Designing an Intelligent Provisioning Router for Red Hat Enterprise Linux

Executive Overview

Red Hat's digital ecosystem serves millions of enterprise technology professionals evaluating hybrid cloud infrastructure, operating systems, containers, and cloud-native platforms.

One of the most critical entry points into that ecosystem is Red Hat Enterprise Linux (RHEL), where prospective customers must determine how to evaluate, install, or deploy the platform.

The existing experience relied on a static repository of deployment options internally referred to as the "Table of Doom" — a dense collection of downloads, installation methods, architectures, and provisioning pathways that forced users to decipher complex technical decisions without guidance.

The result was decision paralysis, abandoned evaluation journeys, and significant leakage throughout the acquisition funnel.

Role & Team

Role: Principal UX Product Designer

Cross-Functional Partners: Amanda Berneburg, Jess Sturgis, Dan Caryll, Liz Wood, Hadley Million, Digital Experience teams, Engineering stakeholders, and Product Leadership.

Platforms: RedHat.com, Developer Portal, Customer Portal, Product Trial Experiences, and Enterprise SaaS Ecosystems.

The Problem

During discovery, it became clear that the challenge extended beyond content organization.

The platform lacked an intelligence layer capable of translating user intent into meaningful outcomes.

The experience functioned as a digital brochure rather than an interactive decision-support system.

Users were expected to independently determine:

• Which deployment method they needed
• Which architecture they should select
• Whether to use cloud images, ISOs, or containerized environments
• Whether they should begin with an interactive learning experience instead

Every decision increased cognitive load and reduced the likelihood of successful activation.

Discovery & Systems Mapping

I mapped the complete evaluation ecosystem and uncovered substantial fragmentation across multiple product experiences.

What initially appeared to be a single download experience was actually composed of numerous disconnected pathways serving different audiences.

The ecosystem contained:

• Developer program download flows
• Enterprise customer portal download flows
• Interactive learning environments
• Cloud marketplace provisioning experiences
• Image Builder workflows
• Traditional ISO installation pathways
• Trial environments
• Sales-assisted evaluation channels

Each operated independently with limited orchestration between systems.

Key Insight

Users were not arriving with deployment-format questions.

They were arriving with goals.

The business was presenting technical outputs before understanding user intent.

This became the foundational design principle for the project.

Instead of asking:

• Do you want a boot ISO?
• Do you want Image Builder?
• Do you want a cloud image?

The system should first ask:

• Are you here to learn?
• Are you evaluating a deployment?
• Are you building cloud infrastructure?
• Are you preparing for production?

Engineering Constraints

The long-term vision required dynamic personalization and intent-based orchestration.

However, discovery revealed a significant technical limitation within the existing Adobe Recommendations implementation.

The platform could personalize content using predefined audience segments but could not dynamically generate recommendations based on real-time user input.

To overcome this constraint, I partnered with engineering stakeholders to define a phased delivery approach.

The initial MVP utilized client-side JavaScript logic to power routing decisions while avoiding major backend dependencies.

This allowed us to validate the experience model immediately while establishing a pathway toward a future AI-assisted orchestration framework.

The Solution

I designed an Intelligent Provisioning Router to replace the static download experience.

Rather than overwhelming users with deployment options, the experience guides users through a lightweight decision flow that progressively narrows available choices.

The interaction model shifts from:

Browse → Compare → Guess → Download

to:

Intent → Qualification → Recommendation → Activation

Progressive Decision Architecture

The provisioning router introduces a lightweight "Micro-Wizard" that gathers context before presenting deployment options.

Users first identify their objective:

• Learn RHEL
• Build a Cloud Image
• Install Locally
• Evaluate for Production

Additional questions then refine recommendations based on environment, architecture, and deployment goals.

The system dynamically suppresses irrelevant choices and surfaces only the most appropriate pathway.

Managing Edge Cases

The experience was designed to support ambiguity rather than punish it.

When users were uncertain about their objectives, the router presented a "Help Me Choose" pathway that guided decision making through contextual recommendations.

Authentication barriers were strategically deferred until after routing decisions were completed.

This prevented users from encountering account creation requirements before understanding the value of the destination experience.

The result was a lower-friction acquisition journey with fewer premature exits.

Strategic Vision

The provisioning router was intentionally designed as the first phase of a broader Experience Orchestration framework.

The long-term roadmap expanded beyond static routing into conversational recommendation engines capable of generating personalized provisioning pathways in real time.

To support executive decision making, I created dual-track concepts demonstrating:

• Near-term personalized orchestration experiences
• Future-state AI-generated experience models

These concepts helped establish alignment around a unified experience strategy across multiple business units.

Business Impact

The new experience architecture was designed to eliminate Day 0 abandonment and accelerate time-to-value for prospective customers.

Expected outcomes included:

• Improved routing accuracy
• Reduced time-to-start
• Higher activation completion rates
• Reduced support burden
• Increased qualified evaluation traffic

Most importantly, the solution transformed the platform from a passive content repository into an active decision-support system.

Organizational Impact

Beyond customer outcomes, the project became a catalyst for broader organizational alignment.

Historically disconnected teams were forced to converge around shared definitions of user intent, evaluation pathways, and activation success.

The initiative ultimately influenced a larger Global Experience Orchestration strategy that continues to shape how Red Hat approaches customer acquisition and product evaluation experiences.

Reflection

This project reinforced an important principle of enterprise UX:

Users rarely need more choices.

They need better guidance.

The most impactful design work often involves creating the logic layer between user intent and system complexity.

By introducing intelligent routing and progressive decision architecture, we transformed an overwhelming product catalog into an experience that actively helped customers achieve their goals.