Redesigning Field app to reduce friction and unlock scale

For experts, the result was high cognitive load during already stressful, time-sensitive days; new features were easy to miss; and learning the app required heavy training. For the business, there was no clear place to add new global features, discoverability made it hard to measure feature impact, and scaling the service meant rethinking the app's core structure.

The hardest part of this project wasn't design. It was convincing the organization that foundational work mattered.

Because the redesign wasn't tied to a near-term financial lift, it kept getting pushed down the roadmap. Rather than wait, I began building the future state anyway. Outside of formal prioritization, I created a high-fidelity vision prototype that reimagined the Field app with clear system anchors, intentional navigation, and space to scale.

I socialized that vision repeatedly — in product reviews, engineering conversations, leadership check-ins. Instead of arguing abstractly for "better IA," I showed what it could feel like. I framed the redesign around expert quality of life and long-term velocity: faster shipping, lighter training, fewer workarounds.

Over time, that narrative gained traction. Trust I had built with product and engineering partners made it easier to push. When the initiative was finally prioritized, it wasn't because it suddenly had financial ROI — it was because the organization understood the cost of doing nothing.

Early UI exploration

Once formally greenlit, we grounded the redesign in real-world observation.

We conducted ride-alongs, expert feedback sessions, stakeholder interviews across Ops and Sales Enablement, and analyzed years of SUS data. We also examined business metrics — jobs completed per expert per day, sales performance, and NPS.

What surprised me most wasn't what was inside the app — it was what was happening outside of it.

Experts were creating manual workarounds to compensate for structural gaps. Coaches required them to manually copy their daily job list into Microsoft Teams each morning. Finding a Zoom link for a start-of-day huddle meant digging through chat threads. Performance metrics required either navigating buried menus or asking a manager for a screenshot of PowerBI.

These weren't isolated inconveniences. They were signals that our system architecture didn't reflect the reality of how experts worked.

I drove the core IA decisions while player-coaching two senior designers — one focused on the Homepage and feed evolution, the other on sales and dashboard integration. Early in the process, I facilitated a cross-functional workshop to align on shared experience principles across design, product, and engineering. That alignment prevented us from solving for features in isolation.

The original app opened directly to the job feed. It had no true top-level navigation. Everything else lived in a hamburger menu or within a job itself. Structurally, it was flat — and brittle.

I led the definition of a new IA model using what we had learned from our research, as well as leveraging common patterns out in the wild. We introduced a bottom navigation with three primary destinations: a Homepage, the Job Feed, and a dedicated Performance Dashboard.

The Homepage became a flexible surface for global features — shift management, expense reporting, non-time-bound tasks, and contextual highlights. It gave us a scalable container for capabilities that previously had nowhere logical to live.

We validated the direction through user testing, launched a pilot market, and then rolled out nationwide in a big-bang release approximately two months after formal prioritization.

Experts often work at night, in cars, or on customers' porches. A bright interface wasn't just uncomfortable — it could be unsafe. We partnered with Asurion's design system team to define color tokens and usage patterns that supported readability, consistency, and scale.

Those standards were later added to the company-wide system, making dark mode reusable beyond Field.

Upfront training time for Experts dropped from three full days to less than one. Ongoing feature releases no longer required heavy retraining modules; in many cases, lighter communication sufficed because the system made sense.

Performance dashboard utilization increased significantly once it was no longer buried. Experts started viewing their sales metrics on average 3x more often than before, leading to a 12.5% increase in sales. Navigation confusion decreased, and qualitative feedback consistently reflected lower cognitive load and easier task completion.

Performance dashboard

At our scale, even marginal efficiency gains matter. Saving one minute per job per expert represents roughly $1.1M in annual value. While we did not eliminate all technical debt in a single phase, we fundamentally repositioned the platform to support faster iteration and sustainable growth.

The following year, the business formalized an OKR around evolving systems and reducing tech debt. We were already moving in that direction and well-positioned to continue the momentum.

Despite a user base known for skepticism toward change, feedback was overwhelmingly positive. Experts intuitively understood where to find things, even though the app had fundamentally changed.

This work reinforced the importance of long-horizon advocacy. Foundational redesigns rarely have immediate financial metrics attached to them. Selling them requires vision, trust, and persistence.

It also sharpened my ability to operate as a player-coach — defining strategic direction while staying close enough to the work to guide structural decisions in Figma alongside my team.

If I were to do one thing differently, I would socialize the long-term vision more broadly and earlier. The conviction was there, but earlier visibility may have accelerated alignment.

More than anything, this project reminded me that scalable systems don't happen accidentally. They require someone willing to look beneath what "works" and ask whether it will still work at 2x or 5x the scale.