UserTesting [AI Insights and Discovery functionality].

Contributed as part of a cross-functional design team working on AI-assisted discovery, research, and insights sectors within a UserTesting platform.

Collaborated with other designers, product managers, and engineers to integrate how AI could support product teams in generating, structuring, and interpreting user insights, especially in high-volume qualitative research environments.

My contribution focused on specific areas within the broader initiative, including:

• Designing parts of AI-assisted workflows for synthesizing user feedback, session recordings, and test results

• Exploring and iterating on prompt-driven interactions to guide insight generation

• Contributing to how AI-generated outputs are structured and presented to improve clarity and usability

• Supporting integration of AI into existing product flows while maintaining consistency with established UX patterns

A key aspect of my work was thinking through how to design for uncertainty — ensuring that AI-generated outputs remain understandable and usable despite limitations such as incomplete context or potential hallucinations.

This included contributing to decisions around:

• communicating confidence and ambiguity

• structuring outputs for validation and trust

• balancing speed of insight generation with depth and reliability

Due to confidentiality, the full case study is not publicly available, but I’d be happy to walk through my contributions, design decisions, and learnings in more detail.