BOISE, Idaho, NEW YORK, CHICAGO, LONDON and HONG KONG, July 22 — A striking investment paradox is emerging at the heart of the asset management industry’s AI revolution, new research from Clearwater Analytics reveals.
While AI budgets across the fund management industry are growing at an extraordinary pace — with 63% of firms increasing AI spending by more than 50% in the past 12 months and not a single firm reporting a budget decrease — the industry is deeply divided about whether it is spending the right amount.
One in four (25%) fund managers believe their organisation is still not investing enough in AI. Yet a striking 66% say they fear their firms are already over-investing. Together, these findings expose a fault line running through the industry: for all the conviction that AI is essential, there is no consensus on what the right level of commitment looks like in practice — and for an industry where capital allocation decisions carry significant weight, that lack of consensus carries real risk.
A Maturing Industry, Not a Late One
Contrary to the narrative of an industry playing catch-up, the research reveals that AI adoption in asset management is more mature than commonly assumed. The majority of fund managers (56%) began integrating AI four to five years ago, and a further 34% started their journey two to three years back. Only 9% have begun AI integration within the past year.
This means the industry is not at the beginning of its AI journey — it is in the middle of it. The challenge is no longer whether to adopt AI, but how to scale it effectively, govern it responsibly, and calibrate the right level of investment to deliver measurable returns.
The Spending Surge Is Extraordinary and Showing No Signs of Slowing
Whatever the debate about whether investment levels are right, the direction of travel is unambiguous:
· 13% of fund managers report AI investment increased by over 100% in the past 12 months
· 50% saw expenditure rise between 50% and 99%
· Not a single firm reported a decrease in AI spending
· Only 4% held budgets steady
This is an industry-wide acceleration, driven by competitive pressure, client demand, and the growing recognition that AI is a core operational imperative.
AI Is Already at the Operational Core
The research makes clear that AI has moved well beyond pilot programmes and proof-of-concept projects. It is already embedded in the engine room of fund management operations, in investment decisions, risk management, and day-to-day workflows:
· Investment Decisions: 43% of managers now use AI for 25–49% of their investment decision-making; 10% rely on it for the majority of their investment calls
· Risk Management: 38% apply AI to 25–49% of their risk processes; 8% use it for the majority of risk assessments
· Operations: 34% integrate AI into 25–49% of operational decisions; 6% use AI for over half of their operational workflows
The message is clear: AI is already shaping how fund managers make decisions, manage risk, and run their operations today.
The Real Challenge: From Spending to Institutionalisation
The paradox the research reveals points to a deeper challenge. The industry has moved past the question of whether to invest in AI. The new question is how to institutionalise it effectively: building the data infrastructure, governance frameworks, talent capabilities, and operational processes that allow AI investment to translate into measurable outcomes.
Firms that solve this challenge — moving beyond the anxiety of calibrating spend to the discipline of deploying AI systematically across their investment lifecycle — will be the ones that pull ahead.
Souvik Das, CTO at Clearwater Analytics, said: “What our research reveals is an industry wrestling with how to get AI right. Increasing the budget is the easy part. The harder challenge is institutionalising AI in a way that drives genuine alpha and operational excellence, rather than simply adding cost and complexity.”
“At Clearwater, we are helping clients navigate exactly this challenge, by embedding AI directly into our platform and automating the data reconciliation and investment accounting lifecycles that form the foundation of everything else,” continued Das.
“When the infrastructure is right, AI doesn’t just work, it compounds. It identifies data anomalies in real-time, reduces the manual burden on risk and operations teams, and frees people to focus on the high-value strategic work that actually moves the needle.”
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