How to Use AI to Speed Up Contract Review and Legal Workflows

Upload a contract to Claude (for the largest documents, given its 200,000-token context window) or ChatGPT and ask targeted questions.

Useful extractions for executive review: 'Summarise the key obligations of each party in this contract. Present as a table with our obligations on one side and their obligations on the other.'

'What are the termination rights in this contract? Who can terminate, under what circumstances, and what is the notice period?'

'What liability caps and exclusions apply in this contract? Are there any areas where our liability is unlimited?'

'Are there any provisions in this contract that are unusual for a standard [contract type] agreement? Flag anything that would typically require negotiation.'

'What are the payment terms, including when payments are due, what triggers payment, and what the consequences of late payment are?'

For comparing a received contract against your standard form or previous version, AI can identify changes and differences rapidly.

'Here are two versions of this agreement [paste both]. What are the key differences between version 1 and version 2? Which differences are most commercially significant?'

'Here is our standard supply agreement and a contract received from a supplier. What provisions in their contract differ from our standard? Flag any provisions that appear more favourable to them than our standard and any provisions that create additional obligations for us.'

This comparison work, which can take a legal team several hours to complete manually for a complex contract, can be completed in minutes. The AI output identifies the differences; your legal team assesses the implications.

For M&A or partnership due diligence involving review of a large number of contracts, AI can process contracts at scale to extract standardised information.

For each contract in a due diligence review: 'Extract the following information from this contract and present in a table: contract type, counterparty name, effective date, term, termination notice period, change of control provisions, and any unusual provisions.'

Running this across a portfolio of contracts builds a contract register rapidly. The register then identifies the contracts that require detailed legal review (change of control clauses, unusual provisions, high-value long-term commitments) versus those that are standard and do not require detailed attention.

This triage approach dramatically reduces the time required for large-scale contract due diligence while ensuring legal resource is focused where it is most needed.

AI contract analysis has genuine limitations that must be clearly understood before relying on it.

AI is not a lawyer. It can identify and extract provisions; it cannot apply legal judgement about whether those provisions are enforceable, how they would be interpreted in the context of UK case law, or what the practical legal risk exposure is in your specific situation. Legal judgements on material contracts require qualified legal advice.

AI can misread or mischaracterise provisions. Contract language is often precise in ways that matter: 'best efforts' versus 'reasonable efforts' versus 'all reasonable endeavours' have specific UK legal meanings. AI may not correctly characterise the significance of these distinctions. Verify any provision where the specific wording matters.

AI does not know your specific legal context. It cannot assess whether a particular provision creates risk for your organisation without understanding your business, your existing contractual relationships, your regulatory environment, and your risk appetite. This context must come from qualified legal counsel.

The right use of AI in legal workflows: accelerate the mechanical review and extraction work; focus qualified legal resource on the judgement and advice that only a lawyer can provide.