Quick takeaways
- Start with high-volume, low-risk drafting and summarization: clause extraction, redline summaries, and compliance checklists.
- Keep human lawyers in the loop for final interpretation, risk judgment, and any advice delivered to the business.
- Measure legal AI by speed to first draft, consistency of review, reduction in repetitive questions, and deal-cycle impact.
AI is helping more deals get done
Bloomberg Television on how AI is accelerating legal and deal workflows, according to Goldman Sachs' Mehrotra.
What legal work to augment first
Legal teams are judged on speed, accuracy, and risk management. The best AI use cases reduce repetitive reading and formatting work so lawyers can focus on judgment, negotiation, and counsel.
Start where the inputs are documents or structured data and the output is repeatable: contract review against a playbook, clause extraction for reporting, or a checklist built from a regulation. Avoid using AI for final legal advice or decisions that could expose the company to liability.
Contract triage
Route incoming contracts and flag non-standard terms against your playbook.
Clause libraries
Extract and organize fallback language from past agreements.
Compliance readiness
Turn regulations and policies into tracked checklists for audits.
Contract review
Contract review is the most common starting point for legal AI because the work is document-heavy and follows a known playbook. AI can compare an agreement against your standard positions and surface deviations for human review.
Workflow: Upload agreement → AI checks against playbook → Flags risky or non-standard clauses → Lawyer reviews flags → AI drafts suggested fallback language → Final approval by counsel.
The AI does not replace the reviewer. It shortens the time spent locating and describing issues so the lawyer can spend more time deciding what to negotiate.
Clause extraction
Legal teams often need to answer questions across a portfolio of contracts: renewal dates, liability caps, governing law, termination rights, and more. AI can extract these clauses into a structured table or report.
Prompt example: "Review the attached contracts and extract termination for convenience, auto-renewal, governing law, liability cap, and data processing terms. Return a table with contract name, clause location, and exact language. Flag any missing fields."
Always validate a sample of extractions against the source documents. AI can misread numbering, miss defined terms, or conflate similar clauses. A human spot-check is essential before relying on the output for decisions.
Redline summaries
Tracking changes across multiple versions of an agreement is tedious. AI can summarize what changed, who proposed it, and what the business impact is so negotiators can prepare faster.
Workflow: Upload both versions or a redline → AI lists changes by section → Lawyer categorizes each as accepted, rejected, or counter → AI drafts a response summary for the counterparty.
Redline summaries work best when paired with a clear internal playbook. Without standards, the AI may treat every change as equally important. Define your priorities first.
Compliance checklists
Regulatory and internal policy compliance involves translating long documents into tracked tasks. AI can turn a regulation, audit request, or policy into a checklist with owners and evidence requirements.
Prompt example: "Convert this regulation into a compliance checklist for a [industry/type] company. Each item should include the requirement, evidence to collect, suggested owner, and due date logic."
Checklists should be reviewed by the compliance or legal owner before they are operationalized. AI may miss jurisdictional nuances or interpret obligations too narrowly.
Legal research
Research is time-consuming but rarely the final work product. AI can help summarize statutes, surface relevant case law, and draft initial memos on discrete questions.
Workflow: Define the research question → AI drafts an initial answer with citations → Lawyer verifies sources and updates for jurisdiction → Final memo is reviewed before sharing.
Always verify citations and current law. AI models can hallucinate cases or cite outdated rules. Use them as a first draft, not a source of truth.
Outside counsel briefs
The quality of outside counsel work depends heavily on the brief. AI can help organize background facts, desired outcomes, constraints, and questions so the firm starts with context.
Workflow: Collect internal notes and relevant documents → AI drafts a structured brief with facts, issues, and questions → Internal lawyer reviews and adds strategy → Brief sent to outside counsel.
A well-structured brief reduces back-and-forth and controls cost. AI helps format and draft, but the strategy and sensitive context come from the in-house team.
Recommended legal stack
Claude or ChatGPT Enterprise
Draft contract summaries, redline memos, research notes, and compliance checklists from documents.
Harvey, CoCounsel, or Lexion
Analyze agreements against playbooks, extract clauses, and surface deviations at scale.
Perplexity or vLex
Source-backed research for statutes, case law, and regulatory context with citations.
Notion or Ironclad
Store playbooks, clause libraries, and compliance checklists where the team can reuse them.
30-day rollout
- Week 1: Audit your recurring legal work. Pick one document type, such as NDAs or vendor agreements, with a stable playbook.
- Week 2: Build a prompt or template and test it on ten real documents with the reviewing lawyer.
- Week 3: Add a human review checklist and measure time to first draft and error rate.
- Week 4: Document the workflow and expand to the next contract type or compliance area.
Without AI vs. with AI
| Task | Without AI | With AI |
|---|---|---|
| Contract review | Lawyers read every clause against the playbook line by line. | AI flags deviations and drafts suggested fallback language for review. |
| Clause extraction | Teams open each agreement to answer portfolio questions. | AI extracts key clauses into a structured table with spot-check validation. |
| Redline summaries | Lawyers compare versions manually to find what changed. | AI lists changes by section and drafts a response summary. |
| Compliance checklists | Regulations are translated into tasks by hand. | AI turns regulations into tracked checklists for compliance review. |
| Outside counsel briefs | Briefs are assembled from scattered notes and emails. | AI structures facts, issues, and questions into a draft brief. |
FAQ
What legal work should AI handle first?
Start with contract triage, clause extraction, redline summaries, and compliance checklists. Avoid autonomous approvals or final legal advice until review workflows are mature.
Can AI replace in-house counsel?
No. AI accelerates drafting, summarization, and extraction. Judgment, negotiation strategy, and advice to the business still require lawyers.
Is it safe to upload contracts to AI tools?
Only use tools with appropriate confidentiality, data retention, and zero-training policies. Review your vendor agreements and involve security and privacy teams. Use the AI policy template to define what can be uploaded.
How do I keep clause extraction accurate?
Spot-check outputs against source documents, define extraction fields precisely, and refine prompts with examples from your contracts.
How do I measure ROI for legal AI?
Track time to first contract review, number of contracts processed per lawyer, deal cycle impact, and reduction in routine questions. See the AI ROI measurement guide for a simple framework.
Can AI conduct legal research on its own?
No. AI can draft initial research memos and surface sources, but lawyers must verify citations, jurisdiction, and current law before relying on the output.
How do we keep clause extraction accurate?
Spot-check outputs against source documents, define fields precisely, and refine prompts with examples from your contracts.
How do we enforce privilege when using AI?
Segregate privileged material, use private or enterprise instances, and get legal approval before uploading client or deal documents.