| AI Adoption, AI-augmented workflows & prototyping – Red Hat (IBM), 2025-206

OVERVIEW

Problem:Across manufacturing facilities and defense logistics networks, OT teams manage thousands of edge devices through fragmented tools, manual processes, and reactive workflows. Many operators need a simpler, more intuitive way to understand system health, identify risks, and act before issues escalate. When insight is difficult to interpret or action comes too late, operational failures follow. Large industrial facilities lose an average of 27 production hours each month, automotive downtime can cost up to $2.3 million per hour, and incident responders spend 38% of their time on manual processes, generating as much as $700,000 in annual operational toil.

Solution: An agent-led edge management platform that gives technical and non-technical operators one intelligent environment to onboard devices, monitor fleet health, manage applications, and identify vulnerabilities before they escalate into operational failures. Beneath the interface, an agentic layer continuously evaluates every connected device at 60-second intervals, detects emerging risks, initiates automated recovery, and restores affected devices to their last known secure state when predefined conditions are met. Rather than waiting for operators to respond after a disruption, the platform shifts edge management from reactive intervention to continuous prevention.

My Role and impact: Within a 15-person engineering and architecture team, I served as UX Lead, bringing enterprise UX strategy, research rigor, and scalable design practices to a $10M edge management platform. I helped shift the team from technology-led assumptions toward validated customer needs, establishing an adaptable experience architecture and reusable design foundation that enabled teams to move from early product definition to GA readiness and enterprise launch within six months. The foundation supported onboarding with customers including Eli Lilly and Lockheed Martin and was designed to scale across evolving products, AI workflows, and enterprise use cases.

My scope included:

Leading UX strategy and experience integration across Edge, Ansible, and OpenShift, connecting fragmented capabilities into a cohesive platform experience
Defining the 0-to-1 experience roadmap for a scalable, adaptable edge management ecosystem, including emerging agentic capabilities
Guiding a small design team, setting priorities and strategic direction while helping designers anticipate challenges, navigate dependencies, and make confident decisions
Shifting product decisions from technology-led assumptions to customer evidence, introducing validated needs, behaviors, and workflows earlier in the development process
Shaping the agentic experience model, defining how the system understands users, surfaces intelligence, and guides action within a clear human-in-the-loop framework
Driving cross-functional alignment across Product, Engineering, Research, and Business, translating complex technical capabilities into shared experience priorities and direction
Championing an AI-augmented, code-based design process, establishing functional, implementation-ready prototypes and tighter design-engineering collaboration

UX PROCESS

DISCOVERY

The team consisted of 15 engineers and architects with deep technical expertise, but no clear product roadmap, customer research, or shared vision for General Availability, Feb, 2026. The product was being shaped primarily around assumptions and existing technical capabilities, creating a significant risk: we could build something technically impressive that customers did not understand, value, or adopt.

I recognized a critical need to bring real customer evidence into the room. Through the Early Access Program, I recruited 10 enterprise clients across manufacturing, defense logistics, and distributed infrastructure, giving the team something it had never had before: direct insight into the people who would buy and use the product. I led research sessions and interviews with enterprise partners, including Eli Lilly, ABB, Cox Automotive, and Lockheed Martin. What we heard reframed the entire initiative. Two distinct groups were shaping the product’s success, and each needed something different.

Buyers were focused on business outcomes: reducing operational costs, strengthening security visibility, maintaining consistent performance across distributed sites, and scaling without adding headcount.
Users needed clarity and control: a fast path to connect a device, an understandable view of fleet health, and the ability to resolve a problem without needing to understand the infrastructure operating beneath the experience.

Until that point, these needs had never been brought together within a single product strategy. The research gave us the foundation to connect business value with a usable, trusted experience, and begin shaping a product that could succeed with both buyers and users.

That finding changed the strategic direction of the product. It told the team exactly where to put our energy for the first release and why. Earn trust with the core experience first. Then introduce intelligence on top of it. The 10% of customers open to AI and automation were not rejecting it. They were telling us the order of operations. Foundation before intelligence. Reliability before automation. For the first time, product, engineering, and design had a shared evidence-based picture of who they were building for and what each person needed to succeed.

Buyer personas

User personas

Through close partnership with product and engineering teams, I translated research insights into product themes, JTBDs, 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.

