When we started interviewing leading private equity professionals for our article series earlier this year, AI usage within deal teams was a nascent topic and funds were still figuring out their initial point of view on the matter. Today, many other consulting and AI blogs have followed suit. Among others, McKinsey, Brain and Accenture have covered the rising role of generative artificial intelligence on investors' ability to enhance and accelerate their due diligence.

To form our perspective, we talked to everyone from CIOs to rising star associates, from VPs to frontline analysts. The one viewpoint they all had in common was that while the technology was still imperfect, the time to figure out an approach to integrating machine learning into core workflows was now. According to Pitchbook, funds are set to spend $80 billion on due diligence (DD) over the next 5 years, and managers know they need to make the most of every dollar.

Often, analysts and associates are bubbling up key insights from the deal process and what they hear from peers, while team leaders and technologists are evaluating the various benefits and risks to different approaches. Through our work at Keye, we knew that fund managers had a vested interest in developing a head-start in AI adoption too.

This series rounds out with just that perspective – what do managing directors at leading funds really think about the way technology is reshaping their teams’ work? First, we needed to understand the issues they care about most when it comes to due diligence, and other knowledge- and time-intensive tasks.

What Keeps MDs Up At Night

Even in the age of automation, due diligence remains one of the most challenging work streams for private equity deal teams. It is both a risky time-drain, and a source of competitive advantage for funds that can bring unique insights to the table, or move fast enough to bid on more deals than others.

In a recent Private Equity Leader Survey conducted by Accenture, it was also a major topic of analysis. When asked to rank the challenges they face when it comes to due diligence, most senior leaders highlight multiple issues related to discovery and data quality. In fact, lack of quality third-party data was the number one challenge, while post-deal surprise gaps, disconnected insights and a lack of modern technology for DD all made the top 5.

One leader of a Philadelphia-based lower middle market PE fund we spoke with validated this, saying “our teams spend days sometimes just hunting down one number we need for a model, or we pay experts thousands of dollars to make one up. At the end of the day, we’re surprised there isn’t yet an AI tool that can triangulate better estimates using everything that’s out there on the web”.

Getting these numbers wrong really does keep MDs up at night - often literally, when they are waiting for their VPs and associates to corral all of the data into the right format to get a due diligence finalized. Because of that, PE funds are putting more emphasis on pre-deal activities including DD, better quality deal flow, screening and building value creation plans. Managers know that the cost of a missed deal is large, but for a space that is more risk-averse than venture capital, betting on a false positive too many times can also have disastrous results for a fund’s lifecycle.

MDs Know Their Organizations Need to Transform…

Due diligence isn’t just a core risk or differentiator – speaking to several fund managers working more heavily with technology investment targets, it’s also getting more complex as the scope of DD expands. Whereas it traditionally may have covered financials, commercials and legal, teams are now regularly diligencing IT, cybersecurity and ESG, all of which is relatively data intensive and can often be better handled by AI systems.

AI's integration into due diligence processes exemplifies a shift from manual to technology-driven methods, marking a profound change in operational strategies. According to a KKR analyst, AI-enhanced tools "process vast datasets with a speed and accuracy that human analysts cannot match." This capability is crucial in an industry where time is a premium and the depth of analysis can dramatically influence investment outcomes.

These AI systems are designed to handle complex analyses by integrating diverse data sources, including financial records, market trends, and regulatory databases, into a cohesive assessment tool. Such integration offers a comprehensive view that is nearly impossible to achieve manually. The analyst highlights, "AI can reveal insights across a target company’s data landscape faster than traditional methods, allowing for quicker, more informed decision making."

As we have discussed in previous articles, this means that funds will need to update their processes and organizations, and their leaders know this. One significant concern that teams need to address with human thinking is the opacity of AI decision-making processes. One Accenture report cautions that "the black box nature of AI algorithms can obscure the logic behind critical investment decisions, necessitating enhanced transparency to maintain trust and compliance." This lack of transparency can complicate accountability and increase the risk of bias, making it imperative for firms to develop robust methods to interrogate and understand AI-driven outputs.

…But the MD role will need to transform too

Since return-to-office and the subsequent AI boom, PE leaders are telling us that they, too, are now expected to take on more work to drive their funds’ innovation holistically. Two leaders from an Atlanta-based fund we spoke with have boards who not only expect them to oversee investment strategies but also to harness AI's potential to drive value creation across their portfolios. According to insights from the interviews, "MDs are now at the forefront of incorporating advanced analytics and machine learning tools to identify investment opportunities and risks." This is especially true when their is no full-time CTO to work alongside, and certainly in that instance, this shift requires a new breed of MDs who are as fluent in technology as they are in financial acumen.

The pressure to integrate AI effectively into the decision-making process has amplified the demand for MDs to possess a dual focus on both the macro-economic trends and the granular, data-driven insights that AI offers. "The challenge for today's MDs," one industry expert told us, "is to balance the rapid pace of technological change with the firm’s long-term strategic goals." This balancing act is crucial in maintaining a competitive edge in a market that increasingly rewards data-centric investment strategies.

The strategic use of AI in portfolio management allows MDs to make more informed decisions about when to double down on investments or divest, based on predictive models that analyze market conditions and company performance. AI tools are particularly effective in identifying patterns that may not be apparent through traditional analysis methods. For instance, one highlighted, "AI systems can identify subtle shifts in consumer behavior or supply chain disruptions before they become apparent, allowing us to react more proactively when we would have been caught off guard by a gap post-deal execution”. Often we were surprised by the depth of MDs’ knowledge – they were familiar with not only Keye, but also the supporting data clouds like Allvue and Planr.

Lastly, MDs are expected to lead the cultural shift within their firms, promoting a mindset that embraces continuous learning and adaptation. The integration of AI tools not only alters workflow processes but also demands a change in the organizational culture to foster innovation. "Managing Directors have to cultivate an environment where teams are encouraged to experiment and learn from AI-driven insights," the Atlanta-based MD told us. This cultural transformation is pivotal in ensuring that AI tools are used effectively to power fund-level ROI.

As we close the curtain on our 'Agents of Change' series, it's worth emphasizing that the emergence of AI in private equity isn't just about adopting new technologies but about redefining the core of decision-making and value creation. “In the end,” as one seasoned MD put it, “our tech and insights don’t just support our decisions; they challenge us to operate at the top of the pack. We have to make sure we're not just participants in the market but makers of it.” Here’s to the daring leaders who are setting the course, one LLM at a time.