Ford Motors
AI/ML-Infused Custom Navigation

  • Company:
    Ford Motors
  • Role:
    Product Designer / IA
  • Project:
    AI/ML Customer Navigation Experience

Project Overview | AI/ML Customer Navigation Experience

For FordPro.com, I designed a dual-personalization IA model that adapted navigation from three user inputs (responsibilities, primary uses, and fleet size), applying retail personalization logic from sectors including Amazon, Target, and REI to an enterprise B2B fleet management platform. The work spanned competitive research, future-state strategy, and pixel-perfect UI comps and functional prototypes delivered directly to engineering.

Key Contributions

Designed a dual-personalization IA model for FordPro.com that adapted navigation from three user inputs (responsibilities, primary uses, and fleet size) applying retail personalization logic from leading consumer brands to an enterprise B2B fleet management platform.

The model informed an AI/ML content surfacing strategy built to scale across FordPro's diverse customer base, from solo fleet admins to large commercial operators. Iterative research and user insights drove validated design decisions through competitive analysis, functional prototypes, and pixel-perfect UI comps delivered directly to engineering.
Conducted research across retail and automotive sectors to validate the personalization model against industry standards, analyzing how leading consumer brands including Amazon, Target, Best Buy, and REI approach personalization at scale. Findings directly informed the navigation strategy and provided an evidence base for applying retail personalization logic to an enterprise B2B fleet context.
Delivered functional prototypes and pixel-perfect UI comps in Figma to bridge concept to engineering handoff, designing for two distinct audience types (new and existing customers) within a single adaptive system.

Pathfinder flows accommodated neurodiverse thinking patterns and multiple task entry points, and a default-to-personalization fallback ensured all users could complete tasks regardless of whether they opted into data sharing.

Strict NDAs mean I can’t share the detailed case studies from my time at Ford publicly, but I’d be happy to discuss my contributions in more detail. Reach out to schedule a conversation!

Driving Navigation Innovation

The personalization model was built on a data intercept strategy that captured profile data from existing customers at re-authentication, closing the gap between new and returning user data sets. Pathfinder flows accommodated neurodiverse thinking patterns and multiple task entry points, reducing reliance on linear navigation. A default-to-personalization fallback ensured all users could complete tasks regardless of whether they opted into data sharing.

  • Competitive research across retail and automotive sectors
  • Functional prototypes and pixel-perfect UI comps for engineering handoff
  • IA model spanning new and existing customers across a fleet ecosystem
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