The widening gap between PE firms that have modernized diligence and those that haven't

AI use in private equity is in full swing. But while many firms at the institutional level have meaningfully adopted new tools for underwriting, others are just beginning to pilot what an AI-enabled workflow could offer. This adoption gap is just beginning — but it’s one that will compound over time.

In this article, we’ll explore how a gap in AI-adoption can show up as firms face this wave of transformation head-on.

Where the gap shows up in deal outcomes

The asymmetry between leaders and laggards is concrete and measurable.

• Deal velocity in competitive processes. In a banker-led auction, the firm that reaches conviction first wins the LOI. AI-augmented diligence shortens the cycle from data room to defensible "yes" by days or weeks, depending on deal complexity. The laggard firm shows up to the second round, still working through the first-round materials.
• Entry discipline and the multiples paid. Faster diligence at the same depth means the firm can run more candidates through deal screening against tighter fit criteria. The deals that advance are the ones with the strongest underwriting case. The laggard firm, working through fewer candidates at the same depth, ends up with a weaker selection pool and either pays for marginal deals or passes on stronger ones it couldn't get to in time.
• Speed-to-conviction in proprietary processes. In a proprietary deal, the firm that brings a credible price and a defensible thesis to the table first wins the right to negotiate. The firm that needs three weeks to validate the financials and articulate the operational thesis isn't in the conversation by the time the owner signs an LOI with the faster bidder.

Where the gap shows up in fund economics

Deal-level outcomes accumulate into fund-level outcomes. Over a fund's deployment period, the gap compounds in two specific ways.

• Deployment pace and IRR drag. Capital that sits in dry powder beyond the optimal deployment window drags down the fund's IRR. Faster diligence cycles mean faster capital deployment, which means a higher proportion of fund capital working through its full hold period rather than sitting on the sidelines.
• Selection quality and exit multiples. The firm that ran deeper diligence on more candidates ended up with a stronger portfolio at the platform-selection layer. That selection quality compounds into exit-multiple quality. Funds underwritten on the new model show up at exit with a portfolio that justifies a tighter dispersion of outcomes. Funds underwritten on the old model show up with the same dispersion the industry has always had — some big wins, some flat exits, some write-downs.

LPs notice this. Pattern recognition across vintages is exactly what they're trying to underwrite when they evaluate a GP for future vintages.

Why the adoption gap compounds rather than closes

A key aspect of AI adoption is that the value of using modern tooling compounds the more deals a firm runs, while laggards get slower as the work gets more complex.

• Adopters get sharper with volume. Every transaction a firm runs through a vertical AI platform adds structured precedent to the firm's institutional knowledge corpus. Addback decisions, covenant precedents, sector comps, underwrite-vs-actual deltas — all of it trains the system for the next underwrite.
• Laggards get slower as data rooms get harder. Modern data rooms keep getting bigger and more complex. The manual workflow strains under that volume in a way the platform-based workflow doesn't.

A laggard firm that decides to catch up in a few years by adopting the same platform that AI leaders bought in 2025 is not actually catching up. They're starting at the front of a learning curve the leaders started two years ago, against a competitor whose precedent library has been compounding the entire time.

What separates AI leaders from laggards

The firms producing measurable returns share three habits — they buy vertical rather than general-purpose, they redirect the resources AI frees upstream into sourcing, and they run diligence to surface the operational thesis rather than confirm a price.

• They buy vertical tools. Horizontal LLMs are excellent general-purpose tools, but neither was built to read multi-tab Excel models, reconcile addbacks against source documents, or produce audit-traceable IC outputs. The best AI tools for private equity are vertically trained for the specific work — financial spreading, document analysis, source-linked memo generation. Leaders treat the distinction between horizontal and vertical AI as the most important capital allocation decision in their tech stack.
• They restructure sourcing around the new capacity. When the analytical layer stops being the bottleneck in diligence, the constraint moves upstream to deal sourcing. Leaders reweight headcount, BD posture, and sector coverage to take advantage of the additional throughput.
• They treat full-potential diligence as the default. A well-run diligence cycle is built around surfacing the firm’s operational thesis with enough specificity that they can execute on it immediately. AI makes that work possible at speed. The IC memo is where that thesis gets defended, and the standard for what belongs in a modern IC memo has shifted alongside the diligence cycle.

What this means for firms still on the fence

The first is that the cost of being a laggard is higher than it appears in any given quarter, because the gap is compounding rather than static. With that said, the firms still evaluating whether to commit still have time, though the window for catching up is narrowing fast.

Three practical implications follow:

• Evaluation criteria matter more than vendor selection. Most PE firms approach the AI question as a vendor RFP. The more useful exercise is an internal readiness audit — where in the workflow does the firm currently lose time, where is the analytical depth thinnest, and what specifically does the platform need to do to make a measurable difference? Firms that answer those questions first buy better.
• Audit-readiness is the gate. The output of any institutional-grade AI workflow has to be defensible at IC. That means full source-linking from claim through formula to source cell, and outputs that survive the kind of interrogation a partner brings to a marginal deal. Firms that buy on speed without verifying the auditability discover the gap later, in IC, on a deal that matters.
• Information compounding starts with your first deal. Every deal the firm runs through the new workflow adds structured precedent to its institutional knowledge corpus. Firms that delay by six months are six months behind in compounding, not just in capability.

AI adopters are extending their lead with each passing quarter. F2 is the infrastructure layer built for that work — vertical, audit-ready, designed for the specific reasoning private markets investors do.

Book a demo to see how F2 fits into your diligence workflow.