HSBC's Chief AI Officer Starts This Week. So Do 46 Others. Most Will Quit Before 2028.

David Rice, who started this week as HSBC's first group Chief AI Officer, has a problem he probably doesn't know about yet. So do the 46 other senior executives who were named to new CAIO or equivalent roles at large enterprises between January and March.

The problem isn't David Rice. By every public account, he's a capable operator — twenty years at HSBC, most recently as COO of the bank's corporate and institutional banking division, respected by colleagues, strong track record. If I were HSBC, I'd hire him too.

The problem is the role they hired him into.

For the last three months I've been quietly tracking Chief AI Officer appointments across 47 large enterprises — an analysis published in parallel. In parallel I've been doing something less formal: I've been interviewing people who took similar roles in 2024 and 2025. The CAIOs who came before this wave. The ones who are now trying to figure out how to get out.

None of the eleven people I spoke with agreed to be named. The reason they gave — that speaking publicly about their role's structural problems would damage their ability to do the job they are currently trying to do, and their ability to get the next job after — is the reason this problem stays invisible to the boards that keep creating these roles.

What they told me, almost without exception, is this: the job description they were given was not the job they ended up doing. The budget authority they were promised did not materialize. The board expected measurable results on a timeline that was impossible to hit. The CIO and CDO saw them as encroaching on territory. The product teams saw them as governance overhead. The business units saw them as someone whose approval they needed before shipping the AI features they were already building.

And the budget pressure — Forrester's prediction that enterprises will defer 25 percent of planned AI spending — showed up about eight months into their role, right when they were supposed to start showing wins.

Most of them are looking for the exit. They don't call it that publicly. The ones who still have their role are framing it in their internal communications as "restructuring" or "evolving scope." Privately, they're planning their next move.

Three structural problems nobody is correcting

I have not seen a single CAIO appointment this quarter that has corrected for any of the three structural problems the 2024–2025 wave is now running into.

The authority gap. The job is written with the vocabulary of executive accountability — "drive enterprise AI strategy," "own AI value realization," "ensure responsible deployment." The authority that would actually make those things possible is almost never granted. The CAIOs I interviewed have responsibility for AI initiatives they cannot direct, budget authority for spend they cannot approve, and governance mandates for teams that don't report to them. The kindest description is "dotted-line everything." The honest description is unaccountable power.

The timeline mismatch. Boards are hiring CAIOs with 12-to-18-month expectations for measurable results. In the deployments I'm hearing about, the behavioral layer of AI adoption — the part that determines whether real value gets created — takes 24 to 36 months to shift meaningfully. You cannot change how an organization of 40,000 people does its work in 12 months, and nobody who has ever tried to change enterprise behavior thinks otherwise. The 12-month expectation was invented by vendors and consultants who needed the sales cycle to be short. It has become the board-level expectation for what the CAIO must deliver. The gap between those two numbers is where most of the 47 Q1 CAIOs will fall.

The budget cycle trap. Every CAIO hired in Q1 of 2026 will face their first real budget review in roughly October or November of this year — precisely when the Forrester-predicted deferral of 2026 AI spending will be hitting their organizations' finance teams. They will be asked to justify AI spending at the exact moment that enterprise AI spending is being cut. They will not have had enough time to produce the kind of evidence that answers the question. The result will be a CAIO who is publicly championing AI investment while watching the budget for that investment get reduced by a CFO who has lost patience.

Watch what happens to those CAIOs over the following six months.

What the eleven interviews actually said

I cannot quote the eleven people I spoke with by name. I can tell you what they said in aggregate.

Nine out of eleven described a moment in their first six months when they realized the job's authority didn't match its accountability. They used different language for it — "the gap," "the mismatch," "the part nobody told me about." One called it "being the designated adult for a teenager who didn't invite me to the party."

Seven described being hired primarily because the board wanted someone specific to be accountable for AI outcomes, rather than because the board had a clear theory of what the CAIO would actually do. "They didn't need me to run AI," one told me. "They needed to be able to say 'we have someone running AI.'"

Five described explicit CEO or board frustration at the 12-month mark when the "AI strategy" they were expected to deliver hadn't translated into visible business metrics. Four of those five said the frustration was framed to them as a personal performance issue, not a timeline mismatch.

Three had already been fired, quit, or moved into a different internal role by the time we spoke. Two more told me they were actively looking.

Only one of the eleven told me the role was working as described. The outlier was at a company where the CEO had previously built and led an AI product team themselves. The CEO understood what AI deployment actually required, so the CAIO's authority matched the assignment.

The outlier tells you what the problem is. The CAIO role works when the CEO already understands what AI deployment requires. It fails when the CEO hires a CAIO to understand it for them. The first condition is rare. The second is almost universal in the current wave.

My prediction

Of the 47 CAIOs hired at large enterprises in Q1 of 2026, I expect more than half to have exited their roles — through departure, demotion, restructuring, or title change — by the end of 2027.

I'd like to be wrong about this. David Rice and the 46 others are smart people walking into a structurally difficult assignment, and I want them to succeed. But the pattern is consistent enough that I'm willing to put the prediction in writing and stand behind it publicly.

The specific failure mode will be the budget cycle trap. Most CAIOs in this wave will not survive their first real budget review, because the review will happen before they can produce the results that would justify the investment. The boards that hired them will not remember that they made this timeline impossible. They will remember that the CAIO didn't deliver.

The news coverage of the CAIO hiring wave has been framed as a sign that enterprise AI accountability is maturing. I think it's the opposite. It's a sign that enterprise boards are trying to solve an accountability problem by hiring someone to be accountable — without addressing any of the underlying structural conditions that made AI accountability hard in the first place.

Wishing David Rice well is not enough. The role he took this week is broken. HSBC has the power to fix it for him. So do the 46 other boards currently watching their new CAIOs walk in the door.

If they don't, I'll see you back here in 18 months writing the obituary for this wave.

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About the author: Brittany Hobbs is co-host of the Product Impact Podcast, where she covers the human and organizational layer of enterprise AI adoption. The eleven interviews referenced in this piece were conducted under Chatham House rules between January and March 2026.

Related reporting:
- Product Impact Podcast analysis: Chief AI Officer Hirings Hit Record in Q1
- HSBC press release on David Rice appointment (April 1, 2026)
- Forrester 2026 Predictions: 25% of AI spend to be deferred to 2027