Linedata, a global provider of asset management and credit technology, data, and services, today announces the results of its report, "What’s Next: A closer look at Artificial Intelligence in Asset Management," conducted in collaboration with Global Fund Media, a leader in market trend analysis for hedge fund and private equity professionals. This study explores the adoption and use of artificial intelligence (AI) by asset management firms. In a volatile market environment with increasing regulatory demands, AI is emerging as a key tool for addressing asset management challenges, particularly in enhancing agility and operational efficiency. However, implementing these AI solutions will require time to establish a solid foundation, especially in overcoming challenges related to acquiring quality data and ensuring expected outcomes.

AI Gains Ground Driven by the Quest for Productivity

Currently, 32% of asset management firms have not yet started their AI journey, while 33% are in the experimentation phase, and 36% are actively using AI. Among the latter, 14% have multiple use cases in production and plan further implementations. Over the next year, 37% of companies will focus on expanding AI use, 22% will increase experimentation, and 28% will monitor progress. The most popular applications of Generative AI (GenAI) include document synthesis (28%), data extraction (28%), and knowledge bases/Q&A (17%). Additionally, Generative AI is finding crucial commercial applications, particularly in enhancing front office productivity. Optimizing the efficiency of the transaction team (23%) and directly generating transaction research or yields (18%) are among the most common use cases. AI also plays a vital role in boosting the productivity of middle and back-office operations (19%).

"In a market under multiple pressures, AI adoption in asset management is accelerating. More companies are investing in this technology to stay competitive and develop new use cases. However, implementing AI solutions and realizing their value is a technological endeavor that requires time to establish the right foundations, secure buy-in, drive cultural change, and mitigate risks," explains Jamil Jiva, Global Head of Asset Management at Linedata.

Expertise and Data Management Challenges

For 46% of respondents, AI expertise comes entirely from within the company, while only 14% rely solely on external partners. The remaining 40% use a hybrid approach. AI solutions are primarily accessed by purchasing off-the-shelf products (25%), though a notable 18% develop them entirely in-house. Conversely, 32% access AI solutions indirectly through brokers, fund administrators, or outsourcing service providers. Besides AI adoption overall enthusiasm, challenges are numerous: data quality and update (19%), costs understanding and business cases development (15%) along with AI expertise availability (13%). These challenges also do appear when it comes to AI solutions extension.

"Most companies prefer to tightly control their AI capabilities. However, developing in-house expertise is challenging, and no single solution can address all a company's needs. This has led to the rise of hybrid approaches that combine internal resources with external partnerships. Data-quality clearly appears as a major challenge to address. Data must be reliable, consistent, safe, and easily accessible to be used efficiently training Large Language Models (LLMs), which clearly sits as a massive project in light of heterogenous systems that spans across the financial institutions. Many asset managers are now developing data lakes, which proves a complex project that requires clear objectives.”, adds Jamil Jiva.