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ZOE meal logging
Turning experimental AI into scalable behaviour change
My role
Product Design Lead
Company
ZOE
Year
2024
Background
Meal logging is a central pillar of the ZOE experience, helping members track what they eat and receive personalised health insights. Over time, however, logging had become increasingly cumbersome, requiring members to manually construct meals by searching for individual ingredients.
The team had begun exploring LLMs and OCR to simplify this process, particularly for homemade meals and recipes from books or websites. Early releases showed strong technical potential, but real-world impact was limited: members struggled to discover the features, understand when to use them and integrate them into everyday logging habits.
The core challenge wasn’t AI capability - it was behaviour change.
I led the design of a streamlined AI-assisted logging experience and activation strategy, focused on embedding these tools into natural user flows, accelerating time-to-value and laying the foundation for more passive forms of logging.
Framing the problem
Early AI experiments showed that meal logging could be dramatically simplified. However, low discovery and unclear first-time experiences meant only a small subset of members were benefiting from the new capabilities.
Rather than continuing to ship isolated AI features, I led a focused discovery phase to understand where adoption was breaking down - combining customer interviews, behavioural data analysis and a review of the existing logging experience across key scenarios.
This surfaced three high-leverage opportunities:
Awareness and first-time value - members didn’t naturally discover or understand when to use the tools
Search as the broken entry point - many logging journeys stalled when meals or recipes couldn’t be found
A shift toward passive capture - members showed strong demand for logging by photo rather than manual input
The strategic goal became clear: move logging from manual construction toward progressive AI-assisted capture, while making value immediately obvious in the moments members already used.
Shaping the solution
I aligned the team on a sequenced approach to drive adoption and long-term behaviour change - embedding AI into moments members already used, rather than treating it as a standalone capability.
Progressive activation through onboarding
Guide members to early 'wow' moments by walking them through high-value logging actions, reducing reliance on passive feature discovery.
AI embedded directly into search
Turn failed searches into moments of creation, enabling members to generate meals or recipes instantly when content wasn’t available.
Foundations for passive logging
Explore image-based capture as the long-term evolution of meal logging, reducing manual effort further over time.
Rather than delivering everything at once, we sequenced these bets around the interventions most likely to shift behaviour, validating impact through experimentation before scaling.
Driving discovery and first-time value
To accelerate adoption of AI-assisted logging, we introduced a new 'Get more from logging' experience - a progressive onboarding flow designed to guide members towards early high-value actions.
This was anchored by a simplified logging screen that:
Clarified entry points for different logging scenarios, including recipes, websites and homemade meals
Connected common use cases directly to relevant AI-powered features
Reduced friction between discovering a capability and successfully using it
Alongside this, we embedded lightweight, in-context education within each flow - using example content to help members get started without pulling them out of task.
Through user testing, we deliberately deprioritised a full guided product tour. While initially appealing, it didn’t meaningfully improve activation and introduced friction at moments where speed and momentum mattered most.
Focusing instead on task-driven activation reduced time-to-value and drove higher adoption of AI-powered logging tools.
Turning search dead-ends into AI creation
Search was the most common starting point for meal logging - and the most frequent point of failure. When members couldn’t find meals or recipes, they were forced into manual construction, creating significant friction.
To remove this bottleneck, we embedded AI directly into search. When results didn’t return useful matches, members could instantly generate a meal or recipe from their query - turning dead ends into moments of creation.
Through iteration and testing, we refined how this surfaced:
Evolving from a simple prompt button to a richer contextual card
Clearly communicating what AI would generate from the member’s query
We also learned that educational content within search reduced conversion. In a task-focused flow, members prioritised speed over learning, so we shifted education into the broader onboarding experience.
This change drove a 41% increase in usage of the AI meal generation feature.
Building the future of logging through images
Strong member demand and discovery insights pointed to image-based logging as the natural long-term evolution of meal capture - reducing effort further and moving towards more passive tracking.
I led early work to de-risk this strategic shift, designing an image-based experience that allowed members to photograph meals and automatically generate ingredient lists.
In parallel with foundational product design, we ran a focused technical proof of concept to validate:
Image-to-ingredient accuracy
Generation speed
Overall feasibility against product quality standards
Once viability was confirmed, I partnered closely with product and engineering on a rapid prototyping phase, shaping an experience that:
Captured meals in seconds
Generated editable ingredient lists
Significantly simplified saving recipes
Early user testing showed strong engagement and clarity, directly informing roadmap prioritisation and positioning image-based logging as a core future initiative.
Outcomes
By shifting from isolated AI features to behaviour-driven design, we turned experimental capability into meaningful member value.
Progressive onboarding, embedded AI in search and clearer first-time experiences led to:
Significantly higher adoption of AI-powered tools
Improved overall logging conversion
Faster time-to-value for members
Beyond the immediate metrics, the work established a clear strategic direction for logging - moving from manual construction towards increasingly passive, AI-assisted capture.
The foundations laid through this project directly shaped ZOE’s future roadmap, positioning image-based logging and deeper AI integration as core long-term investments.