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ENTERPRISE AI · CLOUD OPERATIONS · UX STRATEGY

AI/ML-Powered Private Cloud Management

Designing the UX & strategy for an AI/ML-powered diagnostics experience inside VMware Cloud Foundation Operations — turning opaque infrastructure signals into actionable, explainable intelligence for cloud operators managing private cloud at scale.

AI/ML-Powered Private Cloud Management

Main stage live demo to customers on VMware Explore event in Las Vegas

OVERVIEW

My Role
  • Lead Product Designer & Strategist
Timeline
Jan 2022 – Dec 2023
Team
3 designers, 2 researchers, 1 PM, 6 engineers, 1 AI/ML engineer

Key Responsibilities

  • End-to-end UX strategy
  • AI interaction design
  • Workflow design & user research
  • Stakeholder alignment
  • AI Vision workshop facilitation
TL;DR

Challenge

Cloud operations teams managing VMware Cloud Foundation infrastructure had no unified diagnostics surface — they pieced together signals from disconnected tools while AI/ML capabilities sat unused because operators didn't trust outputs they couldn't explain.

Constraints

Existing VCF platform architecture and alert infrastructure could not be replaced; the AI layer had to integrate non-disruptively without exposing model internals or requiring operators to change their mental models overnight.

Success Metrics

Increase AI feature adoption from near zero to measurable daily usage, reduce mean time to diagnosis for P1 infrastructure events, and establish a design system for explainable AI interactions that the broader VCF product org could adopt.

Design Impact

A conversational AI assistant, explainable diagnostic workflows, and an AI interaction design system gave operators the confidence to act on AI recommendations — transforming passive skepticism into active reliance.

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