In the past two months, we’ve spoken with vice presidents and associates at private equity firms, the senior deal-makers who comprise the core of investment work. But what about analysts, the often fresh-out-of-college or former investment banking professionals who are at the front lines where AI might play a role? This month, we interviewed just that cohort – the junior analysts who spend day in and day out sifting through documents, evaluating companies and modeling investment scenarios. While some of the most intense speculation around AI has to do with where it might replace knowledge work, our conversations with teams of analysts reveal a different story.

Far from fearing AI, junior employees are often the ones championing its integration, confident in their irreplaceable professional acumen—at least for now. As discussed in earlier articles in our "R-AI" series, private equity firms are eyeing AI adoption in AI evaluation, validation and value delivery. This is not dissimilar to the approach taken across corporate finance and strategy teams in M&A. Perhaps in both cases, it is the frontline associates and analysts who are most likely to be intimately familiar with the workflows at hand.

Embracing Digital Tools Amidst Rising Expectations

Today's analysts are among the first wave of professionals who are digital natives, born between 1996-2002. They are comfortable with technology, and some of them even began using it in college or their first job. This comfort, however, comes with its own set of challenges. Despite their proficiency, junior workers often experience higher pressure for manual work, navigating the tension between high expectations for technology adoption and manual rigor or familiarity with the work at hand. According to Bain, young workers see the highest average stress levels, but also show the most interest in automation and AI/ML.

When it came to analysts in PE, we observed a specific set of perspectives on this tension, which came up again and again in conversation. The vast majority of those we spoke with are excited about private equity work, and find that the intrinsic motivation to do well, get invested and excited about the space, with the promise of the career path that PE offers, outweighs many of the potential stressors of the workload. This decreases the incentive to offload it to automation, offshoring or similar approaches.

Even when we discussed the positive promise of generative AI (aiding, rather than replacing analysis work), analysts were excited, but skeptical, that tools were ready for adoption. Because of the degree of ownership felt over the core work of actually reasoning through the deal materials, many were reluctant to give this thinking up to robots. “Once you start having an AI do all of the work, you lose the thread on actually thinking through the hard parts of the deal that need a human decision-maker”, said one senior analyst from a New York-based PE shop.

This is a line of reasoning that we have heard frequently via our work at Keye, as well. “Analysis work” is not a pain point to be solved, but rather a core element of the job itself. That is why Keye, along with many other AI firms working in the analysis space, is less focused on automation and more interested in how technology can augment and enhance what humans are doing – which is what we hope to see reflected in studies on AI in the workplace going forward.

AI and Routine Financial Tasks: A Paradigm Shift

The majority of routine tasks that once occupied the bulk of a financial analyst’s day are increasingly the focus of AI application-makers for financial industries, like the ones we talked about in our market map article. Compiling profit and loss statements, basic financial forecasts, and roles traditionally focused on accounts receivable/payable and invoicing are now efficiently handled by AI systems, with less and less human oversight required for final verifications.

Some firms are also developing these tools in-house, with a heavy degree of involvement from front-line teams to help shape the process. For instance, one analyst leading an AI working group at a Boston-based PE firm has helped develop “a tool that acts as a copilot during the initial stages of client engagement”. Specifically helpful during the sourcing stage of identifying new deals, it uses a RAG-based system to scan through lists of prospective companies, gathers the latest news on each from the web, and crafts personalized introductory emails using AI-generated content. This tool reduces what used to be days of work into mere hours, processing information for over thirty companies swiftly and effectively.

Of course, such tools still require heavy amounts of human interaction, and this seems to be the sweet spot where many analysts are pushing their managers to invest and build tools. The integration of AI into private equity not only streamlines workflows but also opens new avenues for strategic analysis and decision-making. For example, many analysts are expected to be on the front lines of information gathering: reporting, newsletters, opposition research, and trend analysis—both macro and micro—are areas ripe for AI application.

The potential for AI to handle these complex tasks can significantly alter the role analysts play within the firm. Once considered more within the ‘frontline’ category of work, processing and gathering information rather than actually making decisions, analysts now have reach over a much broader range of activities. While still technically low on the decision-making tree, the ability to work with co-pilots is giving analysts a much closer look at real deal work, and in some cases, acts as an accelerant for their own career paths, in addition to the effectiveness of the firm.

This sentiment highlights a broader shift: as mundane tasks are automated, analysts can focus on higher-level strategies and creative aspects of financial management, enhancing their roles rather than diminishing them.

Future Outlook: Driving Bottoms-Up Transformation

One question that arose while investigating analysts’ role in driving the AI transformation in PE, is the same question that comes up in most conversations with financial professionals: can analysts actually use LLM-based tools when it comes to investment work? Are there still concerns about privacy, hallucination and effectiveness, and, more tactically, do their bosses allow them to use chatbots, summarization co-pilots, and other helpful software?

Perhaps unsurprisingly, the answer is similar to what more senior team members like associates and VPs tell us: for the most part, larger funds are limiting the adoption of 3rd-party tools and experimenting internally, while allowing the use of some co-pilot functionality within existing platforms like Microsoft Office. At smaller funds, the policy may be slightly more lax, allowing investors to use Gemini or other web-driven LLMs to offer initial takes on industry overviews and other content to “get up to speed”.

In these environments, analysts are pushing the envelope for experimenting with generative AI in processes like due diligence, where they stand to benefit the most in terms of time savings. Often, we have even spoken and worked with analysts who are directly involved in the process of evaluating and selecting vendors.

"The possibility of introducing AI into our daily workflows has been exciting, especially in automating mundane tasks," explains a junior analyst at a prominent New York-based private equity firm. The same analyst, who spearheaded the development of an automated tool for processing prospective companies, adds, "What used to be a grueling, multi-day project can now be accomplished in just a few hours. It’s about leveraging technology to work smarter, not harder, so we can focus on the most challenging content in the data room."

That conviction seems to be echoed across the industry, where AI integration is seen not as a replacement threat but as a significant efficiency booster. "Last year, our team automated over a million hours of basic financial tasks," shared a data scientist at an investment bank who was considering a move to the investing side of the house. "AI hasn't just sped up processes; it's freed our analysts to tackle complex problems and innovate new strategies."

Often, these technology and data science teams are working with analysts to scope the needs and opportunities for AI in sourcing, due diligence and modeling. Rather than impose their beliefs, managers and directors, at least at some funds, are content to let decentralized innovation drive some of the initial strategy for experimenting with AI.

While strategies can vary significantly from fund to fund, it is clear that many analysts feel both excited by AI’s potential to enhance their work and careers, and empowered to drive that change within their firms. While we have yet to dive deeper into the perspectives of the most senior firm leadership, follow this newsletter to hear managing director and CTO viewpoints as we close out the Agents of Change series in the coming month.