While most private equity firms are busy deploying artificial intelligence across their portfolio companies, they're overlooking a massive opportunity sitting right under their noses: their own operations. InvestorFlow, a platform specifically designed for general partners, is betting that the real AI revolution in private equity will transform how firms source deals, raise capital, and execute their core strategies. The numbers are compelling: one global PE firm using InvestorFlow's AI capabilities uncovered 8,500 unique metrics from interactions with 2,500 companies, generated a 15x increase in actionable insights, and eliminated nearly five months of manual work, while a PEI Top 100 firm achieved a 7x increase in deal flow and $750K in cost savings.

Rather than adding another dashboard to an already complex tech stack, InvestorFlow's AI works quietly in the background, converting the "noise" of unstructured data—emails, meeting notes, investor communications—into real-time insights that flow directly into existing workflows. We sat down with Chris Cummings, Chief Strategy Officer at InvestorFlow, to discuss why GPs themselves represent the next frontier for AI in private equity, how the platform's recent enhancements are delivering measurable results, and why firms that remain cautious about AI deployment may be taking the bigger risk by standing still. 

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VMblog: Let's start with the big picture. AI is being applied across portfolio companies and back-office functions, but InvestorFlow is focused on the GP itself. Why?

Chris Cummings: Exactly. Every private equity firm has a playbook-the best practices they deploy across portfolio companies to grow revenue, boost EBITDA, and cut costs. So it makes sense they'd bring AI into that playbook to accelerate results. But we saw a bigger, more immediate opportunity: improve how firms themselves operate-how they source, target, and execute. Fundraising and dealmaking are the lifeblood of GPs, yet many still rely on spreadsheets, fragmented workflows, and underused systems. We're applying AI directly to these core functions, helping firms harness their proprietary data to move faster, make better decisions, and close more deals.

VMblog: Your recent announcement highlighted expanded AI capabilities. What's new, and why does it matter right now?

Cummings: This update is based on real feedback from early adopters-what's working, what's missing, and where firms need more leverage. We've expanded our AI to extract KPIs from unstructured communications-emails, meeting notes-at scale, combine that with third-party data, and surface it at the moment of action. One firm uncovered 8,500 unique metrics from interactions with 2,500 companies. That intelligence now flows directly into workflows, enabling fundraisers and deal teams to target new investors, increase deal velocity, and deepen relationships. It's no longer about static reports or siloed data-it's about real-time, embedded insights that match how teams actually work.

VMblog: You've cited results like 15x more actionable insights and 18 weeks of manual effort saved. What's driving those numbers?

Cummings: The key is unlocking unstructured data. Every firm has it-emails, meeting recaps, investor notes, internal updates-but very little of it gets captured in a usable form. Manually entering it into a CRM is a productivity tax-and a revenue one. InvestorFlow AI automatically converts that noise into signal. One global PE firm saw a 15x increase in actionable insights: live LP engagement cues, in-quarter deal signals they would've otherwise missed. It also eliminated nearly five months of manual correlation work. These aren't theoretical gains-they're happening right now, in live production.

VMblog: What makes your AI approach different from some of the more "flashy" tools we're seeing?

Cummings: Everyone's using AI in more parts of life-but in private equity, the appetite isn't for shiny new tools. Our clients don't want another chatbot or dashboard that adds complexity, requires a rollout plan, and demands new behavior. InvestorFlow AI works quietly in the background, inside the systems firms already use. It's not about replacing people-it's about augmenting them with timely, meaningful insights. When a deal team gets a nudge about a sourcing signal they hadn't seen, or a fundraiser spots LP interest mid-quarter-that's real competitive edge. And it doesn't require anyone to change how they work.

VMblog: You mention "turning noise into intelligence." Can you give a concrete example?

Cummings: Take fundraising. When it's time to raise a new fund, IR teams often spend weeks assembling a target list of institutions and high-net-worth individuals. One top-tier firm used InvestorFlow to do this instantly. With a click, they generated a prioritized list based on every historical touchpoint-who showed interest in which fund, what KPIs they needed, and which partner had the best relationship. That context turned a manual, weeks-long process into a strategic jumpstart. In a crowded market, that kind of speed and precision can be the difference between a signed commitment and a missed one.

VMblog: Let's talk about West Monroe. What did their deployment look like?

Cummings: West Monroe is a strategic implementation partner of ours with deep experience in private markets. They worked with a PEI Top 100 firm whose legacy CRM was holding them back. The firm needed something purpose-built and scalable for private equity. After evaluating several options, they selected InvestorFlow. West Monroe delivered a phased rollout-on time and under budget. The impact? A 7x increase in top-of-funnel deal flow, a 20x boost in operational efficiency, and $750K in savings by moving off their incumbent system. What really stood out, though, was how seamlessly AI became part of their day-to-day decision-making.

VMblog: Many firms are still cautious when it comes to AI. What would you say to them?

Cummings: That caution makes sense-this is proprietary, highly sensitive data. But the greater risk now is standing still. It's common sense to validate the quality and accuracy of AI outputs. But what we've seen is that the more data firms expose to AI, the better the insights-and the better the outcomes, which drives more usage. You don't need a moonshot to start seeing results. Just begin where your teams already work. Let AI surface insights, test them, and scale from there. If your experience mirrors our clients', your teams will be the ones pushing to go faster. In a year where every basis point matters, AI in core workflows isn't hype-it's a margin driver.

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