Legal AI tool
Introduction

Using AI to assist vulnerable people in accessing legal help, reducing friction and improving service delivery.
Sector
Not-for-profit, Social Impact, Legal Tech, Artificial Intelligence (AI)
Challenge
Design and implement the first trial integration of the custom-built AI model to validate its real-world effectiveness.
My Role
User Experience, Design Strategy, Information Architecture, User Research
Our Solution

A/B testing
An A/B integration that introduced a new “AI-first” pathway into the Intake Tool. This allowed direct comparison between experiences, continuous iteration, and gradual rollout. Iterative changes led to a 48% reduction in abandonments and validated the model’s value, enabling its use in other Justice Connect tools.
How we got there
Background
The Intake Tool is an award-winning platform that helps people apply for legal services online. It reduces admin load and improves service matching by collecting key information upfront.

Hard to Categorise
Despite its success, 41% of users reported difficulty categorising their legal issues. Users had to pick from up to 25 legal categories, often legalistic or unclear, resulting in confusion and misclassification.
Introducing the AI
Justice Connect developed an AI model that could interpret user-submitted text and suggest relevant legal categories from 32 options. My role began once the model was ready to be tested in a live setting.

Strategic Integration Point
The AI was introduced at the point where users select their legal issue—an ideal moment for A/B testing, with minimal disruption to the rest of the tool.

Opt-In Didn’t Work
Initially, the AI was optional, but only 6% of users tried it. We changed to a 25% default rollout with an option to switch pathways. Surprisingly, 60% opted out of the AI, suggesting deeper hesitations or usability issues.
Forced Approach and Insights
We trialled a forced AI group (no option to opt-out), and within 3 months we saw:
- 8% lower abandonment
- 12% less likely to select “something else”. Which we consider a (rough) measurement of confidence
- Extreme AI results: some users received 10+ suggested categories, while others saw no matched categories

Rethinking Categories
We conducted a deep qualitative analysis of user-submitted text to understand gaps in language and matching, and made some changes.
- Refined eligibility logic and result filtering
- Created new user-centred categories like “Divorce matter” and “Neighbourhood dispute”
- Expanded to 39 AI-supported categories (from 25), without overloading users thanks to personalised suggestions
Outcomes and learnings
This work successfully demonstrated that the AI had value for users, staff and the sector. The AI has continued to drive iteration and has since been integrated into other Justice Connect tools.
Improvements to Justice Connect online intake
- 48% reduction in abandonments
- 10% increase in service conversion
- 20% reduction in “Something else” selections
Independent analysis
An independent analysis showed that those who used the AI in the Intake Tool:
- Less likely to quit when asked to select their relevant area of law
- Able to identify the relevant area of law with specificity rather than selecting multiple options
- Able to complete an application quicker