The private equity (PE) and private debt (PD) industries operate in an environment of increasing competition, growing data complexity, and relentless pressure to improve efficiency. Yet, firms face persistent operational challenges that hinder their ability to maximize deal flow, streamline workflows, and optimize decision-making. To better understand these pain points and evaluate the role of Artificial Intelligence (AI)—specifically large language models (LLMs)—in addressing them, we conducted structured interviews with 83 North American investment funds and M&A boutiques.
This research provides a deep dive into the most pressing inefficiencies plaguing PE and PD firms, including deal sourcing, document management, market research, reporting, and financial analysis. Through a combination of qualitative insights and specific examples, the findings reveal not only the limitations of current practices but also the untapped potential of AI technologies to enhance workflows. However, this report takes a measured approach, critically examining both the opportunities and constraints of AI adoption in the nuanced and relationship-driven world of private markets.
By identifying key pain points and assessing how AI can (and cannot) provide solutions, this report aims to offer actionable insights for investment firms seeking to improve operational efficiency while navigating the limits of technology in a complex industry.
Research Methodology
This report draws on insights obtained through structured interviews with 83 North American investment funds and M&A boutiques. Participants included managing directors, analysts, and C-suite executives, providing a diverse perspective on the operational challenges faced across the industry. The primary objective was to identify and understand the pain points experienced in daily operations while uncovering opportunities for leveraging Artificial Intelligence (specifically, large language models or LLMs) to address these challenges.
Results
Our analysis identified key operational challenges faced by PE and PD firms. Below, we rank these pain points by frequency.
Top 5 Pain Points for PE and PD
1. Deal Sourcing
Deal sourcing is the lifeblood of investment firms, yet 52% of respondents identified it as a major bottleneck. From struggling to access proprietary deal flow to maintaining vital intermediary relationships, firms face mounting challenges, particularly as competition intensifies. For smaller players, the hunt for high-quality opportunities can feel like chasing a moving target.
A fund manager at a $3 billion AUM firm described the frustration: "Finding the right deals feels like searching for a needle in a haystack. You rely on instincts, personal networks, and luck—but it’s exhausting and far from scalable."
The traditional approach remains dominant: cold outreach, LinkedIn messages, and boots-on-the-ground networking. "We’re meeting wealth managers, accountants—anyone who might know someone selling a business. It’s time-consuming, and often, the leads don’t go anywhere," shared another fund manager at a mid-sized private equity firm.
2. Market Research
Comprehensive market research is crucial for informed investment decisions and client advisory, yet 48% of firms report significant challenges in this area. A major obstacle is the fragmentation of information across multiple sources such as SEC filings, industry reports, event transcripts, and research platforms. This scattering of critical data forces teams to navigate disparate systems, complicating access to the insights they need.
One fund manager expressed the frustration: "Information is scattered everywhere—SEC filings here, industry reports there. Connecting the dots takes far more time than it should." This fragmented landscape often requires firms to spend up to 25 hours per week sifting through and consolidating data manually, draining time and resources.
3. Document and Information Management
Document and information management emerged as the third most reported pain point, with 47% of investment firms identifying it as a major challenge. Analysts spend an average of 3 hours per day (15 hours per week) manually managing and searching through critical documents like financial reports, legal contracts, due diligence materials, and investment memoranda. This inefficiency not only impacts team morale but also limits the firm's capacity to process more deals.
A managing director expressed his frustration: "Accessing information shouldn't be this hard. I often have to assign a junior associate just to aggregate data into a spreadsheet—something that should take seconds, not hours."
The repetitive nature of document handling also increases the risk of errors. As one investment manager observed: "Everything is done manually, from reviewing reports to cross-checking data, and it's easy to miss inconsistencies."
4. Document Generation
Document generation was identified as a significant challenge by 34% of firms. Producing key documents such as credit memos, Confidential Information Memorandums (CIMs), management presentations, and compliance reports is highly time-intensive, with investment memos typically taking a week of an analyst’s time.
To save time, some firms are exploring outsourcing options. One professional shared: "Drafting the first credit memo is tedious but manageable. We’re considering outsourcing it to India to free up our analysts for more valuable tasks."
Despite the time burden, many recognize the value of hands-on preparation for analyst development. "The process helps analysts deeply understand the credit," noted one investment manager. Still, the inefficiency remains frustrating, as another analyst explained: "Even if I understand it in my mind, I have to write everything out and distill it into paragraphs—it’s tedious and time-consuming." Balancing efficiency with skill development continues to challenge firms.
5. Analysis
Analysis—including financial modeling, portfolio monitoring, and manual data entry—emerged as the fifth most cited pain point, reported by 30% of firms. These tasks are critical for evaluating investment opportunities and tracking portfolio performance but are highly time-intensive. As one investment professional noted: "Even with a good template, financial modeling for a new deal takes 50 to 70 hours of work."
The high stakes involved compound the challenge, as the outputs—financial models, performance reports, and forecasts—are mission-critical and must be error-free. This demand for precision often deters firms from fully embracing automation. One professional expressed skepticism: "For modeling and generating financial statements, I don't trust anyone but myself to do it. AI isn’t there yet."
