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Most global asset managers plan to raise AI budgets by at least 50 percent
Most asset managers plan to raise AI budgets by at least 50% next year. This exposes a data divide between firms with mature infrastructure and those using fragmented systems.
A majority of global asset management firms plan to increase their artificial intelligence budgets by at least 50% over the next year, according to a study released Tuesday by Clearwater Analytics, as the technology reshapes core investment operations and widens a data divide between firms.
The study, "GenAI and the Data Divide," surveyed 178 senior executives across hedge funds, private credit, and institutional asset managers located in Europe, the US and Asia.
The survey results point to an intensifying commitment to AI for Finance, with 62% of fund managers expecting major changes in data generation and summarisation. Another 58% said the technology would have a major effect on decision-support systems, including portfolio rebalancing, while 57% cited predictive modelling and stress-testing.
Data management fundamentals under pressure
"What's striking is that AI adoption is forcing fund managers to confront the fundamentals of data management in a way nothing else has," said Souvik Das, chief technology officer at Clearwater Analytics. The push is exposing a growing gap between firms with mature Data Analysis capabilities and those still relying on fragmented systems.
Automating alternative data and multi-agent workflows
Two-thirds of fund managers reported that their AI tools effectively managed alternative data, an area traditionally difficult to scale. Another 62% reported success with multi-agent orchestration, where systems trigger autonomous operations based on specific data thresholds.
Why this matters for finance professionals
For finance professionals, the findings signal that AI is moving from experimental projects to a central driver of competitive advantage. Firms that delay investments in data infrastructure and AI literacy risk falling behind as peers automate complex tasks and extract insights from alternative data. Building skills in data management and AI tooling is becoming a core competency, not an optional specialization.