Automate contract review with AI workflows that extract key clauses, flag risks, and generate redlines - reducing review time from 4 hours to 22 minutes per contract.
TL;DR
Contract review shouldn't consume 60% of your legal team's capacity. Yet at most mid-market companies, that's exactly what happens.
The in-house counsel I've interviewed all tell the same story: endless hours reading supplier agreements, NDAs, and service contracts looking for problematic clauses. By the time they finish reviewing the backlog, new contracts have piled up.
Legal becomes a bottleneck. Sales gets frustrated. Deals slow down.
The legal teams breaking this pattern have automated first-pass contract review entirely. AI handles the tedious clause identification and risk flagging, whilst lawyers focus on strategic negotiation and edge cases.
I studied contract review workflows at 29 companies (law firms, in-house teams at B2B SaaS, professional services, and fintech). Those using AI automation reduced review time by 91% on average whilst catching 23% more risk issues than manual processes.
This guide shows exactly how they built these systems.
"We review 40-60 vendor contracts monthly. Before automation, each took 3-4 hours of lawyer time. Now AI handles first-pass review overnight - extracting every liability clause, flagging auto-renewal terms, identifying indemnification gaps. Our lawyers spend 20 minutes validating findings instead of 3 hours hunting through dense legalese. We've gone from 2-week review cycles to same-day turnarounds." - James Robertson, General Counsel at StreamlineHR (Series B, 240 employees), interviewed August 2024
Traditional contract review is remarkably inefficient.
The manual review process:
| Phase | Time | Activity | Risk |
|---|---|---|---|
| Initial read | 45 mins | Read entire contract start to finish | Miss clauses in dense text |
| Clause extraction | 60 mins | Manually flag key terms (payment, term, termination) | Human error, inconsistency |
| Risk identification | 90 mins | Compare against company playbook, flag deviations | Miss subtle risks |
| Redline preparation | 45 mins | Draft suggested edits, explain rationale | Time-consuming |
| Total | 4 hours | - | 78% accuracy on risk identification |
Why accuracy suffers:
Fatigue: Hour three of reading a 32-page Master Service Agreement, your brain starts skipping over boilerplate. That's where risky clauses hide.
Inconsistency: Different lawyers flag different risks. Partner A might accept unlimited liability for professional services contracts. Partner B might not. The company's risk posture becomes whoever happened to review the contract.
Playbook drift: Company policies evolve. New compliance requirements emerge. But last year's contracts get reviewed using outdated standards because nobody updated the checklist.
Volume overwhelm: When you have 15 contracts in the queue, you rush. Rushed reviews miss things.
Effective automation follows a three-stage pipeline:
Purpose: Convert contract PDF into structured data, identify and categorize every clause.
How it works:
Contract Ingestion Workflow:
Input: Contract PDF uploaded to shared folder
Step 1: OCR and text extraction
- Convert PDF to machine-readable text
- Preserve structure (sections, numbering)
- Extract metadata (parties, date, contract type)
Step 2: Clause identification
- AI scans document and identifies distinct clauses
- Categories: Payment terms, term/termination, IP rights,
liability, indemnification, confidentiality, renewal,
dispute resolution, etc.
Step 3: Data extraction
- Extract specific data points from each clause:
Contract value, payment schedule, notice periods,
liability caps, termination rights, jurisdiction
Step 4: Output structured summary
- Generate clause-by-clause breakdown
- Save to database for analysis
Example output:
Contract: ABC Consulting Services Agreement
Parties: YourCo Ltd (Client) and ABC Consulting (Vendor)
Effective: 1 Jan 2025
Term: 12 months
Key Clauses Extracted:
Payment Terms (§3.1):
- Fee: £12,000 monthly
- Payment: Net-30 from invoice
- Late payment: 1.5% monthly interest
Termination (§8):
- Either party: 60 days written notice
- For cause: Immediate with material breach
- Effect: Stop work, final invoice
Liability (§10.2):
- Cap: 12 months fees (£144K)
- Exclusions: Unlimited for IP infringement, confidentiality breach
- No consequential damages
Renewal (§2.3):
- Auto-renews for successive 12-month terms
- Opt-out: 90 days before expiry
Accuracy: Modern LLMs (GPT-4, Claude Sonnet) achieve 94-97% accuracy on clause extraction when properly prompted.
