Key Takeaways
- • Data quality, not spending volume, is the primary determinant of AI success in asset management: 44% of firms with good or excellent data accuracy report much more proactive risk management, versus only 17% of firms with moderate or poor data quality.
- • A significant trust gap exists in asset manager data infrastructure—79% of surveyed executives claim their data is complete, but only 56% rate it as accurate, indicating substantial discrepancies in data governance and validation practices.
- • Nearly universal AI budget expansion is underway: 95% of asset managers increased AI budgets in the past 12 months, with 85% planning increases of at least 50% in the coming year and 23% expecting budgets to double.
- • AI adoption is moving into core investment processes, with 95% of respondents believing AI will be important for meeting investment management goals and 3% considering it absolutely critical to organizational success.
- • Clearwater Analytics' survey reveals that operational AI benefits accrue to firms prioritizing data integrity over raw budget allocation, positioning data infrastructure modernization as the critical competitive differentiator for the next phase of AI deployment.
Summary
Asset managers are substantially increasing capital allocation to artificial intelligence, yet a Clearwater Analytics survey reveals that budget expansion alone does not guarantee competitive advantage or operational improvement. The research, which surveyed executives across the asset management industry, demonstrates that data quality has emerged as the primary determinant of whether firms realize meaningful returns on their AI investments in risk management, reporting, and analytical capabilities.
The distinction in outcomes is material. Among firms that rate their data as good or excellent, 44% report their risk management has become significantly more proactive. This contrasts sharply with the 17% of firms possessing moderate or poor data quality that report comparable improvements—a gap that underscores how foundational data infrastructure directly influences the efficacy of AI implementations. According to Clearwater's analysis, "the firms generating the strongest operational outcomes are not necessarily the ones spending the most on AI, but the ones building on data they can trust."
A critical vulnerability persists in asset manager data governance: 79% of surveyed executives classify their data as complete, yet only 56% characterize it as accurate. This 23-percentage-point divergence signals systemic gaps between perceived and actual data quality. Sandeep Sahai, CEO of Clearwater Analytics, emphasized the strategic implications, noting that "budgets are up this year, but what is holding firms back is the quality of their data. Closing that gap is the real work of this next phase of AI."
Budget trajectories reflect confidence in AI's strategic importance despite execution challenges. Nearly all respondents (95%) increased AI budgets within the past 12 months, with 85% planning increases of at least 50% in the year ahead. Twenty-three percent expect budgets to at least double. Concurrently, 95% of surveyed executives consider AI important for achieving investment management objectives, while 3% deem it absolutely critical to organizational success. This consensus on AI's centrality, combined with the data quality gap, positions data infrastructure modernization—not incremental budget increases—as the primary competitive battleground for asset managers seeking to translate AI investments into operational advantage.
The distinction in outcomes is material. Among firms that rate their data as good or excellent, 44% report their risk management has become significantly more proactive. This contrasts sharply with the 17% of firms possessing moderate or poor data quality that report comparable improvements—a gap that underscores how foundational data infrastructure directly influences the efficacy of AI implementations. According to Clearwater's analysis, "the firms generating the strongest operational outcomes are not necessarily the ones spending the most on AI, but the ones building on data they can trust."
A critical vulnerability persists in asset manager data governance: 79% of surveyed executives classify their data as complete, yet only 56% characterize it as accurate. This 23-percentage-point divergence signals systemic gaps between perceived and actual data quality. Sandeep Sahai, CEO of Clearwater Analytics, emphasized the strategic implications, noting that "budgets are up this year, but what is holding firms back is the quality of their data. Closing that gap is the real work of this next phase of AI."
Budget trajectories reflect confidence in AI's strategic importance despite execution challenges. Nearly all respondents (95%) increased AI budgets within the past 12 months, with 85% planning increases of at least 50% in the year ahead. Twenty-three percent expect budgets to at least double. Concurrently, 95% of surveyed executives consider AI important for achieving investment management objectives, while 3% deem it absolutely critical to organizational success. This consensus on AI's centrality, combined with the data quality gap, positions data infrastructure modernization—not incremental budget increases—as the primary competitive battleground for asset managers seeking to translate AI investments into operational advantage.
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