Applying AI in private markets: highlights from our Boston Happy Hour

We recently hosted top investment operations leaders in Boston for an engaging panel discussion followed by networking over drinks and hors d'oeuvres. Our panelists, Brian Wheeler, Senior Director at Liberty Mutual Investments; Laura Jesson, Research Analyst at Cutter Associates; and Usha Veerabhadraiah, VP, Information Technology at HarbourVest Partners, shared their stories of how private markets firms are leveraging AI, across both the front and back office.

Read our top takeaways from the panel below:

Where are firms focusing as they start leveraging AI?

Operational efficiency: Private markets firms are leveraging AI to automate previously manual processes, such as parsing, classifying, extracting, and transforming incoming fund and portfolio data, such as financial statements. Our panelists have already put machine learning applications for processing their data into production, or are in the process of doing so. AI tools for processing data can be utilized across the firm, from investment operations to vendor management, client onboarding, and investment strategy, across private equity, private credit, fixed income, real assets, and more. Each use case can be extended to other opportunities in other teams, helping firms to not just automate but optimize their workflows.

Investment strategy and insights: New generative AI tools are giving firms the opportunity to ask deeper questions about their portfolio data and generate new insights. Our panelists are exploring generative AI use cases and beginning to experiment with rolling out new tools to their teams. To move from experiment into practice, firms are learning how to scale genAI from both a people and process perspective. With one firm dubbing 2024 “the year of exploration,” our panelists agree that there’s broad enthusiasm for implementing generative AI, and both operations and investment teams are focused on finding the right use cases.

What lessons have our panelists learned so far?

Managing the hype: Teams across firms’ lines of business are enthusiastic about using AI in their workflows, and managing that enthusiasm has been an important and unexpected part of rolling out new AI use cases. Firms need to choose which use cases to prioritize. One firm is building a use case scoring model, helping them to determine which use cases to pilot by identifying which will have the best ROI.

Sorting through the noise: With the number of vendors offering generative AI products in a short period of time, firms have had to dedicate resources to finding companies offering real, innovative AI technology. While they may have felt confident navigating the sales cycle and implementation process for traditional tech, finding the right questions to ask when evaluating AI software has been a learning curve, particularly when it comes to data governance and security requirements.

How are firms thinking about data governance for AI workflows?

Formalizing data governance and focusing on responsible AI is a priority for multiple firms. The evaluation of technology, models, and use cases means firms need to perform a constant risk assessment and review of new technology, adding a new layer of difficulty to their traditional implementation processes. Firms need to know how data is being processed, who has access to and review of the data, and build data validation checks into their pipeline.

Our CEO, Harald Collet, the panel moderator, referenced the unique security needs of Alkymi customers, such as large asset owners that want to begin using generative AI but need to ensure their data isn’t shared. The evolution of open source models, alongside private cloud offerings, can give firms access to the benefits of generative AI without the fear of data leaving their environment.

How are firms measuring the ROI of new AI technologies?

In the short term, firms are most focused on their efficiency goals. Productivity gains and FTE hours saved are essential measures when implementing AI-powered technology into operations, compliance, and risk teams. Those gains can also have a flywheel effect—reducing analysts’ time spent on processing data means they have more time to produce analytics, leading to better investment decisions and driving economies of scale.

Many firms are employing a pilot approach to AI, with established KPIs and clear goals, as well as use case scoring to identify which new processes will have the greatest immediate ROI. However, our panelists all agreed that investing in AI requires a longer-term perspective and multi-year investment for firms to fully reap the benefits of AI-powered workflows, and firms need to start implementing AI now to stay competitive.

Interested in coming to our next event? Contact us.