2023
Intuit Account Recommendation Redesign Using AI/ML Innovation
As a Senior Product Designer on Intuit’s Identity Platform, I spearheaded the launch of our first AI Machine Learning model that powered more personalized, intelligent customer experiences. By analyzing account activity and refining how tasks were framed and the illustrations associated, we reduced frustration from repetitive prompts and increased engagement. The launch drove a 5% lift in task completion.
The Intuit Identity Platform
The Intuit Account Manager platform receives 21 million annual visits, empowering customers to manage their data across various Intuit products they use. It serves as a collaborative platform where identity management capabilities are contributed to provide customers with functionalities such as managing payment methods, sign-in and security, profile management, and more.
Business Problem
How might we use AI to help customers complete the right tasks at the right time?
Intuit is prioritizing an AI-driven future, encouraging designers to identify opportunities where intelligence can enhance customer experiences. This shift enables the Platform team to move from rigid, rule-based logic to adaptive, data-driven models that learn from real behavior—allowing recommendations to evolve rather than remain fixed.
Customer Problem
I want to see the right action at the right time
“As an Intuit customer, I receive important task recommendations to complete, but I don’t always take action. I see them repeatedly until I finally dismiss them, which makes me feel forced to click on something I have no interest in or may have completed already, so I end up seeing this as annoying and aggravating than something of value to me.”
Design Deliverables
Designed New Account
Recommendations
To evaluate the impact of machine learning on the Intuit Account recommendation experience, we identified a high-value identity task to redesign and measure. By analyzing customer account behavior, we selected the “Confirm your phone number” recommendation as a strategic pilot.
This task plays a critical role in account security, recovery, and user trust, making it an ideal test case to improve relevance, clarity, and completion rates using ML-driven insights.
By partnering with research, content, and analytics, I led the redesign of account-recommendation experiences to make them smarter, timely, and more meaningful—moving away from generic prompts and towards personalized insights.
Takeaways
This project marked Intuit’s first foray into embedding AI/ML into customer-facing platform experiences. Instead of hard-coded rules, the system now adapts to customer history, surfacing the most relevant task at the optimal time. Our design work not only boosted engagement but also revealed insights about how customers perceive value from AI recommendations: trust, timing, and clarity matter more than volume.
The engagement lift validated our approach and gave direction towards the future—using generative or more personalized approaches to keep experiences fresh and relevant. Beyond the numbers, this project stretched our team’s mindset, proving that design can play a critical role in shaping machine learning systems to be human-centered. It laid the foundation for Intuit to scale AI-driven experiences across its ecosystem.