AON Fleet Risk Intelligence

Product: B2B platform for Fleet risk insights, from Brokers and insurers to large Fleets .

Role: UX Lead – strategy, research, design execution, product optimization.

Goal: Improve usability, accessibility, adapt for larger fleets and provide actionable insights.

Core Issues Identified:

❌ Complex visualizations – Fleet managers struggled to interpret data quickly.

❌ Poor scalability – Dashboard wasn’t optimized for larger fleets, limiting efficiency.

❌ Risk model shift – Transitioning to a unified model caused confusion, requiring clearer explanations.

❌ Expanded risk bands – Increasing from 3 bands (Poor, Fair, Good) to 5 bands (Low risk, Minor risk, High Risk, Severe risk).

❌ Data overload – Too much upfront information made it hard to extract key insights fast.

Discover, Define, Develop, Deliver

  • Discover and Define

    ✔ Conducted stakeholder interviews (fleet managers, insurers, analysts) , logging insights in Dovetail.

    ✔ Reviewed existing research artifacts to identify gaps in user needs and expectations.

    ✔ Analyzed competitor platforms to benchmark features and user experience.

  • Key Findings

    ➡ Time constraints: Fleet managers need quick, scannable insights over complex data. Charts must be clear, actionable, and easy to interpret.

    ➡ Progressive disclosure: Too much data overwhelmed users. Surfacing key insights first, with deeper data on demand, improved comprehension.

    ➡ Automation reduces workload: Manual reporting was inefficient. AI-generated reports and driver notificationsstreamlined communication.

    ➡ AI enthusiasm & trust: Users were eager for predictive analytics but needed transparency in AI-driven decisions.

  • Develop and Deliver

    I ran cross functional workshops involving data science, engineering and product managers with design sprints focused on.


    ✅ Simplified charts: Focus on key takeaways, reducing clutter.
    ✅ Role-based dashboards: Tailor insights for managers vs. drivers.
    ✅ AI transparency: Explain risk scores to build trust.
    ✅ Auto-reports: Deliver timely, actionable insights.

Cross functional workshops

✔ Modular customizable dashboards based on user roles.
✔ Implemented progressive disclosure UX model – surfaced key insights first.
✔ Optimized mobile experience for on-the-go access.

    • Refined color contrast, typography, and visual hierarchy to enhance data clarity.

    • Optimized layouts to reduce cognitive load and improve scanability.for all users, including those with visual impairments.

    • Implemented accessibility best practices, including colorblind-friendly palettes and screen reader compatibility.

    • Simplified interactions to ensure easy navigation and interpretation of complex datasets.

    • Conducted usability testing to assess comprehension and refine interaction patterns.

    • Gathered feedback from brokers and fleet managers to ensure alignment with real-world use cases.

    • Aligned data visualization components with Aon’s design system for consistency.

    • Ensured scalability by designing flexible components adaptable to different fleet sizes and data sets.

Benchmarking Pre-Improvement Metrics

  • Leading metrics - DAU, WAU, Reports opened by fleet managers and drivers,

  • Time to assess risk per Driver/fleet ( minutes taken to interpret dashboard insights).

  • User-reported time estimates from fleet managers (via surveys and from customer success managers).

  • Number of steps/clicks required to complete a risk assessment.

  • Lagging metrics - improvement Fleet/ Driver score

What this means in practice

  • Fleet managers used to take 5 minutes to assess risk, they now take only 3 minutes, reducing cognitive load and decision-making time.

  • The UX improvements (e.g., better visualizations, automated insights, progressive disclosure) contributed to this faster analysis.

  • Validated via usability tests, analytics (session duration, click reduction), or user feedback.

Wireframing, Prototyping, and Testing

  • Wireframing: Started with low-fidelity wireframes and sketches to quickly iterate on ideas and capture early feedback. This allowed me to explore design solutions without committing extensive time to high-fidelity designs.

  • Prototyping: Once the wireframes were validated, I created interactive prototypes to test user flows and interactions. These prototypes were designed to simulate real user scenarios, allowing for quick adjustments and optimization.

  • Usability Testing: Conducted task-based evaluations and A/B tests to compare old workflows with the new design. This helped identify pain points, fine-tune the design, and ensure the solution addressed user needs.

Completed Wireframes and Designs

  • User needs

    Utilized Aon's global design system to ensure consistency across platforms.

    Created custom components when specific platform needs arose, ensuring a balance between brand consistencyand design flexibility.

    Prioritized user efficiency, reducing cognitive load and ensuring fast decision-making.

  • Incorporating ai search.

    Key Point: Aon has its own NLM (Natural Language Model), which influenced our approach to data querying.

    Result: Users now have greater autonomy to segment and filter data as needed.

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Impact and outcomes

📊 User Success
✅ 40% faster risk analysis –risk assessment previously took 5 minutes and now takes 3 minutes.
✅ 25% increase in engagement with key insights.

🏆 Business Impact
✅ Higher platform adoption among fleet managers.
✅ Strengthened Aon’s position in B2B fleet risk intelligence.

🚀 Key Takeaways
✔ Data visualization is critical in complex platforms.
✔ User feedback loops drive engagement & usability.
✔ Cross-functional collaboration ensures scalable solutions

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