Artificial intelligence is becoming increasingly embedded in asset management operations, yet new research from Clearwater Analytics (NYSE: CWAN) reveals that firms across the sector are deeply concerned about the risks the technology introduces — spanning data governance, regulatory compliance, operational integrity, and beyond. The findings arrive as generative AI tools move from pilot experiments toward production deployment across financial services, intensifying pressure on firms to translate enthusiasm into controlled, auditable workflows.

Clearwater's "Gen AI and Data Divide" study surveyed insurance asset managers, hedge funds, private markets specialists, and general asset managers. The findings indicate that while AI presents clear efficiency gains and alpha-generation opportunities, it also creates new vulnerabilities that firms must actively manage — particularly as regulators worldwide sharpen their focus on how financial institutions deploy automated systems.

Adoption Readiness Gap

The most striking finding concerns the disconnect between AI adoption and organizational readiness. Nearly two-thirds (62%) of asset managers are concerned they lack the skills and experience needed to use AI effectively, with 43% describing themselves as very concerned.

More than half (52%) separately worry that internal culture and resistance to change will slow adoption and readiness. The findings suggest that for many firms, the biggest barrier to realizing AI's potential is not the technology itself, but rather the organizational culture surrounding it.

Trust, Governance, and Reliability

At the core of firms' AI concerns lies a fundamental question of trust — whether they can rely on what the technology produces. Two-thirds (67%) of asset managers are concerned about data governance, reliability, and integrity risks, while 64% are concerned about operational risks.

Given the growing number of asset managers leveraging AI to support investment decision-making, 64% of research participants say they are worried about model and algorithm transparency, explainability, and bias. Additionally, 62% are concerned about hallucinations — instances where AI generates plausible-sounding but false information and presents it as fact. These concerns are especially pointed in an industry bound by fiduciary obligations, where unreliable outputs could affect client outcomes and trigger disclosure duties.

Regulatory, Financial, and Cost Concerns

More than half (55%) of global asset managers say they are concerned about the regulatory risks associated with AI, with 30% characterizing themselves as very concerned. A comparable share (58%) are worried about financial risks stemming from AI, including the management of credit, market, and fraud risks. Implementation costs are also a significant worry, cited by 64% of respondents.

The regulatory anxiety reflects a shifting compliance landscape. The U.S. Securities and Exchange Commission has proposed rules addressing conflicts of interest associated with predictive data analytics and AI-driven interactions. In Europe, the EU AI Act — provisionally agreed in late 2023 and formally adopted in 2024 — designates certain AI applications in financial services as high-risk, imposing documentation, transparency, and human oversight requirements.

Preparedness Gaps Remain

While many asset managers report feeling prepared to manage AI-enabled risks, a notable proportion acknowledge they still have work to do. Specifically, 16% of surveyed asset managers say they are not prepared for AI-enabled operational risks, and 15% say they are not ready for risks arising from a lack of AI skills and experience across their organizations.

Source: Clearwater Analytics