Overview

Problem: Customers rely on Red Hat technologies such as RHEL, OpenShift, Ansible, and Lightspeed, to run critical production environments. When small or large updates create instability, the impact can break customer trust, disrupt systems they have spent years building, and increase the pressure on support teams. With support tickets rising 20% year over year and $2M in business lost, the issue became too significant to treat as isolated release problems. Teams needed an intelligent way to detect release risk earlier, understand what was driving it, and take action before customers were affected.

Solution: A unified operational intelligence experience that brings the most meaningful release, product, engineering, customer data signals, and SDLC workflow into one place. By leveraging AI-detected predictive analysis, the experience helps teams identify emerging release risks, understand why they matter, and take the right action before issues reach customers.

My Role & Impact: I introduced two key changes to the initiative.

• First, I established a UX-led product discovery, planning, and definition process, shifting the team from technology-driven assumptions to clearly defined user problems, role-based needs, and experience requirements.
• Second, I implemented a co-creation model supported by a shared, hosted environment and code-based, AI-augmented design approach, replacing static handoffs with working, implementation-ready prototypes developed in close collaboration with engineering.
• Third, I introduced a human-in-the-loop agentic AI experience that operates as a strategic partner while preserving human authority through a clear explain, suggest, and act control model.

These changes transformed an abstract AI initiative into a validated product foundation, enabled adoption across the first 25 teams, created a scalable path to support over 100 teams, and contributed to improved release-risk visibility, a 30% increase in delivery efficiency, and $1M in projected savings.

UX Process

Discovery

The first significant change I made: introducing UX into product planning

Red Hat is known for its open-source, engineer-led culture. Because UX was new to this group, I saw an exciting opportunity to introduce Design Thinking into the team’s planning and decision-making process. I established collaborative working sessions and facilitated a cross-functional workshop to map the existing SDLC and release workflows, identify the highest-value opportunities, and align the team on where AI could support earlier risk detection before solution design began.

Note: AI-generated and modified to remove or anonymize confidential information.

Learnings

Through these working sessions and workflow-mapping activities, we identified two distinct needs:

• Team level: 9,000 engineers, ~47% of employees: Engineers and team leads needed early, explainable visibility into release risks, supported by evidence, severity, confidence, recommended actions, and human control.
• Product level: 500 senior leaders, ~3% of employees: Leaders needed cross-team intelligence to identify recurring risks, technical debt, testing gaps, unstable dependencies, ownership issues, and where AI required better context.
• Both level: all 19,000 employees: Individuals needed AI to explain risks, recommend solutions, and support approved actions while preserving human authority to accept, defer, escalate, or resolve.

Note: AI-generated and modified to remove or anonymize confidential information.

The second significant change I made: bringing the customer into product definition

I saw a meaningful opportunity to bring UX principles directly into the PRD and grooming sessions for the initial launch. By consistently grounding discussions in the customer problem and intended experience, I influenced how product requirements were structured. The team began defining the problem statement, proposed solution, user needs, and acceptance criteria before moving into technical implementation. For many engineers, this introduced a new way of working, starting with the experience and then determining how technology should support it.

DEFINE

By advocating for UX best practices, I translated complex interactions and journeys into clear artifacts that helped engineers and leaders align, engage, and move forward together. These artifacts emphasized the importance of a human-in-the-loop framework and continuous feedback across AI signal quality, engineering trust, repository health, release readiness, and team adoption. They also demonstrated how earlier detection and resolution of release risks could help teams prevent defects, regressions, and release failures from reaching customers.

The third significant change I made: making agentic AI tangible 

Rather than discussing agentic AI experience journey only as a future capability, I used available tools to prototype how it could work within the actual product experience. The prototype gave Product, Engineering, and leadership a tangible model they could evaluate, challenge, and build upon before committing to implementation.

I designed the experience around a human-in-the-loop explain, suggest, and act model that preserved transparency, user control, and accountability:

• Explain: The agent first diagnoses the flagged issue and adapts the explanation to the user’s role, strategic and outcome-focused for leaders, or technical and detailed for contributors. It establishes understanding before proposing an action.
• Suggest: The agent presents possible next steps as recommendations rather than predetermined decisions. It communicates confidence and uncertainty clearly, and asks for clarification when it does not have enough information to proceed responsibly.
• Act: Only after explicit human approval does the agent execute the selected action through Jira, GitLab, or Slack. It then reports what it completed, what changed, and where the user can review the result.

The prototype became a pivotal buy-in moment. It moved the team from abstract conversations about AI to a concrete, testable experience that Product, Engineering, and leadership could evaluate together. By making the agent’s behavior, human controls, and system interactions visible, it reduced ambiguity, built confidence in the direction, and created alignment around an incremental implementation path, starting with explain, expanding into suggest, and introducing act only when the necessary integrations, permissions, and safeguards were in place.

Iterate 

To accelerate learning and create stronger alignment early, I used Figma Make to explore initial concepts and bring the team into the process through rapid feedback. I then advanced the strongest directions into working prototypes in Cursor, allowing us to test the experience in more realistic scenarios, refine interactions with engineering, establish reusable UI patterns, and create a clearer path from concept to implementation.

Admin view (AI-augmented, iteration 2, light mode)

Contributor (AI-augmented, iteration 2, light mode)

AI agent (AI-augmented iterations)

Validation

Testing with AI-augmented rapid prototyping revealed two clear role-based needs:

• 98% of leaders and administrators wanted a high-level view of product performance, team activity, emerging risks, and areas requiring immediate attention.
• 75% of contributors wanted detailed team-level insights, including AI-predicted failure analysis and visibility into whether recommendations were accepted, rejected, or ignored.

These findings shaped role-based detailed dashboard views, progressive levels of data, and permission controls across product, team, and repository levels.

 

As the experience matured, backend integration became a critical part of the strategy. The value of Unified Operational Intelligence depended not only on the interface, but also on how effectively data, workflows, risk signals, and insights could be orchestrated behind the scenes and surfaced at the right moment to support better decisions.

Two tiger teams evaluated the integration options through proofs of concept. My team and I compared Grafana with a more customized n8n-based approach.

• Grafana offered established dashboards, widgets, and monitoring capabilities, but provided less flexibility to shape workflows, interaction patterns, and insight delivery around role-specific needs.
• n8n, on the other hand, gave us greater control over how data was connected, filtered, calculated, and automated, making it better suited to a more personalized and adaptive experience.

 

 

The prototype became a working conversation tool that helped teams evaluate and shape the future experience. I built it in Cursor, hosted the code securely in GitLab, and integrated n8n to bring real data into realistic end-to-end scenarios, from detecting a release risk and reviewing evidence to selecting an action and tracking resolution.

I invited engineering partners into the GitLab project and assigned Developer or Owner permissions based on their responsibilities. This shared environment allowed design and engineering to collaborate directly using Cursor and Claude, accelerate decisions, improve implementation consistency, and create a clearer path to production.

Design-to-Engineering hand-off

This work led to a new AI-augmented delivery model that removed the traditional gap between design and implementation. Because the experience was built directly in GitLab, connected to n8n, and shaped with engineering involved throughout, design decisions and technical execution evolved within the same environment.

AI-augmented prototyping

Engineers had the appropriate Developer or Owner access to review, refine, merge, and deploy changes in real time. Instead of translating static design files through a separate handoff phase, the team worked from a shared source of truth. This preserved design intent, reduced implementation loss, accelerated iteration, and moved validated experience decisions directly toward production.

Impact