Overview

As our learning platform continued to grow, users needed a faster way to find relevant content across courses, policies, reports, and other resources. While leadership agreed that search was becoming increasingly important, there was uncertainty about what form that experience should take.

The central question wasn't simply whether we should add a search feature. We needed to determine whether users would benefit more from a traditional search experience, an AI-powered experience, or a combination of both.

As the UX Designer on this initiative, I led discovery, concept exploration, stakeholder validation, and interface design to help the team identify the most valuable direction for an MVP.

The Problem

Our platform contained a large and diverse content library, but users lacked a centralized way to discover and access information quickly.

During early discussions, two potential paths emerged:

Traditional Search

• Familiar and predictable experience

• Lower technical complexity

• Faster implementation

AI-Powered Search

• Ability to answer questions directly

• More conversational discovery experience

• Potential to surface relevant content proactively

Before making a decision, the team needed answers to two important questions:

1. Would users actually find value in AI-assisted search?

2. Could we build and support that experience within realistic technical constraints?

The challenge was identifying a solution that balanced user needs, business goals, and implementation feasibility.

My Role

UX Designer

I was responsible for:

• Translating project documentation into a design brief

• Conducting competitive analysis

• Exploring search strategies and information architecture

• Identifying accessibility considerations

• Creating low-fidelity design concepts

• Facilitating stakeholder feedback sessions

• Designing high-fidelity prototypes

• Aligning design decisions with engineering and product stakeholders

Research & Discovery

Using AI to Accelerate the Design Process

One of my goals throughout this project was exploring how AI could support design work without replacing design thinking.

Rather than using AI to generate interfaces, I focused on leveraging it to improve the supporting activities that often consume significant time during product discovery.

Creating a Design Brief

The initiative began with an extensive project proposal that outlined the business opportunity, technical considerations, and desired outcomes.

I used Atlassian Rovo to synthesize that documentation into a concise design brief, allowing stakeholders to align more quickly around project goals, assumptions, and open questions.

Competitive Analysis

I used Microsoft Copilot to accelerate competitive research by identifying products that combined traditional search patterns with AI-assisted responses.

This allowed me to evaluate common approaches to:

• Search interaction patterns

• Result presentation

• Suggested follow-up questions

• AI-generated summaries

• User trust and transparency mechanisms

The result was a research report that helped product and leadership understand where similar products were succeeding and where they struggled.

Accessibility Review

As concepts evolved, I used AI tools to quickly identify relevant WCAG guidelines that might influence search interactions.

This helped surface accessibility considerations earlier in the design process, including keyboard navigation, screen reader compatibility, focus management, and content hierarchy.

Stakeholder Communication

I also leveraged AI to organize research findings and draft stakeholder-friendly summaries, enabling faster communication while ensuring recommendations remained concise and actionable.

Design Exploration