In the first part of this piece, I looked at the gap between AI adoption and AI readiness in financial services — and why the constraint on returns is not technology, but people architecture. Here, I want to talk about what Chief People Officers can actually do about it, and why the commercial case makes this a board-level conversation.
Why the Chief People Officer holds the key
There is a phrase appearing in boardrooms and strategy documents: “people architecture.” I’m not entirely sure it will survive — it sounds like something an estate agent might say about a loft conversion — but the idea behind it is a good one. It means the deliberate design of how an organisation combines human capability with machine capability to create value.
The most effective approach is when this work happens as a genuine partnership across the leadership team — technology, finance, and people working together from the start, rather than in sequence. When the people dimension comes in early, you get investment with readiness. When it comes in late, you get productivity gains that plateau because the work itself was never redesigned.
This is where the Chief People Officer can add the most value: not by “supporting the transformation,” but by leading the part that determines whether the rest of it works. That requires a certain confidence — the willingness to hold your own in a room where the technology budget is ten times the people budget. In practice, it means three things.
First, redesigning work, not just retraining people. In the most advanced deployments, AI agents are taking over entire task chains, not individual tasks. If a credit analyst’s job has changed because AI now produces the first draft of a risk memo, you haven’t added a tool to their role. You’ve created an entirely new role that happens to share the same title. That’s worth a proper conversation, not just an updated job description.
Second, building the management capability to lead hybrid teams. Gartner projects that 20% of organisations will use AI to flatten their structures this year, eliminating over half of middle management positions. But Korn Ferry found that 41% of those in flattened organisations feel directionless. Strip out the managers without replacing the management functions — the coaching, the translation, the institutional memory — and you get an organisation that moves quickly but isn’t entirely sure where it’s going.
Third, making the cultural case for working with machines. Employees expect AI to handle 46% of their tasks within three years. Among organisations with advanced adoption, 95% of people report higher job satisfaction — a genuinely encouraging number. But where adoption has been poorly managed, AI becomes a source of anxiety and quiet resistance that no training can unstick. People will use something they trust. They will find workarounds for something they don’t.
The organisations that design for the variation in how people experience AI — across roles, seniority, and demographics — will build something that lasts.
The commercial case
What makes this worth having at board level is the scale of the returns. Organisations that deeply embed AI report investment returns roughly three times higher than slow adopters. In wealth management, that translates to 30–40% increases in net new assets. In retail banking, fraud detection accuracy exceeding 90%. In private banking, the gains are harder to quantify but arguably more defensible: deeper client relationships, faster onboarding, and advisers who can serve more clients without diluting the quality of attention.
These are competitive advantages that compound over time. And they accrue to organisations that have invested in the people dimension — not through a training programme alone, but through a genuine redesign of how human and machine capability combine.
£9bn+ Projected annual cost savings across global banking from AI-driven operations by end of 2026. The organisations capturing this are the ones that invested in people readiness alongside technology deployment. McKinsey / Accenture, 2025
It’s also worth being honest about the downside. When organisations get the people dimension wrong, the costs are not just productivity misses — they can be regulatory events. Financial regulators are increasingly interested in how firms govern AI deployment, including the human oversight of automated decisions. The people you invest in are not just your growth engine. They are your safety net.
The leadership teams that work this out together will move faster, retain better, and compound their advantage. They’ll also build institutions that the next generation of talent actually wants to join — which, in a sector competing for the same people as technology firms, may matter more than any of us fully appreciate yet. I find that a rather energising prospect. I hope you do too.
Domi Alzapiedi is a Chief People Officer in banking, focused on the intersection of people strategy, organisational design, and commercial performance. She writes about the questions that keep leadership teams honest.