A cross-platform onboarding and guidance system that helps customers discover, understand, and successfully adopt Alexa+’s growing set of generative and task-based features from their very first interaction.
Launch Year
2025
Platform
Mobile, web, multimodal
Role
Senior Product Designer
Outcome
Spark is a foundational experience for Alexa+, designed to guide customers the first time they interact with a new Alexa+ feature. As Alexa+ expanded beyond simple commands into a large ecosystem of generative and task-oriented capabilities—ranging from content creation to communication and account-connected workflows—customers increasingly needed help understanding what features exist, how they work, and how to get value quickly.
This challenge became more pronounced as Alexa+ introduced complex tools such as Create with Alexa, Texting, and linked account features like Calendar and Email, each requiring new mental models, setup steps, and cross-device coordination. Without thoughtful onboarding, even powerful features risked feeling opaque or intimidating.
Spark introduced a consistent but flexible bookend model—intro, setup (when required), and outro—that wraps around any Alexa+ feature. Rather than overwhelming customers with full tutorials, Spark adapts to user intent and context, offering just enough guidance to help customers succeed, then stepping aside. The system is designed to scale across dozens of features while remaining skippable, respectful of intent, and supportive of both novice and experienced users.
My Role
I was a Lead UX Designer for Spark on Alexa+, working closely with two other UX designers, product managers, and engineers within a lean, startup-style team embedded in the broader Alexa organization.
While design ownership was shared, I led the definition of Spark’s core experience model—shaping interaction principles, reusable primitives, and cross-device patterns that power first-time feature experiences across Alexa+. I partnered closely with PMs to translate research into product strategy and prioritization, and with engineering to ensure Spark could scale horizontally across many features without becoming a maintenance bottleneck.
Because Spark functions as a horizontal platform rather than a single feature, my role focused heavily on system-level UX design: creating guardrails that allow consistency across Alexa+ while still supporting the unique needs of features like generative creation, messaging, and account-based services.
My Design Focus
Making a growing feature ecosystem learnable
Alexa+ includes a wide and evolving set of capabilities—many of which are powerful but non-trivial. Customers are not just learning one feature; they are learning an ecosystem.
Spark was designed to act as a guide across this complexity, helping customers understand what a feature does, why it’s useful, what’s required to get started, and what they can try next. This was especially important for advanced tools like Create with Alexa, Texting, and Calendar and Email linking, where success depends on understanding the setup involved.
Giving customers control over how and when they learn
Research showed that customers strongly prefer control over their learning experience. Some want to complete a task immediately; others want context first.
Working closely with research and PM, I helped shape Spark to be opt-in in spirit: always dismissible, clear about its purpose, and flexible in depth. Customers can skip intros, defer setup with “save for later,” ask questions mid-flow, or return to a Spark later. This autonomy reduced friction and helped Spark feel like a helpful guide rather than a forced tutorial.
Designing setup for confidence, not friction
Setup is one of the biggest barriers to feature adoption—especially for messaging and account-linked features.
I focused on making setup feel achievable and transparent by clearly communicating what’s required, how long setup will take, and where the customer is in the process. Pause, resume, and reminder patterns allow customers to step away without losing progress, while consistent Echo Show and mobile guidance prevents confusion during cross-device handoff. Visual confirmation and progress indicators reinforce confidence throughout.
How this work affects my design approach
Spark shows that as AI products grow more powerful—and more complex—learning becomes a core UX problem, not an edge case. This work reflects my approach to large-scale AI systems: designing frameworks that respect user intent, scale across features, and make advanced capabilities feel approachable.
I bring deep experience designing platform-level UX in cross-functional environments, translating research into scalable systems, and earning alignment across design, product, engineering, and leadership to move complex AI initiatives forward.