How fund managers are already using AI

AI has moved from hype to implementation, and for fund managers, the tipping point is arriving fast. While only 2% of PE firms expect to see significant AI-driven value in 2025, a staggering 93% of PE leaders expect moderate to substantial AI benefits within three to five years. And while Marc Andreessen may envision venture capital as "one of the last remaining fields that people are actually doing," he also admits that AI is making inroads in due diligence and other areas, and may eventually exceed human capability to pick the best deal.

For fund managers looking to sharpen their competitive edge, the time to evaluate and deploy AI tools is now.

Finding promising opportunities early is essential. AI excels at parsing huge volumes of data—everything from anonymized credit card transactions to satellite imagery—to detect early trends that may signal an attractive investment. As Johnathan Balkin, Founder & Senior Partner of Alpha Alternatives, said in a recent episode of The Distribution by Juniper Square, "A person putting together an investment memo only has the brain capacity to handle a certain number of data sources. But if you can leverage AI, you can look at 5,000 companies. You can look at a data set of a million different things and have AI run thousands of scenarios for you.”

For instance, one PE firm used AI to detect rising spend and buyer counts in the houseplant category by analyzing anonymized card data. Cross-referencing that with web traffic metrics, they identified an emerging e-commerce leader in the space and ultimately acquired a stake.

Another example comes from EQT Ventures' proprietary AI platform, Motherbrain, which uses historical due diligence data and external signals to identify promising startups. To date, the tool has sourced 15 deals, including investments in Peakon, Netlify, and AnyDesk—many of which the firm says would have been missed otherwise.

In another case reported by Forbes, a PE firm used generative AI to automate investment memo creation, saving up to 80% of senior investment staff's time and reallocating resources to strategic evaluation.

As Balkin noted, investment deal professionals are some of the most expensive resources in the firm—they need to be focused on finding great deals, not worrying about the prose in a 20-page investment memo. But there's a catch: AI is only as good as the data it works with and the questions expert humans supply. “The model has to be trained like an analyst coming out of college; it needs to be trained as to what a good memo looks like," he recommended. “AI is an engine that looks at examples and finds patterns. You have to feed the beast so it knows what information to use.”

Traditionally a slow, manual process, due diligence is a prime candidate for AI optimization for many fund managers. Algorithms can process enormous volumes of data, including financials, patents, clinical trials, and customer sentiment—all in a fraction of the time it would take a human team. One VC firm evaluating a pharma startup deployed AI tools to review patent databases, analyze clinical trial outcomes, and gauge public perception via media and social platforms. This level of breadth and speed would have been nearly impossible using conventional methods.

Generative AI also enables real-time scenario testing. In a recent Bain & Company case, a diligence team built prototypes using GPT-4 to evaluate whether an IT services company's proprietary AI tool could withstand competition from more widely available large language models. The target's tool was marketed as a differentiator in a highly technical vertical, but the prototypes outperformed it in key tasks. Armed with this insight, the firm declined to invest.

When it comes to risk management, AI tools can evaluate macroeconomic indicators, supply chain vulnerabilities, ESG factors, and cyber risks faster and more comprehensively than ever before. In fact, an ACA survey from November 2024 found that compliance and risk management was the third most popular use case among those surveyed. Carlo di Florio, President of ACA Group and a member of the Juniper Square Private Markets Regulatory Council, pointed out that fund managers can leverage AI with their compliance program so teams “can do more with less and be more efficient.”

According to a USPEC report, investment firms are already using AI to conduct forward-looking assessments that flag both latent threats and opportunities. Generative AI can simulate scenarios and stress-test assumptions, helping managers make more informed go/no-go decisions.

Once an investment is made, AI can become a force multiplier. VC and PE firms are using it to help portfolio companies optimize operations, from staffing and recruiting to marketing and product development. For instance, one VC firm developed a proprietary AI-powered talent sourcing tool. By aggregating data from GitHub, Google Scholar, Quora, Kaggle, and LinkedIn, the firm helped its portfolio companies in the clean energy sector recruit rare technical talent with experience in renewable energy and regional policy compliance.

On the reporting side, AI holds the potential to automate LP communications, generate capital account statements, and streamline compliance documentation—all while improving accuracy and turnaround time.

While adoption is still in the early stages, the direction is clear: firms that build strong data foundations and begin experimenting with AI now will be better positioned to source, evaluate, and manage deals with greater precision.

For middle-market and emerging managers, especially, this is an opportunity to leapfrog larger firms still burdened by legacy systems. By pairing clean data with thoughtful AI applications, tomorrow’s outperformers will win not just due to intuition and hustle, but also smarter, data-informed decision-making.

But AI's promise only works if your data is clean, structured, and centralized. That's why digitization is the essential first step.

Fund managers using Juniper Square are already ahead. By consolidating general ledger data, investor records, and performance metrics in a single source of truth, Juniper Square provides the infrastructure necessary for AI-driven insights. It also enables seamless integration with the next generation of AI tools, which require large, high-quality datasets to deliver reliable results.

As Juniper Square CEO Alex Robinson recently wrote on LinkedIn, "You can't apply AI until you've mastered the boring fundamentals of structured data."