AI can now produce first-pass deal documents, review thousands of contracts, and surface issues in a fraction of the time. The harder question is what happens when the output looks convincing, moves quickly through the workflow, and nobody catches that it was built on the wrong assumption.

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Aaron Binstock has seen both sides of that tradeoff in live M&A work. Some uses have saved teams hundreds of hours. Others have created false confidence around decisions with potentially significant financial consequences. The gap between those outcomes often comes down to how narrowly the tool is used, what context it is given, and whether an experienced practitioner knows where to challenge the result.

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As AI changes the speed of diligence, drafting, and review, it is also changing what clients are paying lawyers for. The real value is shifting away from producing the work and toward knowing what matters, what is wrong, and what the deal team should do next.

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What You'll Learn

• Where AI reliably speeds up NDA markups versus bespoke merger agreements
• How reverse prompting turns a mediocre AI output into a usable first draft
• The tax step chart mistake that nearly cost a client millions in consideration or tax
• How cross-deal benchmarking replaces a manual deal term study in minutes
• Why some clients and counterparties are opting out of AI entirely, and how firms track it
• What junior lawyer training looks like once document grinding stops teaching judgment
• Why AI can produce a report but still can't own the result

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If you're dealing with AI tools that sound confident but don't actually know M&A, DealPilot, powered by M&A Science experiential data, has guidance built from practitioners who've actually run the deal to help you catch what AI can't see coming.