The Tool Desk
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What AI can—and cannot—do in contract review
AI-assisted contract tools can support focused tasks: locating or analyzing clauses, comparing document versions, checking terms against a review playbook, and drafting proposed redlines. These are aids to review, not a substitute for legal judgment. A tool may overlook a clause or apply playbook guidance incorrectly, particularly in complex or lengthy agreements.
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Microsoft’s Legal Agent documentation describes features for contract review, redlining, playbook alignment, clause analysis, and version comparison in Word. Microsoft says its outputs are advisory and warns of missed clauses or misapplied guidance in complex or lengthy documents. That is product documentation, not an independent performance assessment. Microsoft states: “All outputs generated by the Legal Agent are advisory only.” Microsoft Legal Agent: Transparency Documentation
How to check an AI-assisted review
Use a bounded workflow that makes the tool’s contribution visible and keeps legal decisions with people. The steps below are a practical synthesis of official guidance, not a universal legal procedure.
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- Define the task and acceptable error level. Specify what the tool should do—for example, identify a defined set of clauses or compare a draft with an approved playbook. Decide what kinds of missed or incorrect findings are tolerable for that task; do not assume a general accuracy claim applies to your documents.
- Use an approved system with appropriate data terms. Check what information the supplier retains, whether it can use submitted material for other purposes such as training, who can access it, and how it is protected. Confirm deletion or return arrangements where relevant.
- Ask for findings tied to exact contract language. Prefer outputs that point to the clause or passage they rely on. Treat an unsupported conclusion as a prompt to investigate, not as an established finding.
- Verify the cited text and context. Open the cited passage in the source document. Check definitions, exceptions, schedules, amendments, and related clauses; a locally plausible reading may change when read in context or against another version.
- Have a qualified professional assess legal significance. Validate whether the finding matters for the transaction, whether proposed wording is appropriate, and whether changes create other risks. Escalate uncertainty rather than accepting a suggested redline automatically.
- Record assistance and decisions where policy requires. Keep a useful audit trail of material AI assistance, the human checks performed, and the decisions made under your organization’s rules.
How to evaluate a contract-review tool
Assess a system against the work it will actually handle. Vendor statements about capability are claims to test, not proof of performance on your agreements.
| Evaluation area | What to check |
|---|---|
| Use-case fit | Which contract types and tasks are supported? Confirm that the tool fits your intended workflow rather than relying on a broad “legal AI” description. |
| Source traceability | Can reviewers follow each finding to the exact clause or text that supports it? |
| Accuracy evidence | Test performance on representative agreements, including long or complex documents. Define success and acceptable error for the specific task; ask how any claimed results were measured. |
| Data and access controls | Review retention, secondary use, user access, and security terms for the data you expect to submit. |
| Workflow integration | Check compatibility with document workflows and playbooks, and whether human approval fits naturally into the process. |
| Auditability and oversight | Determine what activity and decisions can be recorded, and whether reviewers can inspect and approve proposed changes. |
| Supplier and exit terms | Assess support, contract terms, data return or deletion, and how you can leave or move your work if the relationship ends. |
The Information Commissioner’s Office (ICO) recommends considering accuracy KPIs or service-level agreements in supplier contracts, as well as appropriate return or deletion terms for personal information. Its guidance page says it is under review following the Data (Use and Access) Act, so check the current page and applicable requirements for your situation. ICO: Contracts and third parties
Other useful selection considerations include use-case definition and scrutiny of data use and retention, highlighted by the State Bar of Arizona, and procurement and exit considerations discussed by the Law Society of England and Wales. Thomson Reuters also publishes a buyer’s guide to evaluation factors; it is a vendor-authored overview, not independent evidence that any product performs better. State Bar of Arizona: Best Practices for Selecting AI Tools; Law Society: Generative AI – the essentials; Thomson Reuters: Buyer’s guide: AI for legal contract review and analysis
Match human oversight to the risk
Not every AI-assisted task has the same consequences. Singapore’s Ministry of Law lists document or contract review among medium-risk examples and describes human-in-the-loop approval for decisions requiring legal judgment. This is Singapore-specific guidance, not a universal classification; use the law and professional rules that apply in your jurisdiction. Singapore Ministry of Law: Guide for Using Generative AI in the Legal Sector
Professional responsibility remains with the people and organization conducting the review. The Law Society of England and Wales advises effective quality control, while the State Bar of Arizona’s selection guidance calls attention to defining the use case and checking data use and retention. Build oversight around the potential impact of an error, and do not let a system’s confident wording stand in for verification.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When AI assistance is a poor fit
Pause or avoid using a tool for a review task when you cannot establish that the system is approved for the information involved, cannot check its findings against source text, or lack a qualified person to validate material conclusions. Be especially cautious when an agreement is unusually complex, the relevant playbook is incomplete, or a proposed change could have significant legal or commercial consequences. In those cases, AI may help organize questions, but it should not be treated as the reviewer or decision-maker.
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