Key Takeaways
- • Asset managers plan only modest increases in technology spending, rising from 12% to 14% of budgets over the next five years, despite widespread AI adoption rhetoric, according to research from the Thinking Ahead Institute and CAIA Association.
- • Labor costs for customer-facing employees are expected to remain flat over the same five-year period, suggesting AI implementations are not yet displacing workforce headcount at scale.
- • Most asset managers remain in an experimentation phase with AI, selectively deploying tools for research enhancement and data analysis rather than undertaking organizational restructuring, per Aaron Filbeck, managing director at CAIA.
- • Fragmented data infrastructure, incomplete technology systems, and underdeveloped governance frameworks remain material constraints limiting faster AI adoption among established asset managers.
- • Compliance and risk teams are still in early stages of learning how to incorporate AI practices into their workflows, indicating institutional adoption remains nascent across operational functions.
Summary
The asset management industry's AI spending growth is decelerating despite sustained market enthusiasm around artificial intelligence capabilities. Research from the Thinking Ahead Institute and CAIA Association reveals that asset managers expect technology expenditures to increase only modestly—from 12% to 14% of total budgets—over the next five years. This measured approach undercuts the aggressive investment narrative that has dominated industry commentary.
Aaron Filbeck, managing director at CAIA and co-author of the report, characterized most asset managers as operating in an experimentation phase rather than executing broad-based AI transformation initiatives. He emphasized that measured capital deployment reflects the current state of use-case development rather than skepticism about the technology itself. According to Filbeck, research teams are utilizing AI selectively to accelerate specific projects and expand analytical datasets, but these implementations remain narrowly focused rather than systemic.
A critical constraint on faster adoption is organizational infrastructure. Fragmented data ecosystems and incomplete technology platforms create significant friction for asset managers attempting to deploy AI at scale. Beyond technical limitations, governance frameworks for evaluating and approving AI initiatives remain under development at most institutions. Compliance and risk management functions are similarly early in their process of integrating AI tools into standard operational practices, suggesting institutional guardrails must mature before broader deployment occurs.
The disconnect between media coverage and capital allocation reflects the reality that back-office AI capabilities—frequently highlighted in headlines—remain relatively underdeveloped operationally. Labor costs for customer-facing roles are projected to remain unchanged, indicating that current AI implementations are not yet functioning as workforce optimization mechanisms. This suggests the industry remains several quarters away from leveraging AI for significant cost reduction or structural reorganization.
Aaron Filbeck, managing director at CAIA and co-author of the report, characterized most asset managers as operating in an experimentation phase rather than executing broad-based AI transformation initiatives. He emphasized that measured capital deployment reflects the current state of use-case development rather than skepticism about the technology itself. According to Filbeck, research teams are utilizing AI selectively to accelerate specific projects and expand analytical datasets, but these implementations remain narrowly focused rather than systemic.
A critical constraint on faster adoption is organizational infrastructure. Fragmented data ecosystems and incomplete technology platforms create significant friction for asset managers attempting to deploy AI at scale. Beyond technical limitations, governance frameworks for evaluating and approving AI initiatives remain under development at most institutions. Compliance and risk management functions are similarly early in their process of integrating AI tools into standard operational practices, suggesting institutional guardrails must mature before broader deployment occurs.
The disconnect between media coverage and capital allocation reflects the reality that back-office AI capabilities—frequently highlighted in headlines—remain relatively underdeveloped operationally. Labor costs for customer-facing roles are projected to remain unchanged, indicating that current AI implementations are not yet functioning as workforce optimization mechanisms. This suggests the industry remains several quarters away from leveraging AI for significant cost reduction or structural reorganization.
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