Asset managers are moving AI into the core of their risk management operations, with 73% expecting the pace of AI integration to accelerate within three years. The finding, drawn from Clearwater Analytics' "GenAI and the Data Divide" study, lands in the one function where caution runs highest - and still points to broad adoption.
The survey of 178 senior executives across Europe, the US, and Asia reveals an industry betting heavily on AI despite known data gaps. Nearly every firm surveyed - 93% - already treats AI agent integration as important or critical, and 95% say it matters for meeting investment goals over the next three years. Risk management, where a wrong signal carries direct financial consequence, is where that consensus faces its hardest test.
"Risk management is where you'd expect that consensus to be hardest to find," said Souvik Das, CTO at Clearwater Analytics. "That so many firms still expect AI's role there to grow reveals intentional direction by firms who've actually tested it and trust what they're seeing."
Spending follows conviction
The investment numbers back the rhetoric. More than four in five managers expect AI spending to rise by at least 50% over the next 12 months. A majority - 62% - anticipate increases between 50% and 99%, while 22% expect spending to jump between 100% and 299%. Fewer than 5% of firms expect budgets to stay flat or shrink.
For an industry that allocates capital with precision, the message is clear: AI is now core infrastructure, not experimental technology. The spending trajectory treats it as such.
The data gap that separates leaders
Beneath the spending figures sits a structural problem. While 79% of firms call their data complete, only 56% call it accurate. That 23-point gap is where risk signals either earn trust or fail. A slow report is forgivable. A risk signal built on bad data is not.
"Risk management is where a firm's data has nowhere to hide," Das said. "The firms closing that gap are the ones making sharper decisions, with better information than they've had before."
This finding shapes how management teams should read the research. The firms getting AI right are investing as heavily in data foundation as they are in the technology sitting on top of it. Those still catching up are funding the tools without fixing what feeds them.
The competitive proposition
Risk management plays a defining role in whether an asset manager can be trusted with client capital. Moving AI into that function signals that firms see it as a competitive differentiator, not a back-office utility. The research describes an industry placing AI at the center of how it competes for trust and mandates.
For professionals building expertise in this intersection, structured learning paths like AI for Finance courses address the specific demands of risk analysis and financial modeling that these findings highlight. The same applies to formal credentials - an AI Risk Management Certification maps directly to the capabilities firms are now funding at scale.
Why this matters for management
The study reveals a gap between ambition and readiness that falls squarely on management to close. A firm can approve a 50% AI budget increase and still get burned if the data feeding its risk models isn't accurate. The 23-point gap between data completeness and data accuracy is a management problem, not a technology problem. It requires decisions about data governance, team structure, and vendor selection - areas where technical teams need leadership direction, not just budget approval. The firms moving fastest are the ones where management treats data accuracy as a strategic priority with measurable targets, not an IT checklist item.
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