One-third of asset managers actively use AI -

36% of companies are actively using AI, and 37% plan to expand their use of it.
46% of companies are developing their AI capabilities entirely in-house, while 40% are taking a hybrid approach that combines internal resources with external partnerships.
Top challenges include data quality and maintenance (19%), understanding costs and building a business case (15%), and accessing AI expertise (13%).

Line DataGlobal leader in technology, data and services for wealth management and credit, today announced the findings of the report, “What's Next: A Deep Look at Artificial Intelligence in Wealth Management,” conducted in collaboration with . Global Fund Mediaa leader in market trend analysis for hedge fund and private equity professionals. The study explores the adoption and use of artificial intelligence (AI) by asset managers. In a volatile market environment with increasing regulatory requirements, AI has emerged as a key tool to address asset management challenges, especially in improving agility and operational efficiency. However, implementing these AI solutions takes time to establish a solid foundation, especially in overcoming challenges related to obtaining quality data and ensuring expected results.

AI becomes widespread due to the pursuit of productivity improvement

Currently, 32% of asset managers have not yet started their AI journey, 33% are in the experimentation phase, and 36% are actively using AI. Of the latter, 14% are using multiple use cases in production and plan further implementation. Over the next year, 37% of firms will focus on expanding their use of AI, 22% will increase experimentation, and 28% will monitor progress. The most common uses of Generative AI (GenAI) include document synthesis (28%), data extraction (28%), and knowledge base/Q&A (17%). Moreover, Generative AI is finding significant commercial applications, especially in improving front-office productivity. The most common use cases include optimizing transaction team efficiency (23%) and transaction research or direct yield generation (18%). AI also plays a key role in improving the productivity of middle- and back-office operations (19%).

“The adoption of AI in asset management is accelerating in a market under multiple pressures. More companies are investing in the technology to stay competitive and develop new use cases. However, implementing AI solutions and realizing their value is a technical undertaking that takes time to establish the right foundations, gain buy-in, drive cultural change and mitigate risks,” he explains. Jamil Jiva, global head of asset management at Linedata;

Expertise and data management challenges

46% of respondents have all their AI expertise in-house, with only 14% relying exclusively on external partners. The remaining 40% have a hybrid approach. AI solutions are primarily utilized by purchasing off-the-shelf products (25%), while a notable 18% have developed them entirely in-house. Conversely, 32% access AI solutions indirectly through brokers, fund administrators, or outsourcing service providers. Beyond the overall enthusiasm for AI adoption, challenges abound, including data quality and updating (19%), understanding costs and developing a business case (15%), and availability of AI expertise (13%). These challenges also arise when it comes to scaling AI solutions.

“Most firms prefer to keep tight control over their AI capabilities. However, building in-house expertise is difficult and no single solution can address all of a firm's needs. This is why hybrid approaches that combine internal resources with external partnerships are emerging. Data quality is clearly a major challenge to address. Data needs to be reliable, consistent, secure and easily accessible to be used to efficiently train large language models (LLMs). Given the heterogeneous systems spread across financial institutions, LLMs are clearly a large-scale project. Many asset managers are currently developing data lakes, which are complex projects that require clear goals,” adds Jamil Jiva.

Research Methodology

The survey was conducted in collaboration with Global Fund Media and focused primarily on hedge fund and private equity investment strategies, with participation from around 100 funds and asset managers. Data was collected through an online questionnaire and in-depth interviews in the first quarter of 2024. Respondents held a range of positions at companies of different sizes and geographic locations, with 40% based in North America, 40% in Europe, 15% in Asia Pacific and 5% in other regions.

Source: Line Data