Arnie Fridhandler
Olivia Greer
Just two years ago, generative AI was largely overlooked in the private equity space. It was considered an afterthought – something from the movies, something occasionally mentioned by Elon Musk as the biggest threat to humanity – certainly not a topic of investment committees, nor office chatter by anyone other than software engineers in Silicon Valley.
With a stroke of the keyboard and launch of ChatGPT, 2023 turned out to be the year AI burst into the mainstream. Every conference (or gathering of more than a few people, for that matter) seemed to include robust discussion on AI. It became easy to experiment and interact with the latest technology, and most private equity deal professionals have now seen AI in action. AI is no longer the exclusive domain of tech giants; it’s becoming a low-cost staple in modern business operations.
After consulting with a number of our clients that have grappled with AI, we are hearing mixed approaches on how best to use the technology. We have found many sponsors to still be in “wait and see” mode, hesitant to wade too deeply into uncharted waters, while others are dedicating significant resources to scaling up capabilities and use of AI with appropriate guard rails. As we look ahead, we see the dichotomy quickly tilting in one direction – to innovation – and the rapid development of AI applications has already revealed the gap between cautionary and resolute as an easy one for many to bridge.
PE Impact Underway
The private equity market and investors already perceive AI as a differentiator. AI is not a technology or one standalone product; it is a once-in-a-generation sea change that has swiftly become, among many other things, an integral layer enhancing existing tools and processes that are critical to the day-to-day work of investment professionals. From Microsoft Copilot to Zoom meeting summaries with AI Companion, AI is already incorporated into ubiquitous tools installed at some PE firms.
AI is enabling better internal systems, from deal flow trackers to reporting portals, streamlining and automating tasks that tend to be prone to human error and inertia. We’ve also seen AI open doors for new software entrants who use it as means to introduce their products, but with the added flair of AI. Think: analytics and summaries for your Slack channels.
Investment in AI-focused tech companies is also beginning to disrupt value propositions, creating new opportunities and risks. The implications of AI’s advancements span across sectors, from manufacturing to healthcare to business services, and every sector in between. Simply put, AI marks a new era of possibilities and challenges.
Challenges
Despite the optimism, concerns about AI’s accuracy, inherent biases, confidentiality, including with respect to sensitive information, and ethical implications persist. There’s skepticism about over-reliance on AI, particularly in an industry that thrives on nuanced judgments. Another barrier is the use of cloud-based large language models, which raise questions about data confidentiality. Solutions have been proposed and implemented to sandbox information in the cloud, most of which have not yet been truly tested – but this is an important area to watch for innovation.
Practical considerations raised by these issues will be complicated by legislation and rulemaking on AI. These include the White House Executive Order on AI at the end of 2023 and proposed rules, risk assessments and pilot programs that have quickly followed from a number of federal agencies touching several sectors, including the Departments of Defense, Homeland Security, Commerce, Treasury, Health and Human Services and Transportation. In addition, numerous states enacting AI-related legislation, and Europe’s expansive Artificial Intelligence Act expected to be finalized early this year.
Experimentation is Differentiation
In practice, PE firms are experimenting with AI in “low-hanging fruit” use cases. Many are customizing publicly available tools, like a branded GPT-4 chat, or employing AI to index and search less-sensitive parts of file systems. Others are permitting, sometimes implicitly, deal teams to experiment with public tools, and many junior and mid-level deal professionals are leaning into the opportunity to pioneer the use of AI for tasks, like writing emails and unpacking complex Excel functions.
Larger firms with robust technology back offices are also aggressively exploring AI’s potential and adding headcount in AI-specific roles. The future of AI in PE hinges not on the size of a development team, but whether each firm’s culture promotes continuous experimentation in search of high-impact use cases, and attracts the type of individuals who are adventurous with the tools that are available, even public, low-cost ones.
At the very least, AI is going to rapidly improve internal deal processes, CRM/sourcing, investment modeling, and reporting. Addressing concerns around confidentiality and data protection will be crucial for the widespread adoption and trust in AI within the PE sector. Amidst this wave of growth, we anticipate that many PE firms will soon lean from AI-hesitant to AI-innovative, and deal professionals with a knack for experimentation will quickly find efficiencies to stand out from others.
Looking Ahead
AI in private equity is a double-edged sword, offering groundbreaking opportunities while demanding careful navigation of its challenges. It will be vital though, that sponsors embrace experimentation, and perhaps even some healthy risk-taking, all within the bounds of our regulatory and ethical sandboxes.
What that looks like will be different for each sponsor, but all should begin with organization-wide level-setting. Sponsors should deploy written AI governance and acceptable use policies, internal steering committees and, in some cases, engage outside experts, whether technologists, counsel, or both. Build a path to encourage teams to run ahead with their ideas in the real world, and give them tools and guidance to stimulate innovation.
At the individual level, bringing your firm from AI-hesitant to AI-innovative is much easier than you might expect, even with rigid policies. Try using AI to help write that difficult email. Have it work through a complex Excel problem. Scour your calendar and contacts for opportunities to build more connections. All these use cases themselves are iterative lessons, in real-time, on how to use AI for even more grueling issues, and more importantly, insights into how to get that leg-up.
