The Eighty-Seven Percent Problem

Here is a number worth sitting with: 87% of organisations are not fully AI-ready. Not because they lack ambition or investment — but because they lack scalable infrastructure, clear strategy and governance, and a skilled, confident workforce. That is according to Cisco’s 2025 AI Readiness Index, which surveyed over 8,000 leaders across 30 markets.

Only 13% qualify as what Cisco calls “Pacesetters” — organisations that have genuinely embedded AI across their operations. The rest are running pilots, buying licences, and building dashboards that look wonderful. What’s missing is everything around the technology — the people architecture, the skills infrastructure, the cultural readiness that determines whether a clever tool becomes a genuine advantage or simply a very expensive way to do roughly the same things.

I find this gap fascinating, because it suggests something counterintuitive: the constraint on AI returns is not investment or ambition. It’s organisational readiness. The Pacesetters — that 13% — are 1.5 times more likely to report major gains in profitability, productivity, and innovation. For every pound invested in agentic AI, companies earn three pounds fifty back — but only among those who have embedded it deeply enough to matter. Which raises an interesting question about what the rest of us are actually paying for.

The readiness gap is a people gap

As an industry, we have become remarkably good at buying technology. We’re getting better at being honest about how far we still have to go in preparing our organisations to use it well — which is progress of a sort. Cisco’s research found that only 33% of organisations have a formal change management plan for AI, and only 15% have network infrastructure fully ready to support it. The technology is outpacing the organisation. There’s something almost reassuring about that candour.

In financial services, the challenge is particularly acute — and particularly consequential. The sector leads global AI adoption by market share, commanding nearly 20% of the global AI market and spending over twenty billion dollars annually. Yet the human infrastructure — the skills, the management capability, the cultural permission to work differently — has not kept pace. From what I hear across my network, this is true across retail banking, wealth management, and private banking alike, though each faces the gap in different ways.

11.5% Average net productivity increase reported by companies in AI-intensive sectors over the past twelve months, accompanied by a 4% net decline in headcount. The productivity is real. The question is whether it’s sustainable without investing in the people who remain. Morgan Stanley, 2025

That productivity gain is not evenly distributed, and where it shows up is instructive. A major bank that deployed AI agents for credit risk memos found productivity improvements of 20% to 60%. Wealth management firms using agentic AI are cutting manual prospecting time by nearly half — freeing relationship managers to do what they were hired for in the first place: build relationships. In private banking, where the client expects bespoke judgment and deep knowledge, the opportunity is arguably even greater: AI that handles the analytical groundwork so that the banker can spend their time on the nuance that no algorithm can replicate.

I spoke recently with a private banker in her mid-thirties who told me that since her firm introduced AI-assisted portfolio analysis, she’s been able to take on twelve additional client families — without feeling she’s giving any of them less attention. “I used to spend my mornings preparing for conversations,” she said. “Now I spend them having them.” That, in miniature, is what the opportunity looks like. But her firm had also spent six months redesigning her role before the technology arrived. Most hadn’t.

But here’s what those numbers also tell us: the organisations seeing these returns invested as much in rewiring their people and processes as they did in the technology itself. The returns are real, but they are not automatic.

The best AI strategies I’ve seen treat culture as a design constraint from day one — not something to be managed after the technology has landed.

That is the eighty-seven percent problem. Not a lack of AI adoption. A lack of AI readiness. And closing that gap — through redesigning work, rebuilding management capability, and reshaping culture — is, I’d argue, the most commercially significant people challenge in financial services today.

In the second part of this piece, I’ll look at what Chief People Officers can actually do about it — and why the commercial case for getting this right is stronger than it first appears. The opportunity for people leaders right now is genuinely exciting. We are not bystanders to this transformation. We are the ones best placed to connect the technology investment to the organisational capability that makes it pay.