AI Contract Review & Redlining Adoption Survey
Measures legal professionals' adoption levels, satisfaction, barriers, and safeguard requirements for AI-assisted contract review and redlining. Designed for legal operations, in-house teams, and law firm practitioners.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
Does your current role involve reviewing, drafting, or redlining contracts?
- Yes
- No
How many contracts have you personally reviewed or redlined in the last 3 months?
- 0
- 1–5
- 6–20
- 21–50
- 51+
In the last 6 months, have you used AI to assist with contract review or redlines?
- Yes
- No
What are the biggest barriers preventing you from using AI for contract review? Please select up to 3.
- Data privacy/confidentiality risks
- Quality/accuracy concerns
- Lack of approval from leadership/IT
- Client or counterparty restrictions
- Cost/budget constraints
- Unclear ROI or metrics
- Ethical or professional responsibility concerns
- Other (please specify)
How acceptable is it for your team to use AI to assist with contract redlines?
Which safeguards would you require before adopting AI for contract redlines? Select all that apply.
- Data residency and confidentiality controls
- Clear audit trail of AI changes
- Explainable outputs with citations
- Alignment with our playbooks/policy
- Human-in-the-loop approval workflow
- Vendor security certifications (e.g., ISO/SOC 2)
- Indemnity or liability terms
- Cost controls and usage limits
- Ability to disable training on our data
- Integration with DMS/CLM
- Other (please specify)
Based on your responses in this survey, please share any additional thoughts or feelings about AI in your contract review workflow.
What is your primary role?
- In-house counsel
- Law firm attorney
- Contract manager/paralegal
- Procurement/sourcing
- Legal operations
- Other (please specify)
Thank you for completing this survey! Your insights will help shape how AI tools are developed and adopted for contract review. Your responses are confidential and will be reported only in aggregate.
How familiar are you with AI tools for contract review?
Which AI-assisted capabilities have you used for contract work in the last 6 months? Select all that apply.
- Clause extraction
- Risk/issue flagging
- Drafting suggested redlines
- Playbook/policy alignment checks
- Summarizing changes
- Document triage or routing
- Tested in pilots only (not live matters)
- Other (please specify)
How much would you support AI drafting initial redlines for standard/boilerplate clauses?
What minimum accuracy rate would you require before allowing AI to auto-apply standard redlines?
- Below 85%
- 85–89%
- 90–94%
- 95–97%
- 98–99%
- 100% (no errors acceptable)
We'd like to explore your thoughts on AI in contract review a bit further. An AI moderator will ask a couple of follow-up questions based on your earlier responses.
How many years have you worked with contracts or practiced law?
- 0–1
- 2–5
- 6–10
- 11–20
- 21+
Based on your experience using AI for contract review, how satisfied are you with the AI assistance you received?
How much would you support AI flagging non-standard or high-risk terms for human review?
Which best describes your organization type?
- Company/in-house legal
- Law firm
- Alternative legal service provider
- Government/public sector
- Nonprofit/NGO
- Other
How likely are you to continue using AI tools for contract review in the next 12 months?
How much would you support AI automatically applying playbook-aligned redlines without human pre-approval?
Approximately how many employees does your organization have?
- 1–49
- 50–249
- 250–999
- 1,000–4,999
- 5,000+
How much would you support AI summarizing counterparty changes and generating comparison reports?
In which region are you primarily based?
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East & Africa
Which contract domain represents most of your work?
- Commercial/Sales
- Procurement/Vendor
- Corporate/Transactions
- Privacy/Data protection
- Intellectual property
- Employment
- Other
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- Combines closed-ended adoption metrics (usage frequency, familiarity, satisfaction, likelihood to continue) with an AI follow-up interview that lets respondents elaborate in their own words on AI in contract review
- Goes beyond a single satisfaction score by measuring support levels for specific AI use cases separately — drafting boilerplate redlines, flagging high-risk terms, auto-applying playbook redlines, and summarizing counterparty changes
- Captures concrete adoption barriers and required safeguards (including a minimum accuracy threshold before AI can auto-apply redlines), giving legal ops teams actionable guardrail data, not just sentiment
- Segments results by role, experience, organization type, size, region, and contract domain so findings can be cut by practice area or seniority, then rolled into an auto-generated report
Jotform
Contract Review Form TemplateThis is a static intake/request form for routing a contract to a reviewer, not a research survey measuring AI adoption, satisfaction, or safeguard preferences. It's useful for operational contract workflows but doesn't collect attitudinal or adoption data at all. Fielding-ready as a form, but it isn't designed to study perceptions of AI-assisted review.
What it does well
- Purpose-built for the contract domain, so field labels and workflow match legal document intake
- Drag-and-drop customization typical of Jotform's form builder
- Likely integrates with e-signature and file upload fields common to contract handling
Where it falls short
- No adaptive AI follow-up interviewing or voice interview option to probe why respondents feel a certain way
- Not designed to measure adoption levels, barriers, or safeguard requirements — it's a routing form, not a research instrument
- No automated response quality scoring or auto-generated analysis report
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
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