Why Getting Asset Management Employees On Board with AI Requires More Than a Rollout Plan
Pat Conroy, Vice President, STP Investment Services | Featured in Ignites | April 6, 2026
Investment firms are spending billions on AI and technology upgrades — but the gap between deployment and adoption is where most transformations quietly fail. Ignites spoke with Pat Conroy, Vice President at STP Investment Services, about why employee resistance to AI tools is often rational rather than obstructionist, and what firms need to do differently to drive genuine adoption.
The Buy-In Problem in Asset Management Technology
Across the industry, firms are racing to deploy AI and next-generation platforms. Franklin Templeton, BNY, and SS&C have each made significant technology investments in recent years. But technology deployment and technology adoption are not the same thing — and the gap between the two is where value gets lost.
As industry consultants told Ignites, employee engagement during rollout is what separates technology investments that deliver results from those that sit underutilized. Without it, even well-designed AI solutions become systems that employees work around rather than with.
What Pat Conroy Said
Pat framed employee resistance to AI not as a change management failure, but as a governance gap — a distinction that matters for how firms diagnose and respond to adoption problems:
“Employees who do not understand how an AI system makes decisions, or who bears accountability when it is wrong, are not being obstructionist. They are being prudent. Firms that address this directly, by building human oversight into the workflow rather than layering AI on top of existing processes, consistently see higher adoption and fewer quality failures downstream.”
The implication is direct: resistance to AI isn’t primarily a communication or training problem. It’s a design problem. Employees who don’t know who is accountable when an AI system produces a bad output have a legitimate reason to be skeptical — and firms that treat that skepticism as a communications challenge will keep running into the same wall.
What This Means for Investment Operations Teams
For COOs and heads of operations deploying AI in investment workflows, Pat’s framing points to three practical design questions that should come before any rollout:
- Who owns the output? When an AI system flags an exception, generates a report, or makes a recommendation — who is accountable for reviewing and acting on it? If that isn’t defined before deployment, employees are right to be cautious.
- Is human oversight built in, or bolted on? There’s a difference between AI that augments a human-led workflow and AI layered on top of a process that was never redesigned to accommodate it. The latter creates the accountability gaps Pat describes.
- What happens when it’s wrong? Employees who don’t have a clear escalation path when an AI system produces an unexpected output have no good option. Defining that path before launch — not after the first failure — is a prerequisite for genuine adoption.
About Pat Conroy
Pat Conroy is a Vice President at STP Investment Services, where he works with investment managers on investment operations strategy, technology transitions, and operating model design. STP provides investment operations outsourcing, fund administration, compliance, and managed services to registered investment advisers and institutional asset managers.
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