When it comes to adopting artificial intelligence, culture — not coding — is the real differentiator. Schroders’ fintech head, Charlotte Wood, recently revealed that the asset manager’s most dramatic AI turnaround didn’t stem from a new system or workflow overhaul, but from a change in leadership.
From Slow Uptake to Industry Leadership
At the Financial Times’ Future of Financial Intelligence conference, Wood shared that roughly 60% of Schroders’ staff now use AI weekly, yet adoption in its wealth management division had historically lagged behind. The shift began when Oliver Gregson, former JPMorgan Private Bank UK head, took over as CEO of the unit in June.
Gregson’s clear messaging — that AI wasn’t optional but a core enabler of performance — created a ripple effect. Within months, the division went from being “slightly below average” to leading the firm on AI usage.
“Having the right kind of motivation was more important than the structural nature of their work,” Wood noted, suggesting that managers often over-rationalize slow adoption instead of addressing leadership tone and incentives.
The Psychology Behind AI Hesitation
Interestingly, Schroders found that some employees resisted AI not out of fear of redundancy, but out of identity protection. Staff whose work involved writing or communication hesitated to use generative tools, worrying that it would erode their personal craftsmanship.
Wood emphasized that understanding these psychological barriers is essential for shaping adoption strategies: “It’s really important to understand why people aren’t engaging — only then can you adjust the company’s approach.”
Fidelity’s Parallel: Top-Down and Bottom-Up
At the same event, Prasad Chandrasheker, Global Head of Emerging Technology Strategy at Fidelity International, echoed a similar view. A top-down push was crucial in normalizing AI use — executives began sharing how AI was changing their daily routines, creating legitimacy for experimentation.
But Fidelity’s biggest catalysts were younger employees — the “reverse mentors” who demonstrated efficiency gains and taught senior colleagues how to integrate AI into daily tasks.
The firm even began with a “lo-fi” internal generative tool, designed simply to get people comfortable experimenting. Chandrasheker summarized it succinctly:
“We had to get over that psychological barrier. They had to try it themselves.”
Key Takeaway: AI Starts with the “Why”
Both Schroders and Fidelity illustrate a broader truth emerging across financial services: AI adoption is a leadership function, not a technical one. The most powerful enabler isn’t the model or infrastructure — it’s clarity of purpose, demonstrated use from the top, and empathy toward employee identity.
For firms still struggling to scale AI internally, the lesson is simple:
Don’t just deploy AI — champion it. Make it part of leadership narrative and daily practice. Address fears of skill erosion with visibility, support, and shared wins.
Because when leadership leads with conviction, even the most traditional teams begin to see AI not as a threat — but as a mirror for their own potential.
