Prioritize Focus

With limited resources, I focused the team on the applications that would deliver the greatest impact for both users and T-Mobile’s budget. We looked for the intersection of highest usage and most problematic UX.

Atlas had no built in analytics, so we had to get creative. I partnered with the Frontline Experience Team (FSE) to access documentation on the top call reasons. I asked our researcher to shadow care experts as they handled those calls and track which microapps they used and what issues came up.

Next, I conducted a visual audit of the apps that appeared most often in calls. I looked for style guide violations, accessibility issues, and UI inconsistencies, which were strong indicators the apps were not built with the design system’s code library. To confirm, I asked my leads to survey lead engineers to hear from them about design system usage (CSS only, library, both, or neither). This information had never been tracked before.

Through this process, we narrowed the list to five high-usage microapps with significant UX and technical debt to prioritize for modernization.

Quantifying Call Costs

UX improvements are often overlooked without clear data, so I focused on modeling how inefficiencies in the Atlas interface increased call handling time and the operational cost of running T-Mobile’s call centers. Since Atlas had no analytics, I worked with our researchers to estimate time delays and error rates caused by legacy UI patterns using internal studies and industry benchmarks.

Once we had rough time estimates, I reconnected with FSE partners to gather operational data such as the total number of call center experts, average daily calls, and cost per minute of call time. From there we modeled the cost impact of UX inefficiencies across the platform.

Maintenance Cost

I also wanted product teams to see the engineering capacity cost of maintaining outdated apps. Two developers on our team who had previously worked on product teams shared comparisons of development time before and after adopting the system’s modular, auto-updating components. Our researcher validated those findings with additional developers across product teams. The sample was small and self-reported, but it provided a credible baseline that showed clear efficiency and maintenance savings.

Outcome

Within one year:

• 2 of 5 targeted apps modernized with modularized components. 

• $42K annual savings in engineering maintenance capacity.

• Improved UX and accessibility for 32% of Atlas call center interactions.

The remaining three apps were added to the following year's roadmap, representing over $100K in annual maintenance savings in addition to call center efficiency improvements.

This project reinforced that design system adoption is often a prioritization challenge rather than a matter of desire. Translating downstream data into tangible business impact helped product teams see modernization as a clear opportunity worth prioritizing, even without direct analytics. This data-driven approach became a repeatable model for future modernization efforts across the platform.

Reflection

While built-in user tracking would have made the analysis faster, taking the time to trace the information and analyze downstream data gave clear cost and efficiency gains. That turned into a compelling business case for modernizing the micro-apps. It also reinforced that in a large organization, it’s easy to default to casting a wide advocacy net but targeting high-impact product teams with data-driven evidence tailored to the areas they owned would have driven change faster.