AI may look like a quick fix to many managers who may not really know what they are doing.
But AI isn’t a magic elixir to improve your fund’s performance.
Angelo Calvello and Leo Svoboda tells a cautionary tale of AI in this article published in Institutional Investor:
AI adoption is expensive; even a modest effort costs millions of dollars per year. Because AI is inherently a long-term project, the manager should provide a budget that is congruent with the problems to be solved. The heart of any AI project is the research agenda, so ask the manager to share it. You don’t need the technical details, but the manager should be able to tell you its R&D plans (including work flow) and how they help solve their specific problem. Also, find out the source of the funding, which could reveal if the manager is making cuts elsewhere in the organization.
Today the generally poor performance of the active management community and the existential threat from “the machines” have managers grasping for a quick fix. Many seem to think that AI offers that solution.
There are managers for which AI is a natural extension of work they are already doing, for which the staff is already in place and the expertise exists internally. Other managers are convinced that AI — usually in the form of alternative data sets — is table stakes, but don’t have a clear understanding of what that means for their organizations and investment processes. They are more likely than not to find their experiments with AI to be expensive distractions. For this reason, don’t discount managers who are avoiding the hype. A manager that honestly admits that it doesn’t see an application for AI in its process, isn’t really sure where to start, or isn’t willing to spend the time and money to fully investigate the application may be a realist rather than a Luddite.