project
02
AI Design to Onboard
results
Produced a Product Vision and reduced onboard flow from 65+ min to 3min in less than 2 hours using AI
increase velocity thru generative refinement
streamlinging onboarding with AI workflows
In prep for Beta Test Planning, I have been diving deep into my SaaS client's onboarding flow. Knowing that this is the most critical piece of building confidence with new users and key developing trust & retention; I wanted to provide a best in class prototype while sharing my finding in an Audit Presentation.
This opportunity gave me an window to explore how AI Design tools like Figma Make, Miro & ChatGPT can optimize my workflows and what tools are delivering the best in class solutions.
2 hrs
Audit, UXR, UX, Prototype
A KPI SaaS tool for small and medium sized business, the user flow requires many steps and data points - which means I need a streamlined ux to build trust efficiency and confidence.
My audit of the alpha-build Onboarding flow uncovered a complex multi-step, multi-entry workflow that struggled with numerous friction points and required a hugh time investment.
I wanted to see if I could dive right into a prototype, to see "what would happen" using Figma, Builder and Loveable's AI tools
I gathered some baseline requirements for myself and jumped into the mess.
The goals was craft a Best in Class Solution while reducing the amount of UX Processes and assets to communicate a Viable User Solution to my client that they would understand and see the value in.
What happened next surprised me.
Once I could imagine my users frame of mind and "act it out" ( I am in the theatre after all,) I launched Figma's Make Platform and applied one simple prompt to see what would happen.
With no PDD, Requirements or User flows - the result was a simple 5 step onboarding flow. It was also populated by universal data sets and information, relative to my clients business model.
Creating another version, I began to add Requirements and data set prompts, refining the overall user flow, personalization and UI into a holistic solution, tailored to the business model & mission.
Jumping over to Miro's AI tool I created a user flow and swimlane diagrams to support engineers understanding of the recommendations. I also double-checked the code being generated to ensure it's compatible with my clients code framework/platform.
And vetted Figma's results with Builder and Loveable; ultimately using the Figma solution.
Using Figma's Make took what might have taken me 2 weeks to down to 2 hours.
Once I had a solution to get conversations going, I then pulled a deck together with light research to validate my choices and conclusions. Highlighting a Vision, a Hypothesis, best practices and recommendation for UX & UI to consider before going into Beta Testing.
My client was thrilled.
The non-technical partner loved the protoype and my structured data-to-ui automation recommendations.
The engineering partner had buy in at the first slide and began envisioning multi-horizon strategies to implement and deploy. Moving from why it should be done to how it can be done.
