Completed Project Case Study: Contemporary Artificial Intelligence & Machine Learning Adoption by Fund Manager  

Artificial intelligence (AI) and machine learning (ML) are transforming the financial sector, but how far have these technologies really been adopted by fund managers, and what barriers stand in the way? A four-month research project led by academics at the University of Nottingham set out to answer these questions. 

The project

The study was led by Dr Huamao Wang, with co-leads Professor Meryem Duygun, Dr Sam Beatson, and Dr Linh Hoang Nguyen. Working in partnership with Cadro Technologies Limited, the team designed and carried out a survey and interview programme. The survey was distributed to fund managers listed in the BarclayHedge Global database, ensuring broad representation from different types of markets and regions.  To complement this, the team conducted in-depth interviews with selected managers. The aim was to capture first-hand insights into how managers perceive AI and ML, how they are using these tools, and what challenges remain.

Collaboration in Practice 

Working with Cadro Technologies was critical to the project’s success. Their industry expertise helped refine the survey, build trust with respondents, and secure access to a wide network of fund managers. This collaboration ensured that the academic analysis was grounded in real-world practice, while fund managers gained early insights into how peers are approaching AI and ML. 

The project has delivered practical and policy recommendations for fund managers, investors, regulators, and researchers. These include guidance on how to leverage AI and ML responsibly to improve performance, while addressing organisational, ethical, and regulatory challenges. 

What the research uncovered 

Despite widespread AI transformation speculation in investment management, empirical evidence on fund manager adoption patterns remains limited. Through survey and interview analysis, we find 77% of funds have initiated AI adoption, primarily in early stages focused on investment analysis (66%) rather than risk management (23%). Fund managers’ AI understanding emerges as the strongest adoption predictor. AI currently functions as augmentation rather than replacement technology, with larger funds showing lower adoption due to complexity, while knowledgeable managers express greater expertise concerns. Internal efficiency pressures drive adoption more than competition, with expertise gaps representing primary barriers. Results suggest industry democratisation of analytical capabilities and a transitional movement toward comprehensive automation with significant workforce and structural implications. 

Impact and insights 

The findings provided fund managers with a clearer picture of how peers are using AI/ML and what strategies are working. For some, it opened new opportunities to consider AI-driven tools more confidently. For others, the insights reinforced the need for robust governance before scaling adoption. 

For policymakers and regulators, the research offers evidence to inform future frameworks, ensuring that innovation can thrive without undermining market stability. 

Looking ahead 

Beyond its immediate findings, the study has laid the foundation for future research. By mapping adoption trends and building relationships with the industry, future research will prioritise longitudinal tracking of AI adoption trajectories to understand how current augmentation applications evolve toward more autonomous systems, while simultaneously investigating the broader ecosystem effects of widespread AI adoption on market efficiency, competitive dynamics, and systemic risk characteristics. 

In just four months, the collaboration between the University of Nottingham and Cadro Technologies has deepened understanding of one of finance’s most urgent “wicked problems”: how to embrace AI in ways that deliver efficiency and innovation without compromising resilience, trust, or fairness. 

Quote from the project lead 

“This research fills a crucial void in understanding how fund managers are actually implementing AI and machine learning technologies. We’re not just looking at adoption rates, but the real-world factors that drive success or failure in these implementations.” 

Quotes from the fund manager participants 

“The democratisation of finance will come from AI, not from human advisors.” 

“Eventually, one person with AI can replace an entire team of analysts.” 

“If a fund isn’t invested in AI by 2030, it’s dead because the industry is moving so fast.”