Bringing AI into UX work
A strategic, documented methodology for integrating generative AI into professional UX workflows.
The brief · Yara International
How can different UX teams embrace AI tools to work better, with clear protocols on how to use them across the UX lifecycle?
The goal was to define a structured and scalable approach that ensures consistency and efficiency across teams.
- Started
- 2026
- Duration
- 3 months
- Use cases
- 4tested on real projects
- Disciplines
- 3design, research, service design
My roleLed the initiative
I personally led the initiative to introduce new operational capabilities that integrated AI into UX design, UX research and service design workflows.
The work involved identifying relevant tools, defining testing protocols, building real use cases and producing clear guidance on what worked, what did not and how each tool could be used effectively in future practice.
What the team did3 people
A small team: 2 UX designers and 1 front-end developer.
- 2 UX designers
- 1 front-end developer
Outcome
Documented protocols and rules for using AI in design, research and service design.
Figma Make as a sprint tool
We explored how to use Figma Make as a way to improve workshop sessions, and to speed up how participant ideas could be captured and visualised instantly. By introducing this tool into sprint design workshops, we shifted the focus from manual sketches to a collaborative discovery process using digital AI help.
The tool allowed for rapid visualisation that helped stakeholders feel their input was being accurately represented in the room. This speed turned static meetings into faster iterative sessions, making it easier for the group to align on a direction and decide which concepts to follow further.
Figma Make as a data‑hungry prototype maker
One of the biggest challenges with prototypes has always been representing and populating realistic data sets. This is an intensive task for designers, and for professional tools, making business decisions