AI for Due Diligence: A Practical Guide

1. How AI accelerates due diligence

Traditional due diligence involves reviewing hundreds or thousands of documents to identify risks, extract key terms, and build a comprehensive picture of the target company's legal position. A mid-size M&A transaction might involve reviewing 500 to 5,000 documents across categories including corporate governance, contracts, real estate, intellectual property, employment, litigation, and regulatory compliance.

Without AI, this process requires a team of associates working through the data room document by document, populating spreadsheets and checklists. The timeline is measured in weeks, the cost in tens of thousands of dollars, and the quality depends heavily on the consistency and attention of the individual reviewers.

AI accelerates this in three ways. First, it classifies and organizes documents automatically, eliminating the sorting phase. Second, it extracts key terms and provisions across the entire document set in parallel, surfacing the data that matters without requiring sequential reading. Third, it identifies risk patterns, such as unusual termination provisions, missing standard protections, or regulatory non-compliance indicators, that might be missed when reviewing documents in isolation.

The result is not a shortcut. It is a more thorough review completed in less time. AI processes every document with the same level of attention, regardless of whether it is the first or the five-thousandth. This consistency is one of its most significant advantages over manual review.

2. Document extraction and classification

The first step in AI-powered due diligence is ingesting and classifying the document set. The system reads every document, identifies its type (contract, corporate resolution, financial statement, correspondence, regulatory filing), and organizes it within the due diligence framework.

For contracts, the AI extracts key terms: parties, effective date, term, renewal provisions, assignment rights, termination triggers, governing law, and dispute resolution mechanism. These extractions are compiled into a structured summary that allows the deal team to see the entire contract portfolio at a glance rather than reading each agreement individually.

For corporate documents, the AI identifies authorized signatories, board resolutions, capitalization information, registered agents, and jurisdictional filings. For IP documents, it maps patent portfolios, trademark registrations, license agreements, and assignment chains.

Irys's document intelligence layer handles extraction across diverse document types, including scanned PDFs, multi-page spreadsheets, and legacy document formats. The knowledge graph engine maps relationships between entities and documents, so you can trace a particular obligation from the contract that creates it through the corporate approval that authorized it.

3. Risk identification

Risk identification is where AI adds the most value beyond simple data extraction. The system does not just find terms; it evaluates them against market norms and flags deviations.

Change of control provisions. AI scans every contract for change-of-control triggers that could be activated by the transaction. It identifies whether consent is required, whether the provision allows termination, and whether there are any cure or notice periods.

Unusual limitation of liability. The system flags contracts where liability caps are significantly below market standard, where consequential damages are not excluded, or where indemnification obligations are uncapped. These provisions directly affect the risk profile of the acquisition.

Employment risks. AI identifies employment agreements with non-standard severance triggers, restrictive covenant issues, undisclosed bonus obligations, or equity acceleration provisions. These can represent significant post-closing liabilities.

Regulatory compliance gaps. The system identifies required filings that are missing, licenses that are expiring, and regulatory conditions that have not been satisfied. These compliance gaps can affect closing conditions or create post-closing exposure.

4. Reporting and deliverables

Due diligence deliverables need to serve multiple audiences: the deal team, the client, lenders, and potentially regulators. AI-generated reports should be structured to serve these different needs without requiring the deal team to rebuild the analysis for each audience.

A comprehensive due diligence report generated with AI assistance includes an executive summary of material findings, a risk matrix organized by category and severity, detailed contract summaries with key term extractions, a list of missing or incomplete documents, and specific recommendations for representations, warranties, and indemnification provisions in the purchase agreement.

Each finding in the report links back to the source document and the specific provision that generated the finding. This traceability is essential for credibility. A client or a lender reviewing the due diligence report should be able to click through to the underlying document and verify any finding independently.

The time savings in reporting are substantial. Instead of spending days compiling findings into a structured report, the deal team reviews and edits an AI-generated report that already has the structure, the findings, and the source citations in place. The attorney's time goes into analysis and judgment rather than document assembly.

5. Integrating AI with existing due diligence workflows

AI does not require you to abandon your existing due diligence framework. The most successful implementations layer AI onto proven workflows rather than replacing them entirely.

Start with your standard due diligence checklist. Upload the document set into the AI platform and use the checklist categories to structure your queries. For each checklist item, ask the AI to identify the relevant documents, extract the pertinent terms, and flag any issues. The checklist ensures completeness; the AI ensures speed.

Maintain your existing quality control process. Partner review of associate work should continue. The difference is that the partner is reviewing AI-assisted analysis rather than raw document summaries, which means the review is faster and more focused on judgment calls rather than completeness checks.

Keep your data room organized. Even with AI, a well-organized data room produces better results than a disorganized one. If documents are properly labeled and categorized in the data room, the AI classifies them more accurately and the review proceeds more smoothly.

6. Practical guidance for adoption

Pilot on a real deal. The best way to evaluate AI due diligence is to run it alongside a traditional review on an actual transaction. This gives you a direct comparison of speed, quality, and cost. Most firms that run this comparison find that AI catches issues the manual review missed.

Train your team. AI due diligence tools are most effective when the attorneys using them understand how to formulate effective queries. Invest time in training your deal team on how to ask the right questions, how to interpret confidence scores, and how to verify AI-generated findings.

Establish verification protocols. Define which findings require independent verification and which can be relied on from the AI analysis alone. Material risk findings should always be verified against the source document. Routine extractions of standard terms can be spot-checked rather than fully verified.

Measure the results. Track time spent, issues identified, and turnaround time for your first few AI-assisted due diligence exercises. This data makes the business case for broader adoption and helps you refine your process.