Legal due diligence often requires teams to review large collections of contracts, corporate records, policies, correspondence, and other documents under significant time pressure. The challenge is not simply reading everything. It is determining what matters, connecting related information, and explaining how identified issues may affect the transaction.
Why Legal Due Diligence Becomes Difficult
A due diligence request may begin with a clear list of required documents. As the review develops, the legal team may encounter:
• Missing or incomplete records;
• Inconsistent contract versions;
• Unclear ownership information;
• Agreements stored under different names;
• Amendments separated from their original contracts;
• Expired agreements that remain operational;
• Change-of-control restrictions;
• Undisclosed disputes or compliance issues;
• Obligations that depend on other documents;
• Large volumes of repetitive material.
The risk is not limited to overlooking a document. A reviewer may find the document but fail to connect it with an amendment, side letter, email, or later agreement that changes its effect.
This is where AI can provide useful support. It can help the team examine a larger body of material while keeping the relevant language and source documents available for review.
What AI Can Do During Legal Due Diligence
AI is particularly useful for repetitive review and information organization. Depending on the matter and available sources, it may help with several tasks.
Organize the Document Set
AI can classify documents by type, party, date, jurisdiction, or subject. It may also identify duplicate files, related amendments, missing schedules, and documents that appear to belong to the same agreement family.
A cleaner document set allows lawyers to spend less time sorting files and more time evaluating their significance.
Extract Important Information
AI can extract structured information from contracts and corporate records, including:
• Party names;
• Effective and expiration dates;
• Renewal terms;
• Governing law;
• Assignment restrictions;
• Change-of-control provisions;
• Termination rights;
• Payment obligations;
• Liability limitations;
• Intellectual property ownership;
• Confidentiality obligations;
• Required approvals and notices.
Extracted information can support an initial review table, but the reviewer should confirm each material point against the source document.
Compare Related Agreements
A commercial relationship may be governed by an MSA, several SOWs, amendments, order forms, and side letters. AI can help identify how those documents interact and where their terms differ.
For example, it may flag that:
• A later SOW contains liability language inconsistent with the MSA;
• An amendment changes the renewal date;
• A side letter creates an obligation absent from the main agreement;
• Multiple contracts contain different definitions of the same service;
• The current operating practice does not match the written agreement.
Identify Potential Issues
AI can help locate provisions associated with the transaction's review criteria. It may surface unusual termination rights, consent requirements, exclusivity obligations, restrictive covenants, data-processing commitments, or uncapped liability.
This does not mean that every identified clause is a legal problem. AI can identify language and patterns, but lawyers must determine materiality in light of the transaction, applicable law, and client objectives.
Prepare Initial Summaries
After reviewing the documents, AI can help organize findings into:
• Contract summaries;
• Issue lists;
• Missing-document requests;
• Obligation schedules;
• Comparison charts;
• Preliminary risk categories;
• Questions for management;
• Draft sections of a due diligence report.
The final report should distinguish verified facts from assumptions, open questions, and recommendations.
A Practical AI-Assisted Due Diligence Workflow
An effective workflow begins with a clearly defined review scope rather than a general request to "find risks."
1.Define the transaction, review objectives, materiality thresholds, and responsible reviewers.
2.Identify the document categories and data sources included in the review.
3.Organize documents by entity, agreement type, counterparty, date, and relationship.
4.Extract the fields and clauses relevant to the diligence scope.
5.Compare related agreements, amendments, schedules, and side letters.
6.Flag inconsistencies, missing materials, unusual provisions, and open questions.
7.Verify every material finding against the underlying source.
8.Assign findings to the appropriate legal, financial, tax, privacy, or operational reviewer.
9.Prepare a report that separates facts, risks, assumptions, and recommended actions.
10.Record the final conclusions and supporting sources for future use.
This structure prevents AI output from becoming an unverified collection of observations. Each finding remains tied to the review scope and the document that supports it.
What Lawyers Should Verify
AI-generated summaries can sound confident even when the source is incomplete or ambiguous. Material findings should therefore be checked carefully.
