Redesigning Amazon Onboarding Experience
Building one framework that could grow from a small pilot to a global workforce
I created one onboarding plan framework and tested it first in Lift Off, a program with 12K+ users. The longer-term goal was to make it the standard growth plan for corporate blue badge employees across Amazon.
My role
Lead UX Designer
Timeline
Oct 2025 - Dec 2025
Team
1 designer 1 PM 10 developers
Tools
Figma UserTesting Kiro (Claude model)
Siloed tools, generic content
At Amazon, your onboarding experience depended on the org you joined. Different business units used completely separate platforms, and the content itself was often "one-size-fits-none."
01
Platform fragmentation
Different orgs used different tools: Embark for corporate, Lift Off for operations, and ASBX for others. The experience could feel completely different from one org to the next.
02
Not enough personalization
A new UX designer could open their plan and find a huge checklist of generic corporate tasks that had little to do with their day-to-day work or team culture.
Framework over platforms
Redesigning three large platforms at once was not realistic. So we focused on one standard plan framework that could work inside any of them.
Starting with a smaller pilot
I worked with the PM on a phased rollout. We started with Lift Off, which had 12K+ users, so we could test the patterns with real people before taking them global. The plan had three phases:
Phase 1
Test within LiftOff
Launch with Lift Off first and use real feedback to see whether the goal-based patterns worked before scaling them.
Phase 2
Scale to Company-Wide Blue Badge
If the pilot worked, expand the framework across Amazon and replace siloed experiences like Embark and ASBX.
Phase 3
Adapt framework to other plans
Adapt the same framework for internal transfers, promotion plans, and career transitions.
How could we bring these separate platforms into one framework and give every employee a more personal, goal-based journey?
Taking a closer look at the current experience
Once we agreed on the rollout, I took a close look at the current onboarding journey: how it organized information, where people got stuck, and what was not working for them.
Problem 01
Lack of customization
A new UX designer and a developer received the same generic corporate tasks, even though their day-to-day work was very different.
Problem 02
Information overload
New hires faced a huge "wall of text" full of generic tasks, so the role-specific information was hard to find.
Problem 03
Static deadlines
Plans were locked to dates like Day 1 and Week 1, with no room for different role needs or team learning curves.
Improvement #1: modular learning chapters
To make the plan less rigid, I shifted the focus from time spent to goals completed. That led to a modular structure with three clear benefits:
01
Outcome-Based Chapters
I replaced calendar weeks with themed chapters, such as Understanding Culture and Understanding Your Team, so each section had a clear purpose.
02
Scalable framework
Using "Goals" as the container meant the same structure could work for any role, from corporate UX designers to operations leads.
03
Dynamic personalization
Managers could filter out the noise, so employees only saw content that supported their specific growth goals.
How does it work?
The framework breaks a plan into goals and smaller learning chapters. Teams can then place the same structure inside their existing platform and tailor the content to each role.
Improvement #2: The layout pivot
Modular chapters also meant fitting more information on the page. I tested two layouts to see which one made the tasks easier to browse and complete. The result challenged my first assumption.
A
Variant A (The Sequential List):
A simple list where each task opened in a new tab or window, keeping the main page clean.
B
Variant B (The Master-Detail Layout):
A split-screen dashboard with sticky milestone navigation on the left and the selected task open on the right.
Using AI to shape the research plan
Once I had the two directions, I used Kiro to turn my early assumptions into a usability plan before I recruited participants.
01
From assumptions to testable questions
Kiro helped me question what I thought each layout solved and turn those assumptions into neutral research questions.
02
Coverage before sessions
I used it to test the task scenarios, catch edge cases, and make sure both versions covered navigation, comprehension, and task completion.
03
I still made the final calls
I edited the language, set the success criteria, chose the final tasks, and ran the study. AI made planning faster, but it did not decide what counted as evidence.
User research insights
I expected Version A to win because it looked lighter, but users clearly preferred Version B, the Master-Detail layout.
After the sessions, I used AI to group repeated behaviors and organize the findings for stakeholders. I checked every theme against my raw notes before making a recommendation.
78%
Participants preferred variant B
Opening tasks in separate windows made people lose their place and click back and forth. The Master-Detail layout let them browse and complete goals in one workspace.
89&
Participants liked the modular chapters
Breaking the plan into chapters made the content easier to scan and helped new hires find information that mattered to their role.
#1 Master-detail dashboard
I designed a split-screen experience that keeps the full journey on the left and opens the selected task on the right. People can read, complete, and check off tasks without losing their place.
#2 Modular growth chapter
Instead of one overwhelming wall of text, the content is grouped into clear chapters like "Understanding Your Team" and "Operational Deep-Dive."
#3 Personalized learning
The system builds each chapter around the employee's role, level, and region, so they get tasks that are actually relevant to them.
What happened in the pilot
We soft-launched the experience with 46 new hires across 12 milestones and tracked how they used it. Engagement moved well beyond the previous 60% completion baseline.
01
Deep engagement
32 employees completed more than 90 specialized activities, and 24 went past 100 in a short period of time.
02
Well above the baseline
The pilot group completed more activities, and completed them faster, than the previous 60% benchmark.
Back-end framework design
To make the experience work across the company, I also designed a four-step setup flow for managers and program leads. They can group tasks, set due dates, and send custom plans to specific roles or regions without asking engineers to change the code.
Ready to scale globally
This setup tool has not launched yet, but the system work shows how the framework can scale in practice. It gives teams the backend structure they need to bring the same goal-based experience to Amazon's global workforce.
