The Deal Team's New Colleague: Using Artificial Intelligence to Run the M&A Transaction

Deal Sourcing and Target Screening

The transaction begins before the data room opens, and so does AI. Target identification has historically been a manual exercise built on referrals, broker listings, and spreadsheet screening. AI platforms have changed the mechanics: machine-learning models now ingest financial filings, market data, patent databases, and news feeds to surface high-fit targets against custom criteria, while natural language processing systems scan press releases, regulatory statements, and even social media for early signals that an owner may be exploring an exit. Deal teams using these tools report better hit rates from early-stage filtering and shorter cycles from first screen to first meeting. For corporate development teams running a programmatic acquisition strategy, AI-assisted screening is becoming the difference between seeing a target first and reading about the deal in a competitor's press release. Note: Target companies are also increasingly using AI tools to identify ways to improve their value and ensure that they are ready for the sale process.

Due Diligence: Where AI Earns Its Keep

Diligence is where AI's impact is most dramatic because diligence is fundamentally a document problem. A middle-market data room routinely holds thousands of documents – multiple years of financial statements, hundreds of customer and vendor contracts, employment agreements, IP schedules, insurance policies – and traditional review requires teams of associates cross-referencing all of it under a compressed timeline. Modern AI diligence platforms build upon existing machine learning models and can compress that work from weeks to hours. They extract change-of-control and anti-assignment clauses across the entire contract set, flag non-assignable agreements before they become closing surprises, reconcile financial anomalies, and surface unusual representation-and-warranty language for counsel's attention. AI tools can speed the drafting of due diligence memos. Virtual data room providers have embedded AI directly into their platforms to automate document categorization, redaction, and permissioning, and some now track reviewer engagement across the data room to show sellers, in real time, where a buyer's attention is concentrating.

The 2026 frontier is agentic workflow: AI systems that execute multi-step assignments rather than answering one question at a time. An agentic system can cross-reference the target's customer contracts against disclosed revenue concentrations, map every open-source component in a codebase against its license obligations, trace a patent family's chain of title through recorded assignments, or assemble a first-draft issues list organized by risk tier. None of this eliminates attorney review (a point we return to below), but it fundamentally changes what a lean deal team can cover. In an auction, the buyer that reaches conviction fastest usually wins, and AI is increasingly what makes that speed possible without sacrificing depth.

A point of caution: Generative AI can be confidently incorrect. It is strong at pattern recognition but it lacks critical reasoning. Generative AI also can struggle with completeness when prompts are overly complex or applied across large datasets. Where absolute certainty is required, such as preparing diligence schedules, the time saved through AI may be offset by the attorney review and verification needed to confirm the output.

Drafting and Negotiation Support

AI is also moving upstream into the documents themselves. Deal teams are using AI to generate first drafts of disclosure schedules directly from data room contents, to prepare ancillary documents from precedent, and to produce issue lists and markups benchmarked against market terms. On the negotiation side, AI tools can compare a counterparty's draft against a firm's precedent bank and against published deal-point studies, flagging where a proposed indemnity cap, survival period, or materiality scrape sits relative to market. Sellers preparing for a process can run their own contracts through the same tools the buyer will use, finding the change-of-control problems, the missing assignments, and the expired agreements before the buyer's diligence team does, when there is still time to cure them quietly.

Valuation, Modeling, and Integration Planning

Beyond the legal workstream, AI is accelerating the financial and operational side of the transaction. Models that once required weeks of analyst time, such as synergy estimates, customer-cohort analyses, working capital normalization, can now be built and stress-tested in days, with AI surfacing the anomalies in the target's numbers that deserve a management question. AI's value continues after signing. AI-assisted integration planning can help acquirers map overlapping systems, contracts, and vendor relationships across the combined business. That capability is especially useful for private equity platforms managing multiple simultaneous add-on integrations at once, where the volume of cross-referencing work makes AI particularly effective.

The Guardrails: Using AI Without Creating New Risk

Speed without discipline creates liability. Before deploying AI on a live transaction, every deal team should work through the following four issues:

1. Confidentiality and the Nondisclosure Agreement (NDA). Most confidentiality agreements were drafted before generative AI existed. Uploading a target's data room contents into a third-party AI tool may constitute disclosure to a third party in breach of the NDA, particularly if the vendor's terms permit customer inputs to be used to train or improve its models. Use enterprise-grade tools with contractual model-training carve-outs and SOC 2-level security, and address permitted AI use expressly in the NDA and the data room rules of engagement. Sellers should impose the same discipline on buyers.
    
2. Privilege. AI-assisted analyses prepared by counsel should carry the same protection as any other work product, but the mechanics matter. Prompts, outputs, and vendor-side logs may be discoverable if privilege is not deliberately preserved. Ask where the data resides, who at the vendor can access it, how long the vendor retains it, and whether the vendor has a credible process for preserving privilege, as more tools are failing buyers' security reviews on privilege-handling grounds.
    
3. Verification. AI systems still err confidently. A hallucinated contract term, a fabricated citation, or a missed exception can distort valuation or leave a party without recourse post-closing. AI can prepare the first pass, but qualified professionals must verify the analysis and own the conclusions. Build a written verification protocol into the workplan and document that human review occurred.
    
4. Your Confidential Information. The deal model, the negotiation strategy, and the client's confidences deserve the same protection you demand for the target's data. An AI use policy should be part of the deal team's standard workplan, identifying which tools may be used, what data may be uploaded, and what approvals are required.

Practical Takeaways

• Start before the deal does. Select and vet your AI toolset now, so the security review, vendor terms, and use protocol are settled before a live transaction imposes its timeline.
• Update form NDAs and engagement letters to address AI use.
• Train the deal team on what the tools do well and where they fall short.
• Measure the results. Firms getting the most from AI treat it as a workflow discipline, not a gadget.

AI will not replace deal judgment, and it will not negotiate the proper indemnity package given the specific deal risk. But the deal teams that harness it are covering more ground, moving faster to conviction, and winning processes against those that have not. In this market, that is the whole game. To learn more about how these developments may impact your business or transactions, please contact Bruce C. Doeg, Edward D. Lanquist, Jr., or a member of Baker Donelson's Artificial Intelligence Team.