Led a cross-functional effort from initial request through post-launch to redesign the mobile claims experience, freeing up claim handlers' time through AI experimentation, research, and a refined product vision.
Overview of my direction to the team
What we were solving
Leadership believed adjusters spent too much time in the field on straightforward claims. We were tasked with finding ways to reduce that time through mobile and AI solutions.
The project required balancing ambitious AI ideas with the reality of complex claims work, ensuring that whatever we delivered actually fit into adjusters' daily workflows.
What made this hard
Even simple claims involved multiple variables. Early AI experiments didn't fully address the complexity, and many adjusters' pain points came from duplicated work rather than field time itself.
Early assumptions didn't align with observed workflows, requiring a shift in product vision. The challenge was identifying real bottlenecks and focusing on high-value fixes while keeping stakeholders aligned.
What I did
I led the design process, managing two designers and collaborating closely with PMs, engineers, data scientists, and field adjusters. I drove user observation, journey mapping, and rapid prototyping to uncover and address real pain points.
Key leadership actions:
Phase outcome
Seeing the work firsthand changed our priorities and saved us from chasing the wrong solution. The team aligned on a focused direction grounded in what adjusters actually needed.
How we worked
We applied a strong product model approach: research, prototyping, and validation at each step. Rather than relying solely on AI experiments, we focused on process simplification and reducing double entry in notes.
Our design principle was to observe real work before committing to assumptions — solve for actual bottlenecks, not assumed ones.
Some leadership decisions I made during this phase:
Phase outcome
The redesigned mobile experience fit naturally into adjusters' daily workflows. The team delivered something that solved real problems rather than assumed ones.
What happened
The redesigned mobile experience and process changes led to measurable time savings, wide adoption, and further investment in the mobile platform. The work strengthened organizational support for the product model.
The project created a scalable model for combining AI experiments with practical process design to deliver real operational gains.
Project outcome
Adjusters spent less time in the field on straightforward claims. The experience saw wide adoption and led to further platform investment. The work was recognized across the organization and shifted how the company approached AI and product development.