Preparing QuickBooks for an adaptive, data-driven future

Overview

Personalisation had been talked about at Intuit for a while. When I joined, it was still mostly a concept. Engineers were interested in using AI to dynamically adjust the product but no one had worked out what that would look like for an actual QuickBooks user sitting down to manage their books.

I was the first dedicated design role focused on it. There wasn’t a brief so I had to define the space in collaboration with a technical director.

The problem

New users were dropping off early and the research pointed to two things: we were asking for too much information upfront and the product wasn’t responding meaningfully to what it knew about the business.

We also had a challenging internal problem; different teams had different ideas of what personalisation meant. Engineering wanted to use AI to customise dynamically, product wanted to reduce feature overwhelm and leadership saw it as a company-wide strategic priority. Getting those perspectives aligned was a big part of the desired outcome.

Design approach

Mapping the data we held

With a technical director, I mapped what we could legally capture, what we could technically capture (pre and post authentication) and what actually mattered for driving a better experience. That gave us a framework to simplify onboarding by collecting less upfront and being smarter about how we used that data downstream.

Built tools for teams to work with, not just a strategy doc

Because personalisation cut across multiple squads, I couldn’t just hand over a framework and walk away. I created interactive wireframes that showed how a data decision in onboarding would ripple into a different part of the product. It gave teams something concrete to respond to, and it stopped the work from fragmenting.

I synthesised 20+ research reports alongside primary research and ran workshops to establish shared ground on what personalisation tangibly meant — not in theory, but at the level of a specific widget or a specific user action.

Ran a small proof of concept

We partnered with QuickBooks’ YouTube content team to understand how their help videos were tagged and structured. The goal was to explore whether we could surface that content contextually based on where someone was in the product and what they’d done, and during the first 31 days. It was a small experiment, but it gave engineering and product a concrete direction to get behind.

Outcomes

• Simplified onboarding — fewer required inputs structured around what actually mattered
• A framework teams could use to build AI-driven adaptation in their areas
• Better cross-team alignment on a shared definition of personalisation
• Foundational patterns for contextual support in the first 31 days

Challenges to learn from

I had no direct line to most of the teams I needed to influence. This meant I had to build relationships slowly though 1:1s, mentoring junior designers and making artefacts to engage teams. One ongoing tension was how we measured transaction categorisation. It kept coming up in different conversations with different answers, so eventually I pulled together a small group including PMs, engineering and design to get clear on what we were actually measuring and why. It was a small change but it did unblock a lot of work.