AI Assistant for Document-Heavy Legal Work
Designing an AI workflow that reduces review risk and scales legal analysis
Executive Summary
I designed an AI-assisted document review workflow for enterprise legal teams working on high-stakes, document-heavy matters. To address the risks of unverifiable AI outputs, I embedded a task-aware AI assistant within structured document grids. This approach added clear scope control, built-in clarification steps for ambiguous questions, and source-level citations to help teams verify and defend their analysis. I validated the workflows with over 12 legal teams across different practice areas. This work guided future AI features and established reusable patterns for trustworthy, efficient AI, helping teams reach insights faster while reducing the risk of errors or unverifiable results.
Impact snapshot: Developed and validated a high-trust AI document review workflow with 12+ legal teams. This reduced ambiguity in AI outputs and established reusable interaction patterns that lowered adoption risk and guided future feature development.
Role & Context
Role
Principal Product Designer, leading UX strategy, interaction design, and cross-functional alignment with product, engineering, and legal domain experts.
Context
Enterprise legal teams often review hundreds or thousands of documents for each matter. While AI promised faster work, early tools failed to earn trust because they gave unclear answers, lacked clear boundaries, and made it difficult to defend findings. I first shared this approach publicly at LegalWeek and improved it through direct feedback from practicing legal teams.
Business Problem
Legal document review creates compounding pressure for both customers and vendors:
- Review work is expensive and time-intensive
- Errors carry material legal and reputational risk.
- Existing AI tools trade speed for trust, limiting adoption
From a business standpoint, this resulted in:
- Slow time to insight for customers
- High perceived risk in adopting AI-assisted tools
- Competitive pressure from newer products promising speed without accountability
The goal was not just to add AI-generated answers, but to reduce risk and enable faster, defensible analysis.
| Traditional Review | AI-Assisted, Defensible Review |
|---|---|
| Sequential document review | Structured evidence review |
| High manual effort | Targeted expert validation |
| Hard to defend conclusions | Evidence-linked findings |
| Slow time to insight | Faster, verifiable insights |
What Success Needed to Look Like
This work would be successful if it could:
- Help legal teams move faster without obscuring source material.
- Reduce ambiguity in AI output rather than masking it.
- Establish a reusable interaction model for AI across structured and unstructured data.
Success meant teams could rely on outputs only when scope, source, and reasoning were clear.
Key Insight from Discovery
Early validation with legal teams revealed a consistent pattern:
Lawyers want speed, but will stop using tools if they can’t verify, define the scope, or explain the conclusions.
This reframed the problem from “How do we answer questions?” to:
How do we support legal reasoning while safely accelerating it?
Design Strategy and Decisions
1. Rejecting the Generic Chatbot Model
Considered
A free-form chat interface layered on top of document sets.
Rejected because
It obscured document scope, encouraged over-trust in AI output, and made conclusions difficult to defend or explain.
Chosen instead
A task-aware AI assistant embedded within structured document workflows, where scope, context, and evidence remain visible.
This decision reduced ambiguity and fit how lawyers already work.
When comparing AI models, generic chat tools often give unclear answers. In contrast, a grid-embedded assistant makes scope, evidence, and verification easy to see during legal review.
2. Making Clarification a First-Class Interaction
Ambiguous legal questions often produce unreliable answers.
I intentionally blocked analysis until questions were clarified to prevent false confidence and downstream rework.
Design decision
Introduce an explicit clarification loop that detects vague queries and prompts users to narrow the scope before generating summaries.
Why this mattered
This reduced rework by preventing reliance on unscoped answers and kept outputs defensible during review.
3. Integrating AI Directly into the Document Grid
Instead of separating conversation from data, I brought AI summaries directly into the context of document grids.
- Users move seamlessly from high-level answers to cited excerpts.
- Scope remains visible and controllable at all times.
This preserved legal rigor while making the review process easier for teams.
4. Designing Trust, Not Assuming It
Trust was treated as a design problem, not a byproduct.
Key mechanisms included:
- Inline citations tied to source documents
- Clear explanations of why an answer was generated
- Feedback controls to flag uncertainty or errors
These choices prioritized defensibility over instant answers.
What I intentionally did not do
- I did not allow free-form AI answers without citations.
- I did not automate final conclusions without human review.
- I did not surface confidence scores without an evidence context.
Validation & Outcomes
Outcomes were concrete and defensible:
- Core workflows were validated with 12+ legal teams across multiple practice areas.
- The interaction model informed subsequent AI-assisted features beyond this concept.
- The work established shared principles across design, product, and engineering for what trustworthy AI must include in legal contexts.
From a business perspective, the project reduced adoption risk and created a foundation for future AI features, not just a one-off demo.
We validated the analysis grid early on and confirmed that its structured questions, focused answers, and clear citations matched actual due diligence workflows.
Reflection
This work established reusable interaction patterns for AI-assisted review in high-risk workflows.
My role was to make AI usable and defensible within real legal workflows, balancing acceleration with accountability.
