OVERVIEW

This initiative focused on transforming Red Hat’s edge capabilities into a new agent-led edge computing management experience for operational technology teams working across distributed, high-complexity environments.

Red Hat had strong technical foundations across OpenShift and Ansible, but the opportunity was to transform those capabilities into a cohesive, agentic AI-led product experience: one that could reduce operational complexity across global edge environments, guide users toward the right actions, and establish a market-ready edge management offering.

Through GA 1.0, I helped shape and deliver a new Red Hat edge offering that connected infrastructure, automation, and application management into a more integrated customer experience. The solution supported emerging enterprise use cases across industrial, drone, marine, and distributed operational environments, while creating a scalable foundation for future edge application management capabilities.

CHALLENGE

Edge computing environments are complex by nature. They often involve distributed infrastructure, limited connectivity, hybrid environments, strict operational constraints, and teams that need reliable systems without unnecessary complexity.

The product had to serve customers working in high-stakes operational environments, including manufacturing, industrial systems, drone operations, marine contexts, and distributed enterprise infrastructure. These users needed to manage edge applications efficiently without being slowed down by overly technical workflows, unclear handoffs, or fragmented product experiences.

The challenge existed on two levels.

• Externally, customers needed a clearer way to manage devices, applications, security, monitoring, and operational status across distributed environments.
• Internally, cross-functional teams needed better alignment around customer priorities, launch-critical capabilities, product sequencing, design standards, and delivery expectations.

The deeper challenge was to create clarity at multiple levels: clarity in the product experience, clarity in the team’s delivery process, clarity in customer priorities, and clarity in how AI could responsibly accelerate learning, alignment, prototyping, and execution without replacing design judgment.

MY ROLE

I stepped in as the UX Architect responsible for turning early product ambiguity into a clearer path to GA 1.0.

The work required progress on three layers at once:

1. Product clarity
I leveraged research insights and SME input to define the people, tasks, and outcomes the product needed to support. This included key personas, jobs to be done, underserved user needs, and the distinction between buyer priorities and daily user needs.

2. Delivery structure
I strengthened how teams moved work forward by establishing design standards, handoff expectations, roadmap inputs, and clearer alignment across integrated workstreams. This helped reduce fragmentation and gave product, engineering, business, research, and design a more consistent way to make decisions.

3. AI-enabled acceleration
I applied AI-assisted methods to synthesize research, refine requirements, generate low-fidelity wires, compare design directions, create stakeholder communication materials, and clarify phased recommendations. This helped the team move faster without losing customer context or product discipline.

My role was not only to shape the UX architecture. It was to improve the system around the work: how teams understood the customer, prioritized the roadmap, aligned across functions, and delivered a stronger product experience for GA 1.0.

STRATEGY

I approached this work as both an enterprise product strategy challenge and an experience orchestration challenge.

The product had strong technical depth, but the strategy needed to shift from technology-first execution to customer-first decision-making. In a highly engineering-driven, open-source environment, my focus was to bring product, engineering, business, and design together around customer evidence, shared priorities, and a clearer path to delivery.

My strategy centered on four areas:

1. Shift the conversation from capability to customer value
I leveraged research insights, SME input, and early documentation to help teams understand the people behind the product: what they needed to accomplish, where they experienced friction, and which outcomes mattered most. This helped move decisions away from opinion-based prioritization and toward customer-centered evidence.

2. Create alignment before solutioning
I socialized findings early, prepared teams for workshops, and created shared artifacts that clarified the problem space, personas, jobs to be done, mission, scope, and desired experience. This helped cross-functional teams align before moving into deeper design and delivery decisions.

3. Use AI to accelerate strategy and execution
I leveraged AI as a leadership and workflow accelerator across research synthesis, roadmap creation, feature refinement, cross-functional alignment, prototyping, testing, and delivery excellence. AI helped the team move faster, compare directions, refine requirements, and clarify phased recommendations without losing customer context or product discipline.

