Offit Kurman Blogs

Artificial intelligence (AI) is rapidly becoming a larger part of mergers and acquisitions (M&A). For a business owner preparing to sell, AI tools can organize information, accelerate document review, and reduce some of the burden of a demanding diligence process. While those capabilities are certainly meaningful, they should not be confused with deal strategy. AI can help prepare a seller for a transaction, but it lacks the ability to determine how to position the business, preserve the negotiated economics, or control liability after closing.

Selling a business is much more involved than just collecting documents and answering questions. It is a negotiated allocation of value and risk. The seller may begin with an attractive headline price, but the amount ultimately retained can depend on dozens of provisions that require context, judgment, and a clear understanding of the seller's priorities. These are all things AI cannot understand.

When used thoughtfully, AI can be helpful early in the transaction process. It may assist in generating a diligence checklist, classifying documents for a virtual data room, reviewing contracts for recurring provisions, and preparing summaries for the deal team. For a seller looking at years of agreements, corporate records, employment materials, and financial information, that organizational support can be invaluable, saving time and helping identify areas that deserve closer attention.

But a well-organized data room is not the same as a well-positioned business. The critical question is not merely, “What information do we have?” It is, “How do we present this company in a way that supports its value while addressing risk before the buyer uses it as leverage?” Experienced deal counsel can strategically determine which issues should be resolved before going to market, how diligence should be sequenced, and how disclosures should be framed accurately without creating unnecessary exposure.

The headline price is only the beginning

Every seller wants the highest possible purchase price. But the stated price in a letter of intent or purchase agreement does not always equal what the seller receives at closing or what will remain after the deal is complete. Diligence findings can trigger price reductions, working capital adjustments can move significant dollars after closing, earnouts can make a portion of the sale dependent on future performance and buyer-controlled decisions, and escrows and holdbacks can delay access to proceeds.

While AI may be able to flag these provisions or compare them with language found in other agreements, it is critical to have an understanding as to how the provisions interact and how they affect this seller's actual economics. A working capital definition that appears ordinary may exclude an item that is central to the business. An earnout metric may sound objective but become difficult to achieve after the buyer integrates the company. A seemingly modest indemnity can become costly if it is paired with broad representations, a long survival period, and an inadequate liability cap.

Experienced counsel is trained to look beyond whether a term is simply present. They evaluate how it operates, where it shifts leverage, and whether it is consistent with the commercial bargain the seller believes it made.

AI cannot negotiate post-closing liability

For many sellers, the provisions governing post-closing exposure are among the most consequential parts of the transaction. The sale price loses some of its meaning if too much remains subject to claims, repayment obligations, or disputes long after closing. Controlling that exposure requires negotiation, not just identifying standard clauses.

Deal counsel works to narrow representations and warranties, limit indemnification obligations, reduce escrow or holdback amounts, establish appropriate liability caps and baskets, define exclusions carefully, and shorten survival periods where the circumstances support it. Counsel also considers how those protections fit together. Winning a lower cap may offer little comfort if a broad exception effectively swallows it. A shorter survival period may not solve the problem if the most significant representations remain outside that limit.

This is where experience directly impacts the economics of a deal. A lawyer who has seen claims arise under similar language can anticipate how a provision may be used when the parties no longer share the optimism of signing day. AI analyzes text. It does not advocate for the seller, read the negotiating dynamics, or make the tradeoffs required to bring the parties to agreement.

A technically correct answer may still be the wrong deal decision

M&A negotiations are filled with decisions that do not have a single correct answer. Should the seller resist a buyer's request or conserve leverage for a more important issue? Should a potential concern be disclosed now, investigated further, or addressed through a specific contractual solution? Is a provision worth extending the negotiation, or is accepting it the better choice given the buyer, the timing, and the seller's objectives?

Coming to the right answer requires understanding context. It may turn on the strength of the buyer's alternatives, the seller's tolerance for delay, the likelihood that the issue will create real exposure, and the relative value of other concessions under discussion. It also requires an understanding of the personalities at the table and how aggressively a point can be pressed without jeopardizing the deal. That judgment is built through experience and cannot be generated from contract language alone.

Overreliance on AI can create new risks

The efficiency of AI can create a false sense of completeness. A seller may assume that an AI-generated contract summary captured every material obligation or that an automated diligence review identified every inconsistency. But incomplete context, ambiguous drafting, poor source documents, or a seemingly minor error can lead to inaccurate disclosures, missed consent requirements, incomplete diligence responses, and post-closing disputes. Confidentiality, data security and the handling of privileged information also require careful attention when AI tools are used in a transaction.

The responsibility for the deal lies with the parties and their advisors, not the technology. They remain accountable for the accuracy of disclosures, the completeness of responses, and the meaning of the documents they sign. AI output should therefore be treated as a work product to validate, never something to accept without review.

Use AI to support the deal team, not substitute for it

Sellers should take advantage of AI tools that make transactions more efficient and allow for better organization, faster review, and earlier identification of potential issues. But the strongest approach is to use those tools under the direction of experienced advisors who understand what matters, can test the output, and know how to convert information into strategy.