Here is a number that should bother every private equity firm: the average firm sees only about 18% of the relevant deals in its universe. More than four-fifths of the opportunities a firm could credibly pursue never surface in front of the people who would act on them. The deal you would have loved to lead often closes somewhere else, and you never knew it existed.
AI is now forcing that gap into the open. And in doing so it is revealing something important: the bottleneck in sourcing was never analyst horsepower. It was reach and memory.
AI has moved to the front of the funnel
For most of the last two years, AI in dealmaking meant diligence — summarizing a data room, reading contracts faster. That has changed. Deloitte's 2025 study found that 86% of corporate and PE leaders have integrated generative AI into their M&A workflows, two-thirds of them only in the past year. Among adopters, 40% now apply it to strategy and market assessment and 35% to target identification and screening. The work has moved upstream, into origination.
Private equity is leaning in harder than anyone. By Deloitte's count, 88% of PE firms have put a million dollars or more behind generative AI for their deal teams, and the great majority expect measurable return within one to three years.
The field is still wide open
It would be a mistake to read that as "too late." Bain reports that while more than 60% of PE firms now use at least one AI tool for sourcing, screening, or diligence, only about a third of even the most active acquirers use generative AI in M&A today, and more than half of all companies expect to deploy it by 2027. The advantage is still there to be taken. Bain's warning is the sharp part: late followers "will be outbid for good deals and find themselves staying too long in processes for bad deals."
The moat is not the model. It is proprietary memory.
This is where most firms will misread the moment. They will buy a screening model, point it at the public market, and discover that everyone else bought the same one. A model anyone can license is not an edge. The durable advantage is the data only you have: your relationship history, your past diligence, the operator you backed twice, the founder who said "not yet" eighteen months ago and is ready now. The edge is turning that history into a system that surfaces the right opportunity at the right moment, before it ever reaches a banker's outbound list.
The clearest proof point is EQT's Motherbrain, which has fully sourced somewhere between nine and fifteen investments and stack-ranks opportunities with interpretable scoring across the deal lifecycle. The lesson people take from it is usually wrong. Motherbrain is not impressive because of the algorithm; it is impressive because EQT spent years turning its own activity into a proprietary data asset the algorithm could stand on. The asset is the moat. The model is a tenant.
What separates the firms winning sourcing in 2026
It is not who has the most analysts or the newest model. It is who has a memory they can trust and act on quickly. PitchBook notes that at some large sponsors, a third to forty percent of investment-committee discussion now centers on AI's impact on a target's business — a sign of how fast the analytical bar is rising. The firms pulling ahead are the ones who made their own history searchable, current, and reachable in the moment a thesis-fit company appears, instead of leaving it scattered across inboxes and a CRM no one trusts.
A deal brain you can trust
Sourcing is, in the end, a memory problem wearing a search problem's clothes. The firms that close the 18% gap will be the ones that build a deal brain: a living record of every relationship, conversation, and prior process, with evidence behind every signal, that surfaces what matters before the window closes. That is what we are building at Arvya — not a smarter guesser pointed at the open market, but a trustworthy memory of your own deals that helps you see the ones you would otherwise have missed.