Reviewers should verify:
• Whether the correct document version was analyzed;
• Whether amendments and incorporated documents were included;
• Whether an extracted date or amount is accurate;
• Whether defined terms were interpreted in context;
• Whether the provision applies to the relevant party;
• Whether an apparent inconsistency is resolved elsewhere;
• Whether the issue falls within the agreed review scope;
• Whether the legal conclusion depends on current authority;
• Whether the finding is material to the transaction.
The American Bar Association's Formal Opinion 512 specifically recognizes due diligence and document review as legal tasks for which lawyers may use generative AI. It also emphasizes that lawyers must consider duties involving competence, confidentiality, communication, supervision, candor, and reasonable fees.
Confidentiality and Data Access
Due diligence materials may contain confidential business information, personal data, trade secrets, financial records, or privileged communications.
Before using an AI system, legal teams should evaluate:
• Which documents the system may access;
• How uploaded information is stored and processed;
• Whether information may be used for model training;
• Which users can view the matter;
• Whether access can be limited by client, matter, or role;
• How source history and user activity are recorded;
• How information can be deleted or removed;
• Whether the system fits applicable legal and professional obligations.
The ABA's Rule 1.6 on Confidentiality of Information provides an important professional framework, although the precise requirements depend on the applicable jurisdiction.
How Alloomi Supports Due Diligence Work
Alloomi AI can help legal teams connect documents, instructions, prior decisions, and review standards during due diligence.
Instead of treating each file as an isolated document, Alloomi can help reviewers:
• Keep related agreements and amendments connected;
• Compare current documents with earlier versions;
• Apply lawyer-approved review standards;
• Surface missing information and open questions;
• Explain the source behind an identified issue;
• Prepare working summaries for lawyer review;
• Preserve the final conclusion and supporting context.
This is particularly useful when the review depends on more than contract language. A risk may only become visible after connecting the agreement with a client instruction, transaction fact, prior decision, or related document.
Legal teams can also reuse the same knowledge in later contract reviews, negotiations, and post-closing work.
AI Does Not Replace the Due Diligence Plan
AI cannot determine the proper scope of due diligence without direction. It does not know which issues are material to the transaction unless the team provides the relevant objectives, thresholds, and context.
A technically comprehensive review may still be unhelpful if it:
• Focuses on immaterial clauses;
• Ignores the client's commercial priorities;
• Fails to distinguish standard provisions from genuine exceptions;
• Omits relevant documents outside the reviewed collection;
• Treats all findings as equally important;
• Produces conclusions without verifiable sources.
The strongest use of AI is therefore not unrestricted document analysis. It is targeted analysis within a lawyer-defined workflow.
Frequently Asked Questions
Can AI Perform Legal Due Diligence?
AI can support due diligence by organizing documents, extracting information, comparing agreements, and identifying potential issues. Lawyers should define the scope, verify material findings, and determine their legal and commercial significance.
What Documents Can AI Review During Due Diligence?
Depending on the system and matter, AI may review contracts, amendments, corporate records, policies, correspondence, licenses, and other authorized materials. Access should be limited to appropriate sources.
Can AI Identify Missing Documents?
AI may identify referenced schedules, amendments, exhibits, or agreements that are absent from the reviewed collection. The legal team should confirm whether those materials exist and request them when necessary.
Should AI Findings Be Included Directly in a Due Diligence Report?
AI findings should be verified before inclusion. The final report should distinguish confirmed facts, potential risks, open questions, assumptions, and recommendations.
Is AI Due Diligence Confidential?
Confidentiality depends on the system, configuration, data practices, and applicable obligations. Legal teams should review security, access, retention, and data-use terms before providing sensitive information.
Conclusion
AI can make legal due diligence more organized, consistent, and efficient. It can help teams classify documents, extract relevant terms, connect related agreements, and prepare initial findings.
Its value depends on the surrounding workflow. Lawyers must define what matters, confirm the correct sources, verify material findings, and decide how each issue affects the transaction.
When used with appropriate controls, AI allows legal teams to spend less time sorting information and more time applying judgment to the risks that deserve attention.
