SingleSource
Overview
Agents research and enter large volumes of property and risk data into a proprietary policy system—a time-consuming process. The business hypothesized that prefilled data from trusted sources would speed this process by reducing the need for verification. But after shadowing and speaking with agents, I found that this solution didn’t reflect how they actually worked. I designed an alternative approach that better aligned with their workflows, and we tested both methods to see which better supported user needs and business goals.
Prefill Concept Testing
The test would inform how API-sourced data should be introduced into the policy system interface.
The initial belief was that showing trusted sources and descriptions for data automatically prefilled into form fields would lead agents to accept the data without further verification.
To test this, two different prefill experiences were created and evaluated using an interactive Axure prototype. The goal was to find the optimal balance between efficiency, data trust, and user satisfaction.
Methods and Participants
I conducted one-on-one sessions with insurance agents from different regions. Participants were selected based on their primary responsibility for entering data into Policy Center. They interacted with both automatic and suggested prefill interfaces using realistic scenarios and were asked to provide feedback on usability, data trust, and preference. The study was qualitative in nature, relying on observational and interview data.
Key Findings
Most participants preferred the suggested prefill approach. While some appreciated the implied efficiency of automatic prefill, they ultimately prioritized accuracy and control. Participants did not inherently trust prefilled data—even when labeled with known sources—and reported they would continue verifying data with third-party tools regardless of the system’s claims. Many stated that they would not interact with icons meant to explain data sources, further undermining the original hypothesis that providing source visibility would increase trust.
Design Implications
Based on user feedback, the automatic prefill method failed to align with agents’ real-world responsibilities and risk aversion. Agents emphasized the importance of avoiding incorrect data to prevent issues with underwriters and customers. Suggested prefill, in contrast, provided agents with agency and choice, which they valued. It reduced cognitive friction, allowed for better error detection, and aligned more closely with agents’ established workflows and psychological needs. The feature also opens new opportunities for system improvements by tracking user selections and applying that data to improve future experiences.
Beyond Efficiency: A More Human-Centered Design
While automatic prefill may theoretically speed up task completion, the suggested prefill model delivers a more satisfying experience. It respects the agent’s role as a data expert rather than a passive error-corrector. By improving emotional engagement, control, and system clarity, the suggested method may encourage greater willingness to write more policies—supporting the original profitability goal through user satisfaction rather than speed alone.
Conclusion
Ultimately, this research revealed that efficiency alone cannot drive adoption or trust. To support agents effectively, design must acknowledge their risk sensitivity, their need for accuracy, and their desire for autonomy. The suggested prefill model not only respects these needs but provides a scalable, UX-aligned way to incorporate complex data into Policy Center. Going forward, refining this model may offer greater benefits in both user satisfaction and business outcomes than the automatic approach originally envisioned.
Conclusion
Ultimately, this research revealed that
efficiency alone cannot drive adoption or trust. To support agents effectively, design must acknowledge their risk sensitivity, their need for accuracy, and their desire for autonomy. The suggested prefill model not only respects these needs but provides a scalable, UX-aligned way to incorporate complex data into Policy Center. Going forward, refining this model may offer greater benefits in both user satisfaction and business outcomes than the automatic approach originally envisioned.