4. Build a phased path from GA scope to future-state experience
I empowered the team separate core capabilities from complementary enhancements, define what needed to launch first, and identify how the product could mature over time. This gave the roadmap a clearer structure and helped teams move from broad product complexity into an achievable delivery path.

This approach helped reduce ambiguity, strengthen design decisions with customer insight, and establish a more customer-centered way of working. Over time, it shifted the team from scattered technical execution toward a clearer, evidence-based strategy for delivering a best-in-class edge management experience.

UX PROCESS

DISCOVERY

Discovery focused on understanding both the customer ecosystem and the internal operating model needed to deliver the product successfully.

I evaluated customer needs, product strategy, team workflows, intake processes, and cross-functional handoffs to identify where friction was slowing clarity, consistency, and delivery. These insights helped shape a stronger future-state approach for how teams aligned, prioritized, and moved work forward.

This work also clarified the difference between buyers and daily users, which became foundational to our personas, jobs to be done, roadmap priorities, and overall experience strategy.

Through research and conversations with Eli Lilly, ABB, Lockheed Martin, and other interested partners, I identified two connected but distinct needs. Buyers were focused on business value: reducing cost, saving time, improving consistency, strengthening security visibility, and scaling operations across distributed environments. Users needed clarity in the daily experience: onboarding devices, monitoring health, identifying what needed attention, and acting quickly when edge devices required support.

I translated these insights into persona artifacts across four buyer stakeholder groups and three core user roles: Admin / System Integrator, Operator, and Viewer.

These artifacts gave product, engineering, business, and design teams a shared understanding of who we were serving. They also helped us define jobs to be done, prioritize must-have capabilities, and shape an incremental delivery model from GA launch toward future-state experience improvements.

DEFINE

I turned customer research and early discovery into a clear product structure that helped the team move from ambiguity to roadmap alignment.

I clarified the problem statement, mission, desired experience, scope, and incremental path forward. This gave product, engineering, business, and design teams a shared understanding of what we were solving, why it mattered, and how we would get there in phases.

I created personas and jobs to be done to define who we were designing for and what outcomes mattered most. From there, I helped separate must-have capabilities from good-to-have enhancements, which aligned the experience scope with the product roadmap and reduced the risk of scattered feature delivery.

Through close partnership with product and engineering teams, I translated research insights into product themes, prioritized capabilities, and phased experience recommendations. This helped the team understand what needed to launch first for GA 1.0, what could evolve next, and how each release could move us closer to the desired future-state experience.

IDEATE

I encouraged my team to use AI as a way to accelerate alignment, make ideas visible earlier, and move with more confidence.

I role-modeled this by implementing AI-enabled workflows myself first. I used AI to generate low-fidelity wireframes, compare design directions, clarify requirements, and translate abstract product goals into tangible experience options. This helped the team see how AI could support better thinking, not replace design judgment.

EXECUTE

I helped establish a stronger design delivery model while staying hands-on where the pace and capacity required it.

As UX Architect, my role was not to own the development process end to end, but to guide how the experience moved from concept to product alignment and development readiness. I role-modeled the use of low-fidelity wires, AI-enabled prototypes to show the team how to create alignment earlier and reduce ambiguity before moving into high-fidelity design.

I introduced practical best practices for when and how to use each artifact:

• Low-fidelity wires to align on structure, scope, and user flow.
• AI-enabled prototypes to explore interactions, component behavior, and experience variations faster.
• High-fidelity designs to finalize content, accessibility, visual polish, and design system alignment.
• Dev-ready handoff to document final decisions, approvals, and implementation details in Jira.

This approach created a clearer path from idea to product sign-off to development readiness. It improved visibility for product and engineering partners, helped designers stay focused on the right level of fidelity at the right time, and improved delivery efficiency by approximately 30%.

Note: The full AI-enabled prototype is available in GitLab and can be accessed here.