AI contract review works well for standard agreements — NDAs, basic freelance contracts, template leases. But some contracts carry enough complexity and financial exposure that AI-only review is actively risky. Here are five types where you should always involve a lawyer.
1. Employment Agreements With Non-Competes or Equity
Employment agreements look simple on the surface but often contain provisions that shape your career for years. Non-compete clauses, IP assignment provisions, clawback terms on equity, and invention assignment clauses all require analysis that depends on your specific situation.
AI can identify that a non-compete exists and that it's broadly written. It cannot tell you whether it's enforceable in your state, whether your new employer's legal team will care, or whether the IP assignment clause captures the side project you've been building on weekends.
The cost of getting this wrong isn't a bad contract clause — it's your ability to change jobs, start a company, or own your own work.
2. Commercial Leases Over $100,000 Total Value
Commercial leases are significantly more complex than residential leases. CAM charges, operating expense pass-throughs, personal guarantees, exclusivity clauses, co-tenancy provisions, and build-out allowances all interact in ways that AI tools don't model well.
A residential lease AI review might catch a missing cap on late fees. A commercial lease review needs to calculate your total occupancy cost over the lease term, including escalation clauses, tax pass-throughs, and maintenance obligations that compound annually.
The financial exposure on a 5-year commercial lease can easily exceed $500,000. Attorney review at $2,000-5,000 is a rounding error on that commitment.
3. M&A and Investment Documents
Merger agreements, stock purchase agreements, SAFE notes, and convertible notes contain interdependent provisions where a single clause can change the economics of the entire deal. Liquidation preferences, anti-dilution protections, drag-along rights, and representations and warranties all need to be evaluated as a system, not as individual clauses.
AI tools analyze clauses independently. They can flag that a liquidation preference exists, but they cannot model how a 2x participating preferred liquidation preference interacts with an anti-dilution ratchet and a pay-to-play provision to affect your ownership in a down round.
These contracts define who gets what money and when. They're the definition of "worth paying for a lawyer."
4. Intellectual Property Licenses
IP licensing agreements — patent licenses, software licenses, content licenses, trademark agreements — involve rights that are invisible until they're violated. The difference between an exclusive and non-exclusive license, worldwide and territory-limited, perpetual and term-limited, can be worth millions of dollars.
AI can parse these terms individually, but it struggles with the interaction effects. A "non-exclusive, worldwide, perpetual license" sounds broad until you read the field-of-use restriction three sections later that limits it to "internal business purposes only" — making it worthless for your actual use case.
IP licensing mistakes are especially expensive because they're often discovered years later, during an audit or a dispute, when the cost of unwinding them is highest.
5. Government and Regulated Industry Contracts
Government contracts (federal, state, and municipal) carry compliance requirements that don't exist in private sector agreements: FAR/DFAR clauses, Buy American provisions, cybersecurity requirements (CMMC, FedRAMP), small business subcontracting plans, and Davis-Bacon prevailing wage requirements.
AI models trained primarily on private sector contracts will miss government-specific risks. A clause that's standard in a commercial SaaS agreement might violate a federal procurement regulation. Compliance failures in government contracting can result in False Claims Act liability, suspension, or debarment — consequences that don't exist in private sector deals.
Similarly, contracts in regulated industries (healthcare, financial services, defense) carry industry-specific requirements that general-purpose AI tools aren't designed to catch.
The Pattern: Complexity and Consequences
These five contract types share two characteristics: high complexity (interdependent provisions, jurisdiction-specific rules, domain expertise required) and high consequences (financial exposure, career impact, regulatory risk).
AI tools are built for the opposite profile: standard structures with moderate stakes. Using them where they work well and involving attorneys where they don't is better than using either approach exclusively.
The goal isn't to avoid AI tools. It's to use them where they add value — initial screening, standard agreements, understanding what a contract says before your attorney consultation — and to recognize the boundary where human judgment becomes essential.
About Vladimir Kuzin
Founder & CEO, Shepherdstack LLC
Vlad Kuzin is the founder of Shepherdstack LLC and creator of Pact, an AI-powered contract review tool. He builds software that helps individuals and small businesses understand the documents they sign.
Disclosure: Founder of Shepherdstack LLC, the company behind Pact. All comparison articles use a standardized evaluation methodology applied equally to all tools, including Pact.