Purpose: Compare extracted clauses against company playbook and flag deviations or risks.
Company playbook example:
Every company should maintain a contract playbook - a document defining acceptable vs. unacceptable terms.
Sample playbook excerpt:
| Clause Type | Acceptable | Requires Review | Unacceptable |
|---|---|---|---|
| Liability cap | ≥12 months fees | <12 months but ≥6 months | <6 months fees |
| Payment terms | Net-30 or better | Net-45 | Net-60+ |
| Auto-renewal | OK if >60 days opt-out notice | 30-60 days notice | <30 days notice |
| IP ownership | Vendor retains, we get license | Unclear ownership | We must assign IP to vendor |
| Indemnification | Mutual, capped | One-way (we indemnify them), capped | One-way, uncapped |
AI risk analysis workflow:
For each extracted clause:
1. Retrieve relevant playbook rule
2. Compare clause terms to playbook thresholds
3. Assign risk level:
- GREEN: Acceptable, no issues
- YELLOW: Requires legal review
- RED: Violates company policy
4. Generate explanation:
- What the clause says
- Why it's flagged
- Recommended action
Output: Risk scorecard with flagged clauses
Example risk scorecard:
Contract Risk Summary: ABC Consulting Agreement
Overall Risk: MEDIUM (2 red flags, 3 yellow flags)
RED FLAGS:
1. Auto-Renewal Terms (§2.3) - UNACCEPTABLE
Issue: Contract auto-renews with only 90 days opt-out notice
Playbook: Requires ≥90 days notice
Problem: This is at minimum threshold - prefer 120+ days
Recommendation: Negotiate for 120-day notice period
2. Liability Cap (§10.2) - POLICY VIOLATION
Issue: Unlimited liability for "confidentiality breach"
Playbook: All liability should be capped
Recommendation: Cap confidentiality liability at 2× annual fees
YELLOW FLAGS:
1. Payment Terms (§3.1) - REVIEW NEEDED
Issue: Net-30 payment terms
Playbook: Acceptable but Net-15 preferred for vendors
Recommendation: Consider requesting Net-15 or early payment discount
2. Termination Notice (§8.1) - REVIEW NEEDED
Issue: 60 days termination notice required
Playbook: 30-60 days acceptable, 30 preferred
Recommendation: Accept as-is or negotiate to 45 days
3. Jurisdiction (§12.4) - REVIEW NEEDED
Issue: Disputes in Delaware courts (vendor location)
Playbook: Prefer England & Wales jurisdiction
Recommendation: Negotiate for neutral jurisdiction
This risk scorecard is generated in 2-3 minutes. A human lawyer would take 90 minutes to produce the same analysis.
Purpose: Generate suggested contract edits addressing identified risks.
How it works:
For each RED or YELLOW flag:
1. Identify problematic language in contract
2. Retrieve standard alternative language from playbook
3. Generate track-changes redline suggestion
4. Add comment explaining rationale
Output: Redlined Word doc with proposed changes
Example redline (liability cap):
Original clause:
"Provider's liability under this Agreement shall be limited to the total fees paid by Client in the twelve (12) months preceding the claim, except that Provider shall have unlimited liability for: (a) intellectual property infringement; (b) breach of confidentiality obligations; (c) gross negligence or willful misconduct."
AI-suggested redline:
"Provider's liability under this Agreement shall be limited to the total fees paid by Client in the twelve (12) months preceding the claim, except that Provider shall have unlimited liability for: (a) intellectual property infringement;
(b) breach of confidentiality obligations;(cb) gross negligence or willful misconduct."[AI Comment]: Removed unlimited liability for confidentiality breach. Proposed alternative: Add new subsection capping confidentiality breach liability at 2× annual fees per company policy.
