Private Equity Is Getting AI All Wrong

This article was written by Lalit Lal, Co-founder & CTO of Keye, the first AI platform built exclusively for private equity. Keye understands the data, performs real analyses, and mirrors the workflows of top-performing deal teams. For more information, visit https://www.keye.co/

The industry is asking the wrong questions about AI. While firms obsess over whether algorithms can pick better deals, the baseline opportunity lies in systematizing institutional knowledge that drives successful investing but has never been properly operationalized. That is where the real opportunity begins.

Stop Worshiping Models and Start Liberating Workflows

The real constraint is not model sophistication; it is operational infrastructure. Analysts spend hours pulling data from confidential information memoranda, reconciling conflicting metrics, and manually hunting for comparable deals. This prevents teams from engaging in pattern recognition and strategic thinking. According to recent research, 82% of employers say lack of industry standards is slowing AI adoption. The bottleneck is organizational, not technical.

Smart firms focus on foundation work first. They build systems that transform messy deal flow into clean, comparable datasets. This work is not glamorous, but it enables everything else that matters.

From Deal Drudgery to Pattern Recognition

Current manual processes break down under today's deal volume. Partners chase teasers through email chains. Staff re-enter data across multiple systems. Everyone relies on individual memory for pattern recognition. Global buyout deal count in 2024 reached approximately 3,000 transactions; even mid-market teams are overwhelmed by this volume.

AI can automate this operational burden. Systems can auto-classify companies by sector, flag unusual metrics, cluster opportunities, and rank prospects against investment criteria. However, technology is only as effective as the underlying data quality and clarity of investment frameworks.

Pattern recognition, benchmarking against historical deals, and understanding a fund's investment fingerprint are all automatable capabilities. These same approaches extend naturally to automated diligence on live deals. Firms making meaningful progress invested early in data standardization and clear decision frameworks.

Make Intuition Machine-Readable

Every successful firm has institutional wisdom trapped in partner expertise. This includes recognizing sustainable competitive advantages, understanding which business models scale across geographies, and spotting early warning signs of management team problems. This knowledge drives the best decisions but cannot easily transfer to new hires.

The opportunity lies in making this knowledge explicit and systematic. Firms should develop taxonomies for deal characteristics, create scoring frameworks for success factors that actually matter, and build feedback loops capturing lessons from both winners and losers. Current data shows that 67% of investment managers now use alternative data sources; however, this data only provides value when integrated with existing decision processes.

From Data to Decision Velocity

AI delivers the biggest returns when implemented across the entire investment process; from initial screening through due diligence to post-acquisition value creation. This requires connecting historically isolated systems and creating feedback loops that improve decision-making over time.

The pressure for this integration is intense. Private equity portfolios held $3.6 trillion of unrealized value by year-end 2024. This environment rewards firms capable of making faster, more informed decisions on both new investments and portfolio management.

Achieving this level of integration requires real upfront investment and organizational change. However, it creates sustainable advantages that compound over multiple investment cycles.

This Is Your Moneyball Moment

This transformation is not about replacing human judgment with black-box algorithms. It is about systematizing successful approaches for consistent application at greater scale. The goal is building systems that learn from past decisions, predict deal fit, and surface relevant comparisons. The objective is giving your best investors superpowers, not replacing them.

The industry is ready for this shift. Recent surveys show 91% of investment managers are using AI or planning to use it in strategy and research. The question is not whether this transformation will happen, but whether firms can set systems before it's too late.

Success requires unusual clarity about what your firm actually does well, systematic capture of institutional knowledge, and disciplined implementation of supporting technology. Firms that execute this well create genuinely difficult-to-replicate competitive advantages.

The transformation is already underway. The only question is whether your firm will help lead it or spend years scrambling to catch up.