Artificial intelligence is now firmly embedded in asset managers’ investment operations, but are organizations fully equipped to employ the technology effectively?
New research from Clearwater, GenAI and the Data Divide – which surveyed insurance asset managers, hedge funds, private markets specialists and general asset managers – finds that while firms recognize the opportunities AI presents, they are concerned data integrity, model transparency, regulatory compliance and organizational readiness.
Consequently, the next phase of AI adoption will not simply be about deploying more powerful technology; it will need to focus on the foundations that allow firms to use it with confidence.
Nearly two-thirds (62%) of respondents are concerned that their organizations lack the skills and experience needed to use AI effectively, with 43% describing themselves as very concerned. More than half (52%) are also concerned that internal culture and resistance to change could slow adoption.
This highlights an important distinction between having access to AI and being ready to use it effectively.
Asset managers can invest in sophisticated tools, but without the right expertise, processes and culture, those tools may fail to deliver their potential. Employees need to understand not only how to use AI, but also when its output can be trusted and where human judgement remains essential.
At the heart of many AI concerns is a fundamental issue: the quality and reliability of the information underpinning the technology.
Two-thirds (67%) of asset managers are concerned about data governance, reliability and integrity risks. A further 64% are worried about operational risks, while the same proportion have concerns about model and algorithm transparency, explainability and bias.
These concerns become particularly important as AI moves closer to investment decision-making.
AI can process vast quantities of information and identify patterns at a speed humans cannot replicate. But speed and scale do not automatically mean accuracy. If the underlying data is incomplete, inconsistent or poorly governed, AI can simply make decisions based on flawed foundations faster.
This is particularly relevant when it comes to hallucinations. Some 62% of respondents are concerned about AI generating plausible sounding but false information.
For asset managers, the danger is not necessarily an obviously incorrect answer. It can be a convincing answer that appears credible precisely because it is delivered with confidence.
Trust also needs to extend beyond investment and technology teams.
More than half (55%) of global asset managers are concerned about the regulatory risks associated with AI, including 30% who are very concerned. Meanwhile, 58% are worried about financial risks arising from AI, including credit, market and fraud risks.
Implementation presents another hurdle, with 64% concerned about the costs involved in implementing AI.
This creates a difficult balancing act. Firms need to innovate quickly enough to remain competitive while ensuring that AI-enabled processes can withstand regulatory scrutiny and meet existing governance standards.
Transparency will therefore become increasingly important. If an AI system contributes to an investment decision, risk assessment or operational process, firms need to understand where the information came from, how it was processed and how its output should be interpreted.
Despite these concerns, the research does not suggest asset managers are turning away from AI. Instead, firms appear to favor a model that combines technology with human expertise.
An overwhelming 93% of respondents agree that the best approach is to balance human expertise with technology, using AI to augment and empower risk teams rather than replace them.
That human oversight remains particularly important given that 16% of respondents say they are not prepared for AI-enabled operational risks, while 15% are not ready for risks arising from a lack of AI skills and experience.
The asset management industry has spent considerable time asking what AI can do. Increasingly, the more important question is whether firms have the infrastructure, data and governance to use it responsibly.
As firms look to move AI from experimentation into business-critical processes, having reliable, accessible and well-governed data is essential. Clearwater’s technology and investment data expertise can help asset managers build that foundation, enabling them to adopt AI more confidently while keeping human judgement, risk management and control at the center of the process.
Clearwater AI embeds AI directly into the investment workflow, so teams can surface insights, ask questions of their data, and work more efficiently across complex portfolios.
To see what separates firms turning AI investment into measurable value from those still struggling to realize its potential, download the full report, GenAI and the Data Divide.