Used properly, AI technologies can enhance every stage of the allocator’s process, automating routine work, detecting inconsistencies, classifying strategies, and tracking changes across vintages and managers. Tools such as natural language processing (NLP), machine learning (ML), large language models (LLMs), and autonomous agents can now extract, structure, and compare information from the mountains of documents and data that surround private-market investing.
Scalability is where AI adds the most value. With clear prompting and oversight, AI can save hours of work and free up human teams to focus on insight, context, and conviction. The lesson for investment managers is not to reject AI tools but to govern them with allocators as the final interpreters and decision makers.
The models do not profoundly think about or understand institutional investing; they predict the probability of a particular outcome which is predicated on data availability and quality. To wit, they can fall short, misread nuances, fabricate information, or overlook subtleties that experienced professionals instinctively catch. AI tools should enhance and support decision-making, not replace it.