Microsoft Azure
Bringing AI Guidance to Cloud Workflows
Timeline
2024 - 2025
Role
Lead Product Designer
Team
Research, Engineer, PM
Tasks
AI Design, UX / UI Design
Overview
As part of improving Azure’s quota management experience, I led the integration of an AI-powered solution (Copilot) into cloud workflows, enabling users to self-troubleshoot issues and providing clearer guidance and visibility into the quota system.
Impacts
- Identified key friction points and integrated AI solutions grounded in user needs
- Increased CSAT from 25% → 75%
- Reduced quota and capacity issues by 70%
- Cut cloud workflow failures by 50%
My Role
This project builds directly on the UX strategy work outlined in the previous case study.
After identifying Copilot as a high-impact opportunity, I led the design of an AI-powered experience that helps users troubleshoot quota issues with more clarity, speed, and autonomy.
Challenge
Quota issues are consistently happening: Users had no clarity on why, or how to fix them
Data and research revealed that cloud users often struggle to understand why their workflows fail, especially when the root cause relates to quota or capacity.
Many lack visibility into what went wrong, what alternatives exist, or how to proactively prevent issues.
These gaps led to repeated delays, rework, and growing dependence on support, ultimately driving up operational costs and lowering customer trust.
Opportunity
Designing Contextual AI guidance at the point of failure
Building on prior research and workshops, I uncovered a strategic opportunity to embed Copilot at failure points in the cloud workflow, moments when users were blocked, confused, and uncertain what to do next.
What users needed was real-time, in-context guidance—right when and where issues happened.
Why I Integrated Copilot?
Users needed real-time, in-context guidance—right when and where issues occurred. Many were already relying on Copilot, and adoption was steadily growing.
They needed help that was:
- Immediate — not delayed by support tickets
- Actionable — not just error messages, but next steps
- Contextual — surfaced right when and where the issue occurred
By embedding Copilot into failure moments, we could:
- Shows real-time explanations for failures
- Recommends relevant actions like retrying or submitting a ticket
- Makes users feel guided—not stuck
Design Process
Integrating Copilot into Critical User Journeys
To provide timely, in-context guidance, I embedded Copilot at moments of highest friction where quota issues disrupted workflows the most. This made support immediate, seamless, and confidence-boosting.
01
Identify Friction Points
Grounded in the customer journey and Jobs-To-Be-Done framework, I mapped where quota-related issues blocked progress.
Outcome: Key moments identified where Copilot could add the most value.
02
Define & Validate Opportunities
Reviewed existing solutions with PMs to understand what worked, what didn’t, and where improvements were needed.
Outcome: Confirmed Copilot would have the biggest impact in pre-deployment and post-deployment.
03
Design Solutions
Designed end-to-end flows with clear guidance, actionable fixes, and context-specific support, embedded directly in the existing UI.
Outcome: Interactive prototypes ready for review.
04
Align & Prepare for Delivery
I facilitated a workshop with PMs, engineers, and the Copilot UX team to review design, align feasibility, and validate technical constraints.
Outcome: Finalized entry points, priorities, and feasibility across teams.
Outcome
Quota issues still happen — but now users know why, and how to fix them
With Copilot embedded into failure points, users now get clear explanations, actionable next steps, and visibility into the quota system.
Design solution / Copilot
Empower Confident Deployment Through AI Guidance
By integrating Copilot into the pre-deployment workflow, users gain visibility into real-time capacity, avoid failure-prone configurations, and make faster, more informed decisions all without leaving the platform.
Design solution / Copilot