How AI can help?
Investment firms face significant inefficiencies across critical areas like deal sourcing, document management, market research, document preparation, and analysis. AI can offer pratical solutions to some of these challenges.
1. Document and Information Management
AI can simplify document management by automating organization and information retrieval. AI tools can tag and categorize files, enabling quick access based on content and metadata. Advanced search features powered by large language models (LLMs) allow users to find information using natural language queries, significantly reducing time spent searching. For instance, a private equity firm implemented an AI-driven system to organize and retrieve due diligence reports, cutting document retrieval time by 28%.
AI also extracts key data from unstructured documents, such as financial statements and contracts, and indexes it into centralized repositories. A private debt fund used AI to extract financial metrics from loan agreements, reducing manual data entry by 24%.
These examples demonstrate how private equity and private debt firms are leveraging AI to research across different knowledge sources.
2. Market Research
AI can be used by private equity and private debt firms for streamlining how they process, search, and utilize data. AI can analyze financial news, industry reports, and regulatory updates as they happen. For example, a private equity firm used AI to identify a sharp shift in commodity prices, allowing them to renegotiate terms with a portfolio company’s supplier before the impact escalated.
Advanced natural language search tools can further enhance research by understanding context and semantics, enabling analysts to extract precise insights. A private debt fund used AI to scan regulatory filings and industry reports, synthesizing key points into actionable insights. This reduced research time by 19% while improving the accuracy of their analysis.
AI can also enrich data by integrating multiple sources such as SEC filings, private company databases (e.g., PitchBook and CB Insights), and subscription services like Bloomberg Terminal. For example, one private equity firm used AI to combine SEC filings with proprietary portfolio data to identify how specific regulatory changes might impact their holdings, enabling them to respond proactively with targeted strategies.
3. Deal Sourcing
AI can enhance deal sourcing by analyzing data from sources like SEC filings, press releases, LinkedIn, and PitchBook. For example, a PE firm used AI to scan LinkedIn for companies expanding their leadership teams, identifying early-stage startups.
AI can also add value by forecasting potential deals. A PE firm flagged a manufacturing company as a target acquisition after the AI identified liquidity constraints and leadership changes, enabling early outreach. Additionally, AI can map professional networks on platforms like LinkedIn to uncover hidden connections between intermediaries and target companies, revealing proprietary deal flow.
However, AI's utility is limited by incomplete data and the need for human judgment in assessing qualitative factors like market fit and leadership. While it accelerates research, deal sourcing still relies on a blend of technology and expertise.
4. Document Generation
AI-assisted reporting tools can streamline the creation of investment memos, pitch books, financial reports, and compliance documents by automating drafting and editing processes. Using natural language generation (NLG), these tools analyze structured data, such as financial metrics or market analysis, to produce cohesive narratives that follow the format and tone of past documents. For example, a private equity firm used AI to draft initial versions of quarterly portfolio updates, reducing drafting time by 46%, allowing analysts to focus on reviewing insights and strategic decision-making.
AI can also enhances language and style by suggesting improvements in clarity, tone, and precision. It adapts reports for specific audiences, such as crafting formal language for regulators or persuasive language for investors. A private debt fund, for instance, used AI to refine investor presentations, ensuring consistency across documents while tailoring tone to align with the firm's branding.
By automating labor-intensive tasks and enhancing the quality of reports, AI-assisted tools help teams focus on higher-value activities while improving the overall efficiency of reporting workflows.
4. Analysis
While AI holds significant promise for automating financial workflows, it faces critical limitations in analytical and modeling tasks. One major challenge is its lack of numerical precision and reliability when managing complex quantitative relationships. For instance, AI may misinterpret financial data tables or inconsistently apply formulas essential for accurate modeling, making it unsuitable for high-stakes tasks like financial forecasting or portfolio performance calculations without substantial human oversight.
Another limitation is the lack of contextual understanding in out-of-the-box AI models. Generic algorithms often struggle to account for industry-specific nuances, such as distinguishing between equity and debt risk metrics or integrating unique market dynamics into scenario modeling. This makes AI tools less dependable for tasks that require deep domain expertise and sophisticated judgment.
To address these limitations, AI systems must incorporate a robust "human-in-the-loop" framework, enabling professionals to guide the AI with intent and expertise. Additionally, integrating tools like calculators and code interpreters can improve the reliability of outputs by enhancing AI's ability to handle complex calculations and ensure accuracy in quantitative tasks. This collaborative approach bridges the gap between AI's automation capabilities and the precision required for financial analysis.
Towards Human-AI Symbiosis in PE and PD
Private equity and private debt firms face complex challenges where operational efficiency and sound judgment are essential. This research highlights how AI can streamline tasks like data aggregation and trend analysis but falls short in areas requiring nuanced judgment and relationship-driven deal sourcing.
The findings advocate for an AI-human symbiosis, with AI as a tool to enhance workflows, not replace expertise. A "human-in-the-loop" approach ensures AI handles repetitive tasks while professionals focus on strategic decisions. By leveraging AI for efficiency and humans for critical insights, firms can stay agile and competitive in a complex market.
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