At enterprise scale, teams often move quickly within their own product areas, and over time it can become difficult to maintain visibility across shared customers, timelines, and dependencies. I looked inward to better understand those connections. I evaluated existing workflows, intake processes, risks, and product surfaces to identify where teams could align earlier and share more context. The findings reinforced what we had already heard from customers: the products were part of the same experience, even though they were being developed through different paths. I introduced a more connected way of working that helped teams share context sooner, understand cross-product dependencies, and make decisions with the broader customer journey in mind.

High-level

Define

The process was deliberately co-creative. Rather than presenting a finished designs for sign-off, I brought cross-functional teams into the work early, running sessions where we mapped what customers needed to accomplish, where existing flows broke down, and what a seamless experience could look like if we designed around the operator first. Every step of that end-to-end journey was validated with the 10 enterprise clients in our Early Access Program before it was built.

Device onboarding – phase 1

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

This strategic approach helped us shift the experience from a feature-led path toward a product-led growth model. The goal was to help internal teams think customer-first by enabling customers to discover value within the environments they already used before asking them to adopt more broadly or expand. To build alignment, I translated the complexity into an ideal-state customer journey and partnered with product and data teams to define signals for adoption, self-service, and upgrade readiness.

Ownership – phase 2

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

IDEATE

Device onboarding – phase 1

I encouraged my team to start with key experience touchpoints, and use AI as a way to accelerate alignment, make ideas visible earlier, and move with more confidence. I role-modeled this by implementing AI-augmented workflows myself first. I used Figma Make to generate low and mid 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.

Ownership – phase 2

After onboarding their devices, users spent most of their time in Edge Management: running applications, installing updates, and monitoring fleet health across local and global environments. Because this was a core operational surface, it required careful attention to information hierarchy, decision-making, and actionability.

The more consequential work extended across the design organization. I brought together the designers responsible for Edge, Ansible, and OpenShift to evaluate the three product experiences as one connected ecosystem, something the teams had not done before. Together, we reviewed navigation patterns, components, terminology, and interaction models to understand where the experiences aligned and where they diverged.

I established a unified information architecture, portable navigation system, and shared component foundation that could extend across Edge, Ansible, and OpenShift without disrupting the experience as users moved between products. I developed the approach collaboratively through working sessions, shared decision-making, and continuous input from each design team. The robust information architecture became the backbone of the broader platform, allowing capabilities to be introduced incrementally as each release validated new customer value.

Information architecture alignment

Deep dive (low-fidelity to high fidelity)

The vulnerability exposure experience was one example. Early low-fidelity wireframes helped us define what users needed to understand immediately, which actions required priority, and how the experience could scale as new capabilities were introduced.

High-level user flow for CVE

For example, by promoting AI-assisted exploration, I helped the design team work faster, think more systematically, and build stronger partnerships across product and engineering. This positioned design as an innovative and strategic partner, not only a delivery function. See examples below.

CVE experience (AI-augmented, low-fidelity, iteration 1)

The transition to AI-augmented, code-based prototyping was intentional and pragmatic. I continued using Figma where it created the most value for alignment, commenting, and tracking, while gradually moving the end-to-end experience into GitLab with Cursor and Claude supporting the shared technical environment.

CVE experience (AI-augmented, mid-high fidelity, iteration 2, light mode)

EXECUTE

As alignment matured, I intentionally evolved the workflow beyond Figma and introduced AI-augmented prototyping as the primary handoff tool. The goal was not to automate design, but to reduce the distance between an idea, a shared decision, and development readiness. This created a clearer path from concept to product sign-off, improved visibility across product and engineering, and contributed to an approximately 30% improvement in cross-functional delivery efficiency.

The full code-based Edge management and Edge onboarding prototypes was made available in GitLab, which allowed engineers to review the working direction directly, contribute through pull requests, and refine the prototype alongside the design team.

Edge onboarding management experience (AI-augmented, code-based)

Edge management experience (AI-augmented, code-based)

That direction did not exist in isolation. IBM and Red Hat’s combined commitment, backed by a $5 billion investment in AI and agentic capabilities, created the strategic environment that made this work possible and gave it a larger stage to land on. The platform we built was not ahead of its time. It was exactly on time, aligned to where the business was already moving and ready to grow alongside it.

Integrated across OpenShift, RHEL, and Ansible (AI-augmented, light mode)

A scalable, modular navigation and experience framework connecting Edge, Ansible, and OpenShift through unified information architecture, portable navigation, and shared components, reducing experience redundancies by 30%.

Red Hat Agent-led Edge Computing