The success of private equity deals largely hinges on a quality-of-earnings analysis – the deep check on whether a target’s reported profits are real. Industrialized around Excel workflows in the 1990s, it hasn’t fundamentally changed since. Of the usual six to eight weeks spent on financial due diligence, half of that time still goes to cleaning data instead of the actual analysis and client conversations.
Speed is only part of the problem. A CPA team working manually reviews a sample of transactions and infers the rest, so anything below the materiality threshold goes unexamined. The fund inherits a gap between what was reviewed and what was real – and that gap is exactly where overstated earnings hide.
Every overstated dollar of EBITDA that slips through gets multiplied by the deal multiple. At a high multiple, a single bad adjustment adds millions to the purchase price. That math is why Nikita Komarov built Dobs AI, an end-to-end financial due diligence platform designed to close that gap. He talked with us about how automation can make the FDD process faster, more precise, and free up senior specialists to focus on analysis and strategy instead of data cleaning.
The Overpayment Problem
FDD protects the PE fund from paying for earnings that aren’t really there. The ways those earnings get inflated are well known: management adjusts EBITDA upward, sellers propose working capital normalizations that flatter the cash conversion cycle, and non-recurring items get buried inside thousands of journal entries.
An analyst team works through those entries one transaction at a time, sampling and spot-checking until the clock runs out. Whatever falls below materiality thresholds goes unexamined, and that’s precisely where overstated earnings tend to hide. So a fund ends up paying a multiple without knowing the full picture.
Purpose-built financial AI removes that constraint by reading the full population – every transaction, journal entry, or reconciliation – flagging anomalies and surfacing correlations a sample would miss. The fund sees earnings quality as it actually is
That changes the math on every deal. A single misjudged EBITDA adjustment carried across a 10x multiple becomes a seven-figure overpayment, and catching even one of those pays for the diligence many times over.
Where The Weeks Go
The first three weeks of quality of earnings today are structurally repetitive work that requires no senior judgment. Pulling the general ledger out of a legacy accounting system, tying the trial balance to the financials, and identifying non-recurring items – AI handles it better than humans. It’s faster, more consistent, and processes thousands of transactions, producing traceable output.
The interpretation layer stays human – that’s where the true value lies. What does a 4% revenue decline in Q2 mean for this specific buyer’s thesis? Is customer concentration a risk or a feature for this platform? These questions require context, judgment, and an understanding of the deal.
Automation gives senior practitioners the clean data and the time to do that work. Both the fund and the CPA team benefit from the speed. In an auction, a CPA firm which can confirm earnings quality quickly holds an advantage over the one still waiting on a spreadsheet.
The Bottleneck
What holds decision-makers back is a willingness to redraw the workflow. Most are simply waiting – waiting for the technology to mature, for someone else to make the first mistakes, for a clearer signal from the market. To people who evaluate risk for a living, that caution feels prudent, and that is exactly where the danger lies.
The technology is maturing at a pace that makes waiting a risk of its own. What wasn’t possible three months ago is a standard practice today, and a firm that sits still can find the gap too wide to close by the time it decides to move. For a PE fund working with a diligence provider, that position toward automation is worth considering.
The practitioners who resist usually raise two objections. The first is that AI introduces errors into deliverables that have to withstand legal scrutiny and feed directly into deal terms and purchase price mechanics. The second is a genuine unease about what automation does to junior training and to the profession over time. Both deserve to be addressed directly
The Benefit of Automation
The accuracy objection is easier to address, since purpose-built financial AI now surpasses 99% accuracy on the reconciliation, data extraction, and transaction matching. It’s like replacing an abacus with a calculator, so the firms still working by hand are just absorbing the error rate they claim to fear.
But there’s more to it. A system capable of reading the full transaction population surfaces things a manual engagement never reaches: the customer cohorts that actually carry the margin, the revenue that gets pulled forward at quarter-end to hit a number, the vendor concentrations that become leverage in a post-close renegotiation.
These insights go beyond the current state of the finances, showing a fund where business opportunities lie. The same diligence that protects the entry price starts informing how the fund can run the asset for the next five years.
The second objection is trickier, because it’s personal. Caution is fair, and the worry about what automation does to the profession is real. But for a fund, what matters is how a diligence provider handles that question. A firm that is clear about where humans still matter and open about how it uses the technology is easier to trust than one that avoids the subject.
The Timing
Twelve months is about how long it takes for the gap between an automated team and a manual one to become impossible to close. By the time the latter one decides to move, the automated team will have a year of refined process behind it, and no hiring push or software purchase can buy that back.
By 2030, FDD will look nothing like it does today. The Excel workflows will be gone, and the value delivered to a fund will look different. For a PE fund, the question is no longer whether AI belongs in diligence, but whether your provider is already using it.
