Findings

Digital asset findings

A unified view that helps teams act on accessibility issues faster and with more confidence.

💡 Feature design

Lead designer

Timeline
Q4 2024 - Q3 2025
‍
Team
PM, engineering team, and support for our UX researcher.

Impact at a glance
- +30% more remediation actions after launch
- Reduced context-switching by consolidating scattered reports into one table
- Reusable components now powering 3 other teams’ workflows

Findings tab, where users can interact with the findings table and all their features.

The Problem

Teams struggled to act on accessibility issues. Findings were scattered across scans, evaluations, and reports, making it unclear what to fix first or where to track progress. This led to confusion, duplicated effort, and slow remediation.

Findings on scan reports, evaluations and file exports.

My role & process

I led this initiative end to end as the sole product designer, shaping the experience from problem framing to launch. My work included clarifying the problem space through interviews and workflow mapping, defining the design strategy, running three usability test rounds on navigation and table interactions, and collaborating across PM, engineering, and marketing. This also meant shifting user and team mindsets from static reports to real-time, actionable data.

Mapping the information that was required for the feature.

Research & Discovery

Utilizing different methods to both validate and discover, this research surfaced critical insights:

Maria user persona

Research surfaced four key pain points:

Findings felt disconnected from action

Usability testing: Using Maze with static views, A/B comparisons, and full clickthroughs trying out Figma Make and Bolt

Terminology varied between products and teams

Card sorting exercises: to prioritize what users needed to see in the table

Users didn’t know where to start

Navigation flow testing: to understand how users moved between grouped and detailed views

Filtering and grouping didn’t reflect mental models

Behavior analysis: clustering users into behavior types (Excel-minded, Manual-first, etc.)

Design Goals

We envisioned a new experience: Digital Asset Findings, a single, centralized space where teams could view, filter, assign, and resolve accessibility findings in real time.

Create a single source of truth for findings
Standardize language across the product
Reduce context switching with in-flow navigation
Support different user mental models with flexible filters
Lay the foundation for smarter prioritization, automation, and future AI support

It was then that I started putting some mockups where we connected the flow to the rest of the remediation flow:

Remediation flow starting from digital asset findings.

Iterations & Design

Early wireframes exploring how insights and table design could unify scattered data

Unified Table Design

My early research contributed to a new, reusable table component for the design system, improving accessibility, performance, and extensibility across products. The table brought together findings from multiple sources into a single view, added robust filtering and grouping, and supported actions like assigning, dismissing, or exporting.

Contextual Navigation

To keep users in flow, I introduced modals for detail views that reduced context loss. Findings were linked back to their origin, page, rule, and resolution status, so users always knew where issues came from and how they were progressing. This reduced clicks, minimized back-and-forth navigation, and helped teams act more quickly.

Mockup of the contextual slide-out, showcasing information of a finding from the table.

Language Standardization

I led a terminology audit across PM, Marketing, Docs, and Design to standardize key terms (digital assets, finding, validation, sources). This framework is now reused across other product areas.

Shifting Mental Models

The findings table helped reframe how users think: from static, point-in-time reports to a living table of real-time data. The focus shifted from only understanding what’s wrong to acting quickly on what matters.

The different iterations of the filter component within the page.

Video of a prototype of the upcoming iteration of Digital asset findings.

Outcomes

Launching Findings reinforced the value of cross-functional collaboration. From naming to architecture, I worked closely with PMs, CSMs, and engineers to deliver clarity across the platform.

We launched Digital Asset Findings in Q3 2025 to all customers.

30% increase in conversions for core remediation flows (e.g., task creation, validation)
Notable drop in confusion around where issues originated or what to do next
Improved internal workflows thanks to consistent language and reusable components
Components and patterns were reused by 3 other teams

Design of the MVP for Digital asset findings.

What’s Next

We're introducing agentic AI to scale the impact of findings and reduce friction across workflows:

Smart Prioritization
The AI will learn from past assignments and team behavior to surface which issues need attention first, based on severity, frequency, and organizational goals.

‍Auto-Drafted Tasks
Instead of writing tickets from scratch, users will get task suggestions pre-filled with relevant metadata, ready to review and assign.

‍Issue Clustering & Similarity Detection
Findings with similar patterns will be grouped automatically to speed up validation and reduce redundancy.

‍Cognitive Offloading
‍By highlighting what’s new, what’s been viewed, and what’s unresolved, the AI will help users stay focused without needing to retrace steps, or change context.

Mockups for AI ebmedded in the experience.