AI-Powered Due Diligence for M&A Transactions

Overview

M&A due diligence requires reviewing hundreds or thousands of documents under tight deadlines. Harvey AI enables legal teams to conduct comprehensive due diligence faster — extracting key provisions, identifying risks, and generating summary reports across massive document sets.

Steps

1. Organize the data room: Structure documents by category (corporate, IP, employment, litigation, real estate, regulatory). Harvey works best with organized inputs.
2. Define your diligence checklist: Upload or configure your standard DD checklist — the specific provisions, risks, and data points you need to extract from each document category.
3. Run automated extraction: Harvey processes the document set, extracting key terms, change-of-control provisions, assignment restrictions, and risk factors from each document.
4. Review flagged issues: The AI surfaces documents with unusual terms, missing provisions, or high-risk clauses that require attorney review.
5. Generate the diligence report: Compile findings into a structured DD report organized by risk category, with citations to specific documents and provisions.

What you'll learn

• How AI handles large-scale document review in M&A contexts
• Structuring diligence checklists for AI extraction
• Quality control techniques for AI-assisted due diligence