Reimagining CI Troubleshooting with AI Dashboards (DevOps)
An AI-assisted workflow for investigating pipeline failures
Company
CloudBees (CI)
Role
Lead Product Designer
Industry
AI · DevTools · Data Visualization
Duration
3 Months
Overview
Developers using CloudBees CI were spending too much time troubleshooting pipeline failures, jumping between logs, views, and tools to piece together what went wrong.
I designed a CI-native dashboard concept that transforms this experience into a proactive, AI-assisted workflow.
Status: Validated concept. Influenced internal roadmap discussions, but not developed due to organisational changes.
The problem
Troubleshooting CI pipelines was slow and fragmented.
Developers had to:
Navigate multiple views and logs to find issues
Manually interpret pipeline data
Work with static dashboards that offered no guidance
This led to long debugging cycles, constant context switching, and slower delivery.
My role
Built on existing user research to define the opportunity
Designed and prototyped a new dashboard model
Introduced AI-driven interaction patterns
Presented and aligned the proposal with stakeholders
The solution
I reimagined the dashboard as a decision-making layer, not just a reporting tool.
1. Modular dashboard system
Customisable widgets tailored to user needs
→ Flexible visibility across pipelines and metrics
2. AI-powered troubleshooting
Natural language queries (e.g. “Where did my last pipeline fail?”)
→ Direct access to relevant logs and insights
3. Pipeline health at a glance
Clear visual indicators for risks, failures, and trends
→ Faster issue detection and prioritisation
Outcomes
Validated direction
Positive stakeholder feedback and alignment on the vision
Product influence
Contributed to roadmap conversations around CI dashboard evolution
Designs remained a reference point for future exploration
Key takeaway
Even without shipping, this project shaped how the team thought about AI in CI workflows — moving from passive dashboards to interactive, insight-driven tools.
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