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Privacy & Compliance

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.

25 questions · ~11 min
Q01
Message

Welcome and thank you for participating in this survey about contract-review practices and AI tools. This survey is voluntary and confidential — you may stop at any time. There are no right or wrong answers; we are interested in your honest opinions and experiences from the last 3–6 months. Your responses will be anonymized and reported only in aggregate for internal research purposes. Estimated time: 11 minutes.

Q02
Multiple Choice

Does your current role involve reviewing, drafting, or redlining contracts?

  • Yes
  • No
Q03
Dropdown

How many contracts have you personally reviewed or redlined in the last 3 months?

  • 0
  • 1–5
  • 6–20
  • 21–50
  • 51+
Q04
Multiple Choice

In the last 6 months, have you used AI to assist with contract review or redlines?

  • Yes
  • No
Q05
Multiple Choice

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)
Q06
Opinion Scale

How acceptable is it for your team to use AI to assist with contract redlines?

Scale: 17
Min:Not at all acceptableMax:Completely acceptable
Q07
Multiple Choice

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)
Q08
Long Text

Based on your responses in this survey, please share any additional thoughts or feelings about AI in your contract review workflow.

Q09
Dropdown

What is your primary role?

  • In-house counsel
  • Law firm attorney
  • Contract manager/paralegal
  • Procurement/sourcing
  • Legal operations
  • Other (please specify)
Q10
Message

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.

Q11
Opinion Scale

How familiar are you with AI tools for contract review?

Scale: 17
Min:Not at all familiarMax:Extremely familiar
Q12
Multiple Choice

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)
Q13
Opinion Scale

How much would you support AI drafting initial redlines for standard/boilerplate clauses?

Scale: 17
Min:Strongly opposeMax:Strongly support
Q14
Dropdown

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)
Q15
AI Interview

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.

Q16
Dropdown

How many years have you worked with contracts or practiced law?

  • 0–1
  • 2–5
  • 6–10
  • 11–20
  • 21+
Q17
Opinion Scale

Based on your experience using AI for contract review, how satisfied are you with the AI assistance you received?

Scale: 17
Min:Not at all satisfiedMax:Extremely satisfied
Q18
Opinion Scale

How much would you support AI flagging non-standard or high-risk terms for human review?

Scale: 17
Min:Strongly opposeMax:Strongly support
Q19
Dropdown

Which best describes your organization type?

  • Company/in-house legal
  • Law firm
  • Alternative legal service provider
  • Government/public sector
  • Nonprofit/NGO
  • Other
Q20
Opinion Scale

How likely are you to continue using AI tools for contract review in the next 12 months?

Scale: 17
Min:Not at all likelyMax:Extremely likely
Q21
Opinion Scale

How much would you support AI automatically applying playbook-aligned redlines without human pre-approval?

Scale: 17
Min:Strongly opposeMax:Strongly support
Q22
Dropdown

Approximately how many employees does your organization have?

  • 1–49
  • 50–249
  • 250–999
  • 1,000–4,999
  • 5,000+
Q23
Opinion Scale

How much would you support AI summarizing counterparty changes and generating comparison reports?

Scale: 17
Min:Strongly opposeMax:Strongly support
Q24
Dropdown

In which region are you primarily based?

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East & Africa
Q25
Dropdown

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 Template

This 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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