Scaling UX Strategy for a Multi-Sided Logistics Ecosystem

Role: Head of UX
Period: 2022 – Present
Company: Ship.Cars
Platform Scale: 12,000+ active users
Products Led: Carrier TMS, Driver App, Shipper Dashboard, Broker Portal, Internal Tools

Keywords: Logistics UX, Multi-Sided Platforms, Design Systems, AI in UX, Enterprise SaaS, B2B Product Design

Executive Summary

At Ship.Cars, I led UX across a multi-product, multi-persona logistics platform used daily by carriers, drivers, dispatchers, shippers, and brokers. I built the UX function from the ground up, introduced a unified design system with 600+ components spanning web and native products, and established AI-supported workflows to increase speed and consistency across the design lifecycle. The work focused on operational clarity, scalable interaction patterns, and stronger trust signals as the platform evolved toward enterprise readiness.

Evidence Signals

This case study is grounded in long-term, recurring signals rather than isolated metrics:

• Product analytics and funnel behavior

• Support logs and recurring issue categories

• Usability testing and controlled experiments

• Design system adoption and delivery velocity

• Cross-product UX audits and consistency reviews

All outcomes below are framed conservatively and reflect directional, repeatable trends, not single experiments.

Impact Snapshot

Anchor metric:
Design system scale: 600+ reusable elements across web and native products

What Ship.Cars Is and Why UX Is Critical

Ship.Cars is a multi-sided logistics platform connecting carriers, drivers, dispatchers, shippers, and brokers. Users operate in time-critical, exception-heavy environments where decisions must be made quickly and often under stress.

Key insight
In logistics, unclear UX does not stay at the interface level. It propagates into operational errors, delays, support escalation, and churn.

The Challenge

As the platform expanded across products and personas, UX foundations needed to scale alongside operational complexity and rising enterprise expectations.

Core Problems Observed

• Cross-product workflows lacked shared logic and predictable structure

• UI inconsistency increased learning time and cognitive load

• Operational flows handled edge cases poorly

• Delivery speed slowed due to missing shared UX infrastructure

Who Was Most Affected

• Drivers: low attention bandwidth, variable environments, unreliable connectivity

• Dispatchers: high-volume task management and frequent exceptions

• Enterprise buyers: assessing maturity, reliability, and long-term fit before committing

Key insight
Operational users feel friction immediately. Enterprise stakeholders interpret UX quality as a proxy for platform reliability.

My Role and Scope

I owned UX both strategically and operationally across all products.

What I Owned

• UX strategy, discovery, and prioritization

• Information architecture and interaction design

• Prototyping and validation

• Design systems across web and native apps

• Stakeholder alignment at C-level

• Team building, structure, and UX process definition

AI and Advanced UX Initiatives

AI was used to accelerate research, synthesis, and design execution while all product decisions remained human-led and accountable.

AI Workstreams I Led

• UX design for AI-supported vehicle inspection flows

• AI-assisted design workflows for research synthesis, ideation, and documentation

• Prompt frameworks to standardize and speed up design tasks

• UX design for an AI voice agent supporting operational scenarios

AI Infrastructure for Design Execution

I introduced an MCP (Model Context Protocol) server integrated with Figma to support generative UI and design system workflows. This enabled designers to generate, iterate, and validate UI patterns directly within the design environment while remaining aligned with system rules and component standards.

The setup reduced repetitive design work, improved consistency during exploration, and allowed faster iteration without compromising system integrity.

Observed pattern
AI improves UX outcomes when it accelerates learning, reduces repetitive work, and supports clearer decisions. Without strong UX foundations, it amplifies noise.

Research and Discovery

Summary
Research focused on observing real operational behavior rather than validating stated preferences.

Research prioritised behavioural reality over opinion sampling. Insights emerged from observing users’ actual workflow navigation, identifying hesitation points and the emergence of errors or workarounds. Product analytics support patterns and operational feedback served as primary signals while interviews were employed to explain behaviour rather than confirm assumptions. This approach ensured decisions aligned with real logistical conditions rather than self-reported preferences.

Inputs Used

• Interviews across key personas
  Semi-structured interviews guided by JTBD and task-based walkthroughs to surface decision points, failure modes, and workarounds in real operational contexts.

• Product analytics and session behavior
  Event-based analysis using tools such as Mixpanel for funnel and path analysis, combined with session replays and heatmaps (e.g. Microsoft Clarity) to identify hesitation, repetition, and drop-off patterns.

• Support data and operational feedback loops
  Thematic analysis of support tickets and operational notes using UX debt tagging and root-cause clustering to distinguish usability issues from training or policy gaps.

• Competitive analysis across logistics and adjacent enterprise SaaS
  Structured comparative UX audits and pattern benchmarking across logistics platforms and high-maturity B2B SaaS to evaluate workflow structure, hierarchy, and error handling standards.

System-level insight
Most friction stemmed from misalignment between system logic and real workflows, not from missing features.

Strategic UX Approach

The strategy centered on three pillars:

1. Context-aware workflows

2. Cross-product consistency through systems

3. Value clarity aligned with enterprise expectations

Solution 1: Context-Aware Workflow Simplification

Summary:
Operational flows were redesigned to adapt based on role, state, and scenario, reducing irrelevant steps.

What Changed

• Conditional paths based on role, permissions, and load state

• Removal of unnecessary steps when no action was required

• Clearer feedback and error handling at decision points

Why It Mattered

• Faster completion of high-frequency tasks
  Validated through Mixpanel funnel and path analysis to measure step reduction and flow efficiency, supported by moderated usability testing focused on time-on-task and first-attempt completion.

• Fewer mistakes in exception-heavy workflows
  Identified and reduced using support ticket root-cause analysis, error-state audits, and scenario-based usability testing covering edge cases and failure paths.

• Reduced repetition and hesitation
  Observed through session replay analysis (e.g. Microsoft Clarity) and interaction pattern audits, highlighting repeated actions, backtracking, and pauses at decision points.

Solution 2: Design System and Style Guides at Scale

Summary
A shared design system became the backbone for consistency and delivery speed.

What I Built

• A design system with 600+ reusable components
  A scalable design system built from reusable components and shared tokens for color, typography, spacing, and layout. Each component included clear usage guidance, variants, and states so teams could reuse patterns confidently and avoid inconsistencies across products.

• Separate but aligned guidelines for web and native apps
  Platform-specific guidelines for web, iOS, and Android that respected native behavior while keeping a shared visual and interaction language. Differences were intentional and documented, while core patterns stayed consistent across platforms.

  • Accessibility and WCAG-aligned design standards
    Accessibility was built into components and flows where relevant, including contrast rules, touch target sizing, focus states, and screen reader considerations. This ensured usability in varied environments and supported compliance with WCAG AA expectations.

  
  

Why It Mattered

• Higher consistency across products

• Faster implementation with fewer regressions

• Easier onboarding for designers and engineers

Operational takeaway
Consistency is not visual polish. It is a performance optimization.

Solution 3: Enterprise Readiness and Value Clarity

Summary
UX changes reinforced maturity and clarified value as the platform moved toward paid enterprise usage.

What Changed

• Clearer hierarchy and messaging at key decision points

• Outcome-focused framing within workflows

• Better alignment between onboarding, usage, and upgrade intent

Why It Mattered

• Stronger confidence during evaluation

• Less friction in sales and support conversations

• Clearer perception of platform reliability

Validation and Iteration

Methods Used

• A/B experiments on UI elements and flows

• Moderated usability testing with core personas

• Cross-product UX audits to surface systemic issues

Validation focused on repeatability and clarity, not isolated wins.

Collaboration

UX decisions were shaped through close collaboration with:

• C-level leadership on strategy and priorities

• Product managers on discovery and scope

• Engineers on feasibility and implementation patterns

• Operations teams to validate real-world constraints

My role often involved translating between business, technical, and operational perspectives.

Learnings

• UX debt compounds rapidly in multi-product ecosystems

• Design systems change organizational behavior, not just UI

• AI magnifies existing UX maturity, good or bad

What I Would Improve Next Time

• Establish shared UX metrics earlier

• Formalize edge-case validation loops sooner

FAQ

Q: What did you lead

at Ship.Cars?
A: UX strategy, execution, validation, team building, design systems, and AI-supported UX workflows across multiple products.

Q: What is the most scalable outcome from this work?
A: A cross-product design system that improved consistency, delivery speed, and long-term scalability.

Q: How were decisions validated?
A: Through analytics, usability testing, controlled experiments, support data, and systematic UX audits focused on operational reality.