Role: Lead Product Designer
Timeline: 7 Months
Company: Wolters Kluwer
Product Category: Enterprise B2B SaaS | LegalTech Platform
Core Team: Lead Designer (myself), 1 UX Researcher, 1 Engineer, and 1 Product Manager
Product Overview
As part of LegalCollaborator’s evolution, we introduced AI-powered enhancements to assist corporate legal teams in evaluating law firm proposals more effectively. Our goal is to use machine learning to offer comparative scoring, predictive insights, and visual analytics that reduce manual effort and improve decision-making.
The Objective
To streamline the proposal evaluation workflow using AI-driven tools for:
Comparative analysis for law firm proposal evaluation, utilizing AI summarization, firm history, industry best practices, and market trends.
Law firm scoring and ranking based on historical data.
Forecasting likely engagement and contractual success for selected firms.
Surfacing contextual insights.
Key Features & Enhancements
AI-Powered Comparative Analysis:
Utilize AI to analyze and compare law firm proposals based on predefined evaluation criteria (e.g., pricing structures, staffing plans, and diversity metrics).
Provide clients with clear, concise summaries of proposal strengths, weaknesses, and unique differentiators.
Impact: Simplifies complex decision-making processes by enabling data-driven recommendations and reducing time spent on manual analysis.
Scoring, Ranking, and Predictive Insights
Implement AI-driven scoring systems to rank law firm proposals using customizable client-defined weightings.
Generate predictive insights to forecast the likelihood of success or efficiency for specific law firm engagements.
Impact: Enhances transparency in decision-making, allowing clients to select firms with the highest probability of meeting their strategic goals.
Historical Case Analysis and Contextual Insights
Integrate historical data from previous engagements to inform current RFP evaluations, highlighting trends in law firm performance and past success rates.
Use machine learning to identify patterns and group similar cases for better decision-making.
Impact: Equips clients with deeper contextual knowledge, enabling more informed evaluations and mitigating risks.
Enhanced Visual Dashboards
Introduce user-friendly dashboards that display comparative insights, rankings, and historical analysis in an intuitive format.
Impact: Improves user experience and ensures actionable insights are easily accessible to stakeholders.
Use Cases
Client Managing Attorneys
Quickly compare law firm proposals, gain actionable insights, and select the best partner for critical legal matters.
Pricing Managers/Directors
Validate pricing decisions and ensure budget compliance by leveraging comparative data and predictive insights.
Law Firm Representatives
Receive AI-powered feedback to refine RFP submissions, identify improvement areas, and increase competitiveness.
Implementation Strategy
Phase 1: Research and Feedback Collection
Gather client feedback on existing workflows and pain points in the evaluation process.
Validate AI-powered feature concepts through user research and focus groups.
Phase 2: AI Model Development and Prototyping
Train machine learning models on anonymized data to enable accurate comparative analysis and scoring.
Build prototypes of scoring systems, visual dashboards, and historical case analysis tools.
Phase 3: User Testing and Iteration
Conduct usability testing sessions with client managing attorneys and pricing managers to ensure transparency and usability.
Iterate and refine AI models and dashboards based on user feedback.
Phase 4: Deployment and Onboarding
Launch the features to select clients with personalized training and support.
Develop onboarding resources, including documentation, tutorials, and best practices.
Expected Outcome
Improved Decision-Making
Faster, more confident decision-making for law firm selection with AI-backed data.
Increased Efficiency
Reduced manual effort in evaluating RFP responses by up to 40%, enabling faster turnaround times.
Higher Satisfaction
Enhanced user experience through actionable insights and improved transparency.
Cost Savings
Empower clients to choose firms that deliver the best value for money while meeting strategic goals.
Future Plans
Expand AI capabilities to predict long-term partnership success rates.
Enable deeper integrations with legal analytics and matter management platforms.
Offer advanced customization options for dashboards tailored to specific client needs, such as regulatory compliance or diversity metrics.
Reflection
This was a turning point in my design leadership journey, blending UX clarity with AI complexity. The challenge was not just making the data intelligent, but making the insights trustworthy and digestible for time-pressed legal professionals.