AI Won’t Make Negotiators Faster—But It Will Make Them Better

Corporate deal-making has always run on a familiar cadence of deadline pressure, war room urgency, and dueling redlines. As generative artificial intelligence sweeps across the legal profession, many assume its value lies in optimizing deal mechanics—faster drafting, quicker diligence, and shorter meetings. The premise is that efficiency leads to better outcomes. But does it?

A contract can be reviewed in record time and still fall flat because the team missed deeper strategic signals such as the personality across the table, the unspoken priorities, or the tension hiding behind a routine objection. In practice, speed rarely is what carries a deal across the finish line. It’s clarity, preparation, and the ability to frame solutions that the other side can accept.

AI’s real role in negotiation is illumination: helping teams see the issues and how to tackle them. The next era of deal-making will be shaped by lawyers who think better.

Efficiency’s Ceiling

Efficiency is seductive because it feels measurable. If a model flags nonstandard terms or summarizes a 60-page agreement in seconds, that appears to be progress. From a mechanics standpoint, it is. But the efficiency ceiling is real: Refining a process doesn’t lead to a better outcome.

In countless transactions—mergers and acquisitions, commercial, technology licensing—the sticking points aren’t mechanical. They stem from how parties relate to each other and solve strategic issues.

People get anchored to positions they don’t fully understand. Teams talk past one another. A clause intended as risk management is perceived as distrust. None of these issues improve because a document was reviewed 20 minutes faster.

Efficiency doesn’t eliminate friction, but insight does. AI becomes transformative when it exposes the negotiation dynamics that actually determine outcomes.

AI’s Biggest Impacts

AI is best understood as a thought partner that helps negotiators prepare before anyone logs into the video call. The highest-impact uses fall into four categories.

Anticipating objections you haven’t considered. Models generate plausible counterarguments, letting negotiators pressure-test reasoning and confront blind spots early.

Understanding how your message will land. AI can surface emotional cues in prior communication—formality, defensiveness, passive resistance—that shape delivery.

Stress testing negotiation paths. Teams can simulate multiple strategies and see how each might play out with different counterpart personalities.

Rehearsing under pressure. Like athletes visualizing performance, negotiators can simulate difficult conversations before they occur.

None of this is about speed. It’s about reducing misalignment before it becomes a deal-threatening problem.

Real-World Example

A global logistics company is negotiating a critical software licensing agreement with a rapidly scaling AI vendor. The vendor’s technology is essential to automating the company’s freight optimization models, and the financial upside is enormous. But time isn’t on their side: Holiday shipping looms, and operations needs a signed agreement within weeks.

Everyone is moving quickly. Too quickly.

Inside the company, the deal team senses friction but can’t pinpoint it. The vendor’s counsel seems agreeable on calls but becomes rigid in redlines. Technical teams are aligned but frustrated by procurement’s risk questions. The negotiation feels like ships passing in the night.

Rather than push harder or faster, the company’s commercial counsel tries something different. She feeds an AI model prior correspondence, marketing language, meeting summaries, and the open issues list. She asks it to identify where misalignment is likely to emerge based on behavioral patterns rather than contract substance.

The model flags something the team missed: signs of “progressive concession fatigue.” In plain terms, the vendor feels it has already given more than it should have. The next concession, no matter how minor, could trigger a blow-up.

Armed with that insight, she adjusts her strategy. Instead of opening with the hardest issue, she acknowledges the progress both sides have made already. She reframes several points as clarifications tied to the vendor’s own architecture. Most importantly, she asks the vendor directly with a warm tone whether the timeline is distorting their perception of risk.

That pivot in strategy causes the call to land differently. Defenses drop. The vendor proposes a new risk-sharing solution, and the deal closes within the needed timeframe.

No drafting speed-up could have produced that shift and outcome. Preparation and insight made it happen, and AI helped uncover the hidden issues.

Building Trust

At its best, negotiation is the process of identifying and aligning worldviews. Deals that break after signing rarely do so because a clause was drafted imperfectly. They break because expectations never aligned—or one party felt unheard.

AI helps negotiators avoid that outcome. By exposing misalignment early, it prevents small misunderstandings from hardening into mistrust. When used thoughtfully, it enhances the most human aspect of deal-making: empathy.

Empathy is understanding why the other side perceives risk the way they do and crafting a solution that respects their perspective without sacrificing your own. AI helps lawyers do that with more consistency, less ego, and fewer surprises. Parties return for the next deal because of the quality of the outcome and how effectively it was achieved.

The Real Advantage

Lawyers who treat AI as an efficiency hack will get short-term time savings. Those who treat AI as a strategic amplifier will gain something far more valuable: repeatable insights that drive better outcomes.

AI can’t replace human judgment, but it can refine, challenge, and stretch it in ways even the most experienced negotiator couldn’t do alone. The lawyers who adopt AI with that mindset will negotiate more wisely and achieve better outcomes. In the high-stakes world of modern deal-making, wisdom is what wins.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Justin S. Daniels is a corporate mergers and acquisitions and technology transactions shareholder at Baker Donelson.

Write for Us: Author Guidelines