Reimagining Verizon's mobile experience through unified IA and AI personalization.

Verizon offers customers a range of services across mobile, internet, devices, and account management. However, these experiences were distributed across four separate applications, creating a fragmented digital ecosystem.

Customers could find themselves navigating between different apps to complete related tasks, discover services, or manage their relationship with Verizon.

The opportunity was to rethink how these experiences could work together, creating a more cohesive destination that felt intuitive, connected, and responsive to individual needs.

CURRENT EXPERIENCE · EXISTING APP SCREENS

Rather than approaching unification as a visual redesign, I focused on understanding how the existing experiences could be organized into a more cohesive structure.

Through research and early exploration, I examined how customers interact with Verizon's digital ecosystem, where experiences overlapped, and how emerging AI capabilities could support more relevant interactions.

The challenge wasn't simply combining four applications. It was determining how their individual capabilities could coexist within an experience that felt intuitive rather than overwhelming.

I explored potential information architectures that brought related services and customer needs closer together, considering how navigation, content hierarchy, and key touchpoints could work across a unified application.

• Simplifying navigation across previously separate experiences.
• Creating a more consistent structure for discovering and managing services.
• Reducing friction between related customer tasks.
• Establishing a scalable foundation that could support future capabilities.

Before exploring interface concepts, I examined emerging human–AI interaction patterns across industries to understand how adaptive experiences could balance intelligence with user agency.

Rather than treating AI as a standalone feature, I focused on how it could be integrated into the underlying experience: building trust, supporting discovery, and making interactions more relevant without overwhelming users.

These are conceptual design principles, not claims about a completed AI product.

With the behavioral considerations established, I mapped detailed information architecture models to explore how four separate applications could be consolidated into a unified framework.

This approach improved logical grouping and created a more consistent structure. However, as the hierarchy became deeper, less frequently used features became harder to discover.

The experience was becoming cleaner, but increasingly rigid.

The initial architecture revealed an important limitation: a structure optimized for highly engaged customers could create unnecessary complexity for those who only needed a handful of features.

I shifted the exploration toward two alternative approaches, examining where adaptive intelligence should live within the experience.

Both concepts explored ways to reduce complexity, but they approached adaptivity differently.

Concept A relied on centralized prediction to surface relevant tasks. Concept B distributed adaptivity across navigation and feature hubs, allowing customers to retain more control over how their experience was organized.

Concept B offered a more scalable foundation, supporting both minimalist and highly engaged behaviors without relying entirely on system assumptions.

Concept B introduced adaptive navigation hubs: modular groupings of related features that could appear based on relevance and user preference.

This approach allowed the experience to expand as customer needs evolved, while maintaining a consistent underlying structure.

With the conceptual framework established, I revisited the information architecture, reorganizing features based on their purpose, flow, and relationship to each adaptive hub.

This phase focused on structural validation rather than polished UI execution.