How Do You Transform Complex Risk Data Into Actionable Business Intelligence?

How Do You Transform Complex Risk Data Into Actionable Business Intelligence?

How Do You Transform Complex Risk Data Into Actionable Business Intelligence?

Beroe's Supplier & Category Watch pages serve as mission-critical tools for businesses monitoring supplier and category risks across their supply chains. However, the existing interface created significant barriers to effective risk management.

Users struggled with disjointed navigation across multiple tabs and filters, overwhelming data presentations that obscured critical insights, and a lack of actionable recommendations that forced manual interpretation of complex risk scenarios. Additionally, Abi - Beroe's AI assistant, remained underutilized despite its potential to provide contextual guidance and premium feature recommendations.

Users struggled with disjointed navigation across multiple tabs and filters, overwhelming data presentations that obscured critical insights, and a lack of actionable recommendations that forced manual interpretation of complex risk scenarios. Additionally, Abi - Beroe's AI assistant, remained underutilized despite its potential to provide contextual guidance and premium feature recommendations.

Users struggled with disjointed navigation across multiple tabs and filters, overwhelming data presentations that obscured critical insights, and a lack of actionable recommendations that forced manual interpretation of complex risk scenarios. Additionally, Abi - Beroe's AI assistant, remained underutilized despite its potential to provide contextual guidance and premium feature recommendations.

The business need was clear: transform a complex, fragmented risk monitoring experience into an intuitive, data-driven decision-making platform that could deliver immediate value while showcasing Beroe's advanced capabilities.

Previous State

User Journey Architecture & Information Hierarchy

Completely restructured the user journey to prioritize critical information through a logical flow that answers four fundamental questions:

  • What is happening? (Overview & General Insights)

  • Why is it happening? (Root cause analysis)

  • What has it done? (Impact assessment)

  • What can I do about it? (Actionable recommendations)

This approach reduced cognitive load by organizing content into intuitive modules and eliminating excessive scrolling and navigation between disparate interface elements.

Strategic AI Integration

Repositioned Abi(Beroe Live AI assistant) as a central feature throughout the interface rather than a peripheral tool.

Risk Data & Factors Reimagined

Dynamic visualizations and narrative-driven insights transform complex data into intuitive stories that users can understand and share.

Bento Framework Implementation

Adopted a Bento-style layout strategy focused on progressive disclosure:

  • At-a-glance overview: Critical information presented immediately upon landing

  • Expandable detail drawers: Deep-dive capabilities without losing context

  • Modular information architecture: Flexible content blocks that adapt to user needs

  • Visual hierarchy: Clear distinction between summary insights and supporting data

This framework ensures users can quickly assess high-level risk status while maintaining access to comprehensive details when needed.

Layout
Drawer

Final Design

Final Design

Reflections & Conclusion

Information Architecture as Strategy: The most significant improvement came from restructuring information flow around user decision-making patterns rather than technical data organization. By answering "what," "why," and "what next" in sequence, we transformed data consumption into actionable workflows.

AI Integration Requires Intentional Design: Simply having AI capabilities isn't enough—strategic placement and contextual relevance determine adoption. Abi's success relied on appearing at moments of user uncertainty with specific, valuable recommendations.

Progressive Disclosure Reduces Complexity: The Bento framework proved that showing less initially can reveal more value. Users gained confidence when they could quickly understand the big picture before drilling into specifics.