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AI has officially entered the deal room. But what it’s doing there depends on where you are in the deal.
Datasite and FT Longitude asked 1,000 senior dealmakers how they’re actually using AI across M&A, from the first look at an opportunity to the final board report.
Follow the deal from start to finish to see where AI is making its mark and where humans are still firmly in the driver’s seat.

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Your Results
At this initial stage of the deal, 34% of dealmakers are using AI regularly or have fully embedded it. Some 44% say that using AI gives them the ability to better assess and compare opportunities, which is a crucial part of this stage of the deal.

This stage is where AI starts to have a real impact: 43% of dealmakers are using it regularly or have fully embedded it. Sourcing and screening involve a significant amount of research, reading, and checking against set criteria – tasks that AI excels at.
AI is transforming the way dealmakers market deals, with 39% using it regularly or having the technology fully embedded at this stage. More than half (59%) say that AI is improving their ability to address buyer and seller questions at scale.
Almost all dealmakers (92%) are using or exploring AI to support deal preparation, which is one of the most labor-intensive and analytical stages. Ultimately, this is expected to improve decision-making quality.
Due diligence is where AI is having the biggest impact. Half of dealmakers are using it regularly or have fully embedded it at this stage of the deal, and only 4% don’t use it at all. This is the stage of the deal journey where AI provides the most ROI.
An AI-enabled deal is only as good as a dealmaker’s ability to close it. This stage is relationship-based: it requires interaction and empathy. So it’s unsurprising that AI is playing a smaller role: 31% of dealmakers don’t use AI here at all.
About a quarter (27%) of dealmakers aren’t using AI at this final stage of dealmaking, perhaps because they think the formal parts of a deal are best handled by humans – but they’re in the minority.
Accuracy, security, and control are the baseline.
Outputs need sources, review, and a clear path back to the evidence.
AI should reduce manual work without adding new risk.
Better decisions still need human ownership.