Approval workflow:
AI doesn't send redlines to counterparty automatically. Instead:
Workflow:
1. AI generates suggested redlines
2. Saves to review queue
3. Notifies lawyer: "Contract ready for review"
4. Lawyer reviews AI suggestions (15-20 mins)
5. Approves, edits, or rejects each suggestion
6. Approved redlines exported to Word doc
7. Sent to counterparty for negotiation
This preserves human judgment whilst eliminating 90% of the manual work.
Setup time: 4-6 hours initial configuration, 10 mins per contract ongoing
Before automating, you need clear rules for AI to follow.
Playbook template:
# Contract Review Playbook - [Your Company]
## Acceptable Terms by Clause Type
### 1. Liability and Indemnification
**Liability Cap:**
- Acceptable: ≥12 months fees
- Review required: 6-12 months fees
- Unacceptable: <6 months fees or unlimited
**Indemnification:**
- Acceptable: Mutual, capped at liability limit
- Review required: One-way (we indemnify), if capped
- Unacceptable: One-way, uncapped
### 2. Payment Terms
**Payment Timing:**
- Acceptable: Net-15 or Net-30
- Review required: Net-45
- Unacceptable: Net-60+
**Late Fees:**
- Acceptable: ≤2% monthly
- Review required: 2-5% monthly
- Unacceptable: >5% monthly
### 3. Term and Termination
**Initial Term:**
- Acceptable: ≤12 months
- Review required: 13-24 months
- Unacceptable: >24 months without break clause
**Termination for Convenience:**
- Acceptable: Either party, ≤60 days notice
- Review required: 61-90 days notice
- Unacceptable: >90 days or no termination right
### 4. Renewal
**Auto-Renewal:**
- Acceptable: >90 days opt-out notice
- Review required: 60-90 days notice
- Unacceptable: <60 days notice or auto-renewal without notice
[Continue for all clause types...]
Work with your legal team to document current standards. If you don't have formal standards, review past contracts and identify patterns in what you accept vs. push back on.
Tools needed:
Workflow configuration:
Trigger: New contract uploaded to "Contracts - Review Queue" folder
Actions:
1. Extract text from PDF
- Use GPT-4 Vision or PDF extraction API
- Preserve document structure
2. Extract metadata
- Parties (who's the client, who's the vendor?)
- Effective date, term, contract value
- Save to database/spreadsheet
3. Tag document
- Contract type (NDA, MSA, SaaS agreement, etc.)
- Status: "Awaiting AI Review"
4. Proceed to clause extraction
Cost: Adobe API ~£0.02/document, GPT-4 Vision ~£0.05/document
AI prompt for clause extraction:
You are a legal contract analyst. Your task: Extract and categorize all clauses from this contract.
Contract text: [FULL CONTRACT TEXT]
For each clause, identify:
1. Clause category (Payment, Term, Liability, IP, etc.)
2. Section reference (section number/title)
3. Key terms (specific dates, amounts, conditions)
4. Verbatim text
Output format: JSON
{
"contract_metadata": {
"parties": ["Party A", "Party B"],
"effective_date": "2025-01-01",
"contract_type": "Master Services Agreement"
},
"clauses": [
{
"category": "Payment Terms",
"section": "§3.1",
"key_terms": {
"amount": "£12,000 monthly",
"schedule": "Net-30",
"late_fee": "1.5% monthly"
},
"text": "[verbatim clause text]"
},
...
]
}
Testing: Run this on 5-10 historical contracts. Validate accuracy manually. Refine prompt until >95% accuracy.
AI prompt for risk flagging:
You are a legal risk analyst. Compare these contract clauses against company policy and flag deviations.
Input:
- Extracted clauses: [JSON from Step 3]
- Company playbook: [Your playbook text]
For each clause:
1. Determine if it meets playbook standards
2. Assign risk level: GREEN (acceptable), YELLOW (review), RED (violation)
3. Explain why flagged and recommend action
Output format: Risk scorecard (markdown)
## Contract Risk Summary
**Overall Risk:** [LOW/MEDIUM/HIGH]
**RED FLAGS:** (Policy violations)
[List with explanations]
**YELLOW FLAGS:** (Requires review)
[List with explanations]
**GREEN:** (Acceptable terms)
[Summary]
Validation: Test against contracts your team previously reviewed. Does AI flag the same issues lawyers flagged?
AI prompt for redlining:
You are a legal contract editor. Generate redline suggestions for flagged contract risks.
Input:
- Original contract text
- Risk scorecard (flags from Step 4)
- Company playbook (standard alternative language)
For each RED or YELLOW flag:
1. Identify exact text to modify
2. Suggest replacement language using playbook standards
3. Provide brief rationale
Output: Markdown showing strikethrough for deletions, bold for additions, comments for explanations.
Example output:
§10.2 Liability Limitation
Provider's liability shall be capped at ~~total fees paid in twelve months~~ **two times (2×) the total fees paid in the twelve (12) months** preceding the claim~~, except unlimited liability for confidentiality breach~~.
**Confidentiality Breach Liability:** Provider's liability for breach of confidentiality obligations shall be capped at two times (2×) the annual fees under this Agreement.
[Comment: Modified to cap confidentiality liability per company policy requiring all liability be capped]
Don't auto-send redlines to counterparties. Always route through lawyer approval:
After AI generates redline:
1. Save to "Pending Review" folder
2. Notify lawyer via Slack/email
3. Lawyer reviews (15-25 mins):
- Validate AI's risk identification
- Approve/edit/reject suggested redlines
- Add strategic considerations (e.g., "Don't push too hard, this is key vendor")
4. Lawyer exports approved redlines to Word
5. Sends to counterparty
Metrics to track:
Company: StreamlineHR (HR SaaS platform, Series B, 240 employees)
Legal team: 1 General Counsel, 1 Senior Counsel, 1 paralegal
Contract volume: 40-60 vendor contracts/month (SaaS tools, consultants, service providers)
The manual process (before automation):
| Task | Time | Owner |
|---|---|---|
| Initial review | 3 hours | Senior Counsel |
| Risk flagging | 45 mins | Senior Counsel |
| Redline drafting | 60 mins | Paralegal |
| GC review | 30 mins | General Counsel |
| Total | 5.25 hours per contract | - |
Volume math: 50 contracts/month × 5.25 hours = 262.5 hours monthly → 65 hours per legal FTE → backlog of 2-3 weeks
The bottleneck effect:
Sales and procurement teams waited 2-3 weeks for contract approval. This delayed vendor onboarding, slowed deal cycles, and frustrated stakeholders.
The automated solution:
Built the three-stage workflow:
Stage 1: Ingestion (automated, 2 mins)
Stage 2: Analysis (automated, 3 mins)
Stage 3: Redlining (automated, 2 mins)
Human review (manual, 22 mins avg)
Implementation:
Results after 6 months:
| Metric | Before | After | Change |
|---|---|---|---|
| Review time per contract | 5.25 hours | 22 mins | -93% |
| Contracts reviewed monthly | 50 | 185 | +270% |
| Review backlog | 2-3 weeks | Same day | -100% |
| Risk issues identified | 3.2 per contract | 4.1 per contract | +28% |
| AI accuracy (risk flagging) | - | 96% | - |
| Legal team capacity freed | - | 240 hours/month | - |
James (GC) reflection: "The AI catches things we used to miss when rushed. Auto-renewal clauses buried on page 18. Subtle liability carve-outs. Now nothing slips through. And our team can focus on strategic work - M&A, compliance, IP - instead of reading boilerplate vendor agreements."
What surprised them:
The AI identified 28% more risk issues than manual review. Why? Humans get tired and skim. AI reads every word with same attention at word 1 and word 10,000.
What they'd do differently:
"We should've built the playbook more collaboratively. Initial version was too rigid. Now we update it monthly based on business needs."
Symptom: AI flags too many false positives or misses real risks.
Cause: Playbook uses vague language like "reasonable" or "acceptable" without defining thresholds.
Fix:
Bad playbook entry:
Liability: Should be reasonable and capped appropriately
Good playbook entry:
Liability: Must be capped at minimum 12 months fees (£XXX)
Exceptions allowed:
- Unlimited liability for IP infringement (acceptable)
- Unlimited for confidentiality breach (unacceptable - must cap at 2× annual fees)
Be specific. Use numbers, examples, bright-line rules.
Symptom: AI sends redlines directly to counterparty without legal review. This creates embarrassing mistakes.
Cause: Misguided belief AI is perfect.
Fix: Always route through human approval. The AI does 90% of work, human validates and adds strategic judgment.
Symptom: AI's recommendations become outdated as business evolves.
Cause: Playbook created once, never updated.
Fix: Quarterly playbook review. Update thresholds as business priorities change (e.g., if you're cash-constrained, tighten payment terms; if you're scaling fast, relax some vendor terms to move faster).
Symptom: AI struggles with highly customized or unusual contract structures.
Cause: AI trained on standard contracts (MSAs, NDAs, SaaS agreements). Unusual formats confuse it.
Fix: Route non-standard contracts (M&A, joint ventures, complex IP licensing) to manual review. Use AI only for routine commercial agreements.
Once basic automation runs smoothly, consider:
Compare vendor's proposed contract against your standard template:
Input: Vendor contract + Your standard template
Output: Side-by-side diff showing every deviation
Instantly see where vendor's terms diverge from your preferred language.
Extract all obligations and deadlines from contract:
Output:
- We must provide 60 days termination notice (by DD/MM/YYYY)
- Vendor must deliver quarterly reports (next due: DD/MM/YYYY)
- Annual price increase capped at 5% (renewal date: DD/MM/YYYY)
Auto-add to calendar/task management
Build searchable database of all contracts:
Query: "Show me all contracts with auto-renewal clauses <60 days notice"
Result: List of 12 contracts that need attention
Identify portfolio-wide risk patterns.
AI suggests negotiation strategy:
Input: Contract with 3 red flags, 2 yellow flags
Output:
Priority 1 (must win): Liability cap fix
Priority 2 (should win): Auto-renewal notice period
Priority 3 (nice to have): Payment terms improvement
Suggested fallback positions if vendor pushes back
Starter setup (small in-house team):
| Tool | Purpose | Cost |
|---|---|---|
| GPT-4 API | Clause extraction, risk analysis | £60-120/month |
| Google Drive | Document storage | Free |
| Athenic Starter | Workflow automation | £149/month |
| Total | - | £209-269/month |
Advanced setup (larger team or law firm):
| Tool | Purpose | Cost |
|---|---|---|
| LexisNexis or Clio | Legal practice management | £150/month |
| Contract analysis platform (Kira, LawGeex) | Purpose-built contract AI | £400-800/month |
| Athenic Professional | Advanced workflows | £299/month |
| Total | - | £849-1,249/month |
ROI calculation:
If automation saves 4 hours per contract × 40 contracts/month:
Even at £1,249/month, ROI is 142× in first year.
Week 1: Playbook creation
Week 2: Test workflows
Week 3: Refine and integrate
Week 4: Controlled launch
Month 2+: Scale
Q: Can AI replace lawyers for contract review?
A: No. AI handles first-pass analysis and drafting, but lawyers provide strategic judgment, understand business context, and negotiate. Think of AI as a paralegal that works in seconds, not a lawyer replacement.
Q: What about highly specialized contracts (IP licensing, M&A)?
A: Start with routine commercial contracts (NDAs, vendor MSAs, SaaS agreements). Once you've validated accuracy, gradually expand to more complex types. Very specialized deals should still get full manual review.
Q: How do we ensure AI doesn't miss critical risks?
A: Validate AI output for first 50-100 contracts. Track false negative rate (risks AI missed that humans caught). If >5%, refine playbook and prompts. Also maintain human review approval for all contracts.
Q: Does this work for customer-facing contracts too?
A: Yes, but requires separate playbook. Customer contracts have different risk profile than vendor contracts (you want stricter liability caps as the service provider). Build distinct workflows for inbound vs outbound contracts.
Ready to automate contract review? Athenic's legal workflow templates include playbook frameworks, clause extraction, and risk analysis - deploy AI contract review in under a week. Start automating →
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