Collaborative AI for large scale network management

My Role & Contribution

UX Concept Lead

Aligned stakeholders and defined a vision for explainable AI across the product.

User Advocate

Collaborated with research teams and synthesized findings into actionable UX framework.

Systems Designer

Built prototypes and logic flows for AI suggestions, mapping, and alerting.

The Challenge

Despite robust AI capabilities, the original product struggled with opaque decisions, overwhelming data, and poor UI scalability.

Goal: Transform complex network management into an intuitive, efficient, and proactive experience by leveraging contextual AI, ensuring users can manage large-scale networks with confidence and clarity.

Strategy

Vision

Transform complex network management into an intuitive, efficient, and proactive experience by leveraging contextual AI, ensuring users can manage large-scale networks with confidence and clarity.

Business Goals

  • Reduce network downtime and associated costs.

  • Enhance operational productivity through AI-driven insights.

  • Position the product as a market leader in AI-powered network solutions.

User Goals

  • Simplify complex tasks and workflows.

  • Gain transparency into AI decision-making processes.

  • Customize AI interactions to fit specific operational needs.

Key UX Objectives

  • Simplify Complexity: Design intuitive interfaces that reduce cognitive load.

  • Enhance Transparency: Make AI processes visible and understandable.

  • Promote Customization: Allow users to tailor AI functionalities to their workflows.

Strategic Initiatives

  • User Research: Conduct in-depth interviews and usability testing to understand user pain points and needs.

  • Design System Development: Create a consistent design language that supports scalability and clarity.

  • AI Interaction Framework: Develop guidelines for AI interactions, ensuring they are contextual, transparent, and customizable.

  • Prototyping & Testing: Build interactive prototypes to validate design concepts and gather user feedback.

Metrics for Success

  • Reduction in task completion time.

  • Increase in user satisfaction scores.

  • Adoption rate of customizable AI features.

Process

Research

Architecture

Ideation

Prototyping

Testing

Process

Research

Architecture

Ideation

Prototyping

Testing

Process

Research

Architecture

Ideation

Prototyping

Testing

Result

Contextual AI Assistant

Created transparent, embedded AI interactions.

Node-Based Navigation

Designed scalable UI for navigating complex systems.

AI Recipe Builder

Designed editable logic flows for advanced users.

3-Step AI intervention:

  1. Perception

Detection
Playback
Summary
Explanation

  1. Prediction

Future impact
Summary
Explanation

  1. Decision

Solution generation
Explanation
Adjustability
Upsale

Perception

Detection
Playback
Summary
Explanation

Prediction

Future impact
Summary
Explanation

Decision

Solution generation
Explanation
Adjustability (recipe entry)
Upsale

Result

Impact

  • Reduced downtime

  • Flattened learning curve

  • Improved trust and workflow scalability

  • Increased NPS over 10%