth doubling down on cluster around four areas. The first is genuine technical fluency, which means more than the ability to explain large language models at a dinner party. It means understanding how AI systems behave in production, where they fail, what they cost to run at scale and how to reason about model quality in commercial terms. Candidates who can hold a substantive conversation with a senior engineer about a specific implementation choice are increasingly rare and disproportionately valuable. The second is the discipline of building a defensible business case. The era of pilot projects funded out of innovation budgets is ending; boards now want measurable returns, and the executives who can connect AI investment to specific commercial outcomes will be the ones still in the role in three years. The third is governance and risk literacy, which has shifted from a peripheral concern to a board-level priority almost everywhere. Familiarity with the EU AI Act, the NIST AI Risk Management Framework and the relevant sectoral regulators is no longer optional. The fourth, and most underdeveloped in most candidates, is the change-leadership work: building AI literacy across the workforce, managing the legitimate anxieties of employees whose jobs are being redesigned, and creating the cultural conditions in which adoption can actually take hold.

The capabilities that belong in the old-world filing cabinet are pretty clear. The credibility that came from having led a large data warehouse project a decade ago no longer exists. The view that AI is essentially a more sophisticated version of analytics is increasingly held only by people who have not been close to the work. The instinct to lead with frameworks, maturity models and reference architectures rather than with specific use cases and measurable outcomes is now actively counter-productive in board conversations. So is the inclination to defer all difficult judgements about model behaviour to a technical team, on the grounds that it is not the CAIO's job to understand them. It absolutely is.

The most important shift, and the hardest to describe, is in posture. The executives who will hold these seats well over the next five years are the ones who are genuinely curious about the businesses they enter, who treat their first months as a period of inquiry rather than performance, and who resist the strong temptation to arrive with an answer. The candidates who lead with their credentials, with the things they did at their last company, or with a clear view of what this company should do before they have understood it, will not last. The market has not yet fully learned to identify the difference between the two postures, but it is learning quickly, and the executives who internalise the second posture early will be advantaged for the rest of the decade.

What survives, and what doesn't

The Chief AI Officer role will not disappear. Like the Chief Digital Officer before it, it will fragment. The Builder and the Shepherd will survive in evolved form, probably under different titles and located within different functions. Most of the remaining work will fold back into the C-suite that already exists: the Chief Technology Officer, the Chief Operating Officer, the General Counsel and, increasingly, the Chief Financial Officer, who is quietly becoming the de facto owner of AI return on investment in many large organisations.

We're not yet at the high-water mark of this role, but we're getting close. Lets be honest, transitional roles like the CAIO are useful and in many cases fulfill an essential function. The Chief Y2K Officer of 1998 was not a fraud; the work needed doing, and then it was done. The Chief Digital Officer of 2014 was, for many companies, exactly the right appointment for exactly that moment. The Chief AI Officer of 2025 is most likely the same kind of role: necessary now, transitional by design, and likely to be remembered as a marker of the moment rather than a permanent feature of the C-suite.

The question worth asking, for anyone currently holding the title, is which of the four types of CAIO are they, and what does the next move look like once the mandate is complete. That is not a conversation I am having but its is a useful conversation to have.