This article was originally published on Modern Marketing Today on October 13, 2026. It explores why general-purpose AI tools can make proprietary deal sourcing harder by directing investors toward the same publicly visible companies. The article examines how differentiated private-market data and behavioral signals can help investors uncover less-visible opportunities and identify companies preparing for a sale.
For decades, private-market investors gained an edge the old-fashioned way: through regional relationships, sector specialists, industry insiders and word-of-mouth networks that rarely showed up in any searchable system. That advantage is eroding fast. A recent Datasite survey of global dealmakers found that 96 percent now use or pilot AI somewhere in their sourcing and screening process.
That should make proprietary deal flow easier to find. Instead, it is making true proprietary access harder to defend.
The reason is straightforward: when everyone uses the same tools on the same data, everyone starts to see the same market.
Generalist AI tools are powerful, but they are still largely bound by publicly available information. They can help a team pressure-test an investment thesis or scan a sector quickly. But they surface companies only when those companies have enough digital footprint to index. When every firm points AI at the same open web, every firm gets the same familiar names back. The result is not a wider hunting ground. It is more competition around the same visible targets.
Outreach has become cheaper at the same time. A tailored note that once took 20 minutes can now be drafted in seconds. Founders in narrow, attractive markets are receiving more polished pitches from more firms, all competing for a smaller set of visible opportunities.
The best targets often look nothing like that. They are founder-owned, thinly capitalized, and active in fragmented industries with no obvious category leader. They may have strong cash flow, loyal customers, and low public visibility. That makes them attractive to investors, yet nearly invisible to generalist AI. Even the most sophisticated model cannot index information that was never public in the first place.
Real proprietary deal flow must come from signals generalist tools cannot reach.
Timing Is Now as Important as Access.
A strong market map still leaves one critical question unanswered: is this company preparing to sell, or does it merely fit the profile of a company that could sell? Common signals, such as an aging founder or a sponsor beyond a typical hold period, can identify who might sell someday. They reveal little about who is moving now.
The run-up to a sale often begins long before a banker is formally engaged. It can start a year or more in advance, through small operational decisions that rarely make headlines: hiring a controller to clean up the books, meeting a wealth manager for the first time, or attending an industry conference that historically was not on the calendar. None of these moves warrant a press release. Together, they can point to a company preparing for a transaction.
That is where behavioral intelligence changes the sourcing equation. It closes the gap between companies that are merely ‘sellable’ and those that are actively moving toward a sale. Rather than relying on one indicator, these models identify combinations of signals that do not typically appear in a company’s public profile: hiring tied to deal preparation, such as adding a controller or first outside CFO; rising engagement with advisors, bankers or wealth managers; research activity around comparable transactions; and changes in how a company presents itself externally, from conference attendance to public messaging.
Individually, these signals are imperfect. A new finance hire or an adviser conversation can happen for plenty of reasons. The insight comes from watching patterns emerge across a broad universe of companies over time, then comparing those patterns with the pre-sale activity that preceded past transactions.
Private-market data platforms are starting to build this kind of signal tracking into their products. They layer behavioral signals on top of traditional firmographic and ownership data, giving deal teams a more disciplined way to identify companies that deserve outreach. Teams can approximate parts of this process manually by tracking job postings, executive moves on LinkedIn, and adviser or banker announcements. But systematic coverage across a meaningful universe of companies requires scale, and scale is exactly what these platforms are built to provide.
At its best, behavioral intelligence gives deal teams a reason to start a credible conversation while a founder is still privately weighing options, and before a banker’s mandate turns that opportunity into a crowded process.
The Winning Workflow Will Combine AI With Differentiated Data.
The firms adapting fastest are not abandoning generalist AI. They are putting it in the right role. Generalist tools can sharpen market research, test an investment thesis, and accelerate early analysis. But they must sit inside a broader workflow grounded in independent, verified private-market data: ownership records, offline financial detail, verified contacts and behavioral signals that show which companies are genuinely close to a sale. AI can improve the speed and quality of the thinking. The data underneath determines whether the resulting pipeline is proprietary, or simply faster to replicate.
Twenty-four percent of dealmakers in the Datasite survey say AI has already helped them close a deal they otherwise would have missed entirely. That is meaningful progress. It also underscores how early this shift remains. As AI becomes standard across private markets, the edge will not come from using AI alone. It will come from combining AI with behavioral intelligence that identifies which companies are actually likely to transact—not just which ones match a screen. That is the difference between a tool that describes the market and one that helps investors get there first.
The author, Andrew Bocskocsky, is Chief Product Officer at Datasite.