Where AI Actually Helps and Fails in M&A Legal Work

Aaron Binstock, Partner, Co-Head of Private Equity Practice at Cooley LLP

AI can now draft, review, and benchmark deal documents in a fraction of the time it used to take, but knowing when to trust the output is a different skill entirely.

Aaron Binstock, a partner at Cooley with nearly 20 years of transactional experience, has seen both sides of that tradeoff firsthand.

Where does AI actually save time on a deal, and where does it create false confidence? What happened when a client's AI-generated tax step chart was built on the wrong assumption? How does reverse prompting produce a better first draft than a single one-shot prompt? And what's changing about how junior lawyers build judgment, and how firms bill for their time?

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 pulls survival periods, caps, and baskets into one reference chart 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

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.

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The Buyer-Led M&A™ Summit is back

August 18th, free and virtual. We're releasing the State of AI in M&A 2026 report live at the event before it goes public. Benchmark your program, hear from practitioners across the industry, and leave with a clearer picture of where dealmaking is headed.

Register here: https://hubs.ly/Q04kBhzV0

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