A global study of 178 senior asset management executives published today by Clearwater Analytics reveals a decisive shift in the asset management industry: a majority of managers expect artificial intelligence to transform their front-, middle-, and back-office operational functions over the next 12 months. The study, titled “GenAI and the Data Divide,” indicates that 62% of managers expect AI to transform how firms generate and synthesize information, 58% expect a radical change in decision-support systems and portfolio recommendations, and 57% anticipate a transformative impact on predictive modeling and stress testing.
Adoption is accelerating in tandem: 95% of firms increased their budget allocated to AI last year, and 85% plan to increase it by at least an additional 50% over the next 12 months. However, the analysis also identifies the key factor in capturing this transformation: a 23 percentage point gap exists between how entities value the completeness of their data versus its accuracy. While 79% consider their data to be complete, only 56% believe it to be accurate. This divide is emerging as the primary differentiator between firms realizing measurable returns on their information and those still waiting to monetize it.
A Year of Operational Transformation
The report reflects significant consensus among investment professionals regarding where AI will deliver the deepest impact over the next 12 months. Leading the list is content automation and data synthesis: 62% of managers are confident that AI will facilitate a profound or transformative shift in generating standardized reports and synthesizing complex data into concise summaries.
The industry’s reliance on predictive modeling will also undergo an AI-driven overhaul. Fifty-seven percent of respondents anticipate a transformative impact on how their firms analyze historical and current data to forecast results and evaluate stress-testing scenarios. Close behind, 58% expect AI to revolutionize decision-support systems, specifically by proposing potential actions or parameter adjustments, such as rebalancing portfolios based on specific objectives and constraints.
Souvik Das, Chief Technology Officer (CTO) at Clearwater Analytics, stated: “Our data shows that the global investment community is no longer just curious about AI. It is deploying it to solve the most labor-intensive operational tasks in asset management. By automating the heavy lifting of data synthesis and scenario modeling, firms are reclaiming thousands of hours that can now be redirected toward alpha-generating activities.”
Tactical Success in Day-to-Day Operations
The study also provides an assessment of the effectiveness of AI tools currently in use. Far from being a theoretical benefit, AI is already delivering measurable tactical advantages in daily work.
The first advantage is natural language interaction: 73% of surveyed managers rate the use of natural language AI agents to query data-dense investment platforms, risk management systems, and reconciliation tools as “effective.” Second is deep analysis: 62% of respondents consider AI agents effective for delving into complex topics like regulatory compliance, with nearly half (47%) describing these tools as “very effective.”
Third is workflow automation: The drive toward straight-through processing continues to gain momentum, with 63% of managers successfully using AI to automate repetitive workflows, such as daily report generation. The final advantage is multi-agent orchestration: 62% of firms report success in using AI to trigger operations based on data thresholds or specific schedules, indicating progress toward more autonomous and sophisticated system behaviors.
Solving the Data Dilemma
Data quality has historically been one of the industry’s greatest challenges. This study demonstrates that managers now understand why it is more critical than ever. Seventy percent of surveyed professionals note that deploying AI has intensified their focus on data governance, with 8% describing this shift in focus as “drastic.” Two-thirds (66%) consider their AI tools effective in managing the intricacies of alternative data—an area historically complex to scale.
That progress in alternative data is real. However, it has failed to close the truly decisive gap: only 56% of firms rate their data as accurate or reliable, compared to 79% that consider it complete. AI is focusing attention on data, but it has not yet resolved the underlying trust issue.
“What is striking is that AI adoption is forcing fund managers to confront data management fundamentals like nothing ever has before,” adds Souvik Das. “The confidence observed in alternative data management is telling. It suggests that firms investing in AI are also the ones investing most in ensuring data accuracy, and that both priorities must advance hand in hand. It represents a fundamental shift in how the industry perceives complexity,” he concludes.
Divergence in Adoption
Although most of the sector demonstrates an optimistic stance, the study highlights a growing divide between leaders and laggards. In several areas—such as software delivery and workflow coordination—between 12% and 18% of managers still foresee “little or minor impact” from AI. This divergence suggests that while the technology is ready, firms’ internal infrastructure and cultural maturity vary significantly.
