Hedge funds are increasingly using artificial intelligence to accelerate research and data processing, particularly in time-sensitive strategies, while maintaining human oversight for trading decisions amid concerns over accuracy and data security, according to a report by the Financial Times.
Firms such as Sand Grove Capital Management are using large language models including tools from Anthropic, OpenAI and Microsoft to rapidly analyse lengthy merger and acquisition documents that previously required significant manual effort. Tasks that once took analysts more than an hour can now be completed in seconds, allowing event-driven strategies to respond more quickly to market-moving announcements.
Sand Grove, which focuses on corporate events and merger arbitrage opportunities, said AI systems are particularly effective at rapidly extracting key details from complex legal filings, helping the firm form initial views more quickly when deal activity emerges.
Across the industry, adoption of AI tools has become widespread. An industry survey by the Alternative Investment Management Association found that the vast majority of hedge funds are now using AI in some capacity, with common applications including research, document summarisation and meeting analysis. Many firms also expect usage to increase further over the next year as capabilities improve.
Data and workflow specialist firms such as Permutable AI say AI is increasingly being embedded across hedge fund operations, particularly in processing company filings and scanning large datasets for potential investment signals. Much of the current efficiency gain is concentrated in automating tasks traditionally handled by junior analysts, enabling faster identification of potential opportunities.
Emerging markets-focused managers such as Pharo Management are also exploring AI applications across research, portfolio support and back-office operations, while acknowledging that core investment decisions remain human-led.
Despite rapid adoption, hedge funds remain cautious about relying on AI for execution or portfolio construction. Concerns highlighted by industry surveys include inaccurate outputs, commonly referred to as hallucinations, as well as data privacy and cybersecurity risks.
Some firms have also pointed to recent incidents involving AI systems operating outside controlled environments as reinforcing the need for caution and oversight.
Industry practitioners emphasise that while AI is increasingly central to research workflows and operational efficiency, investment decisions continue to require human judgement. As one senior manager noted, AI can enhance analysis and speed, but final portfolio decisions are still best left to experienced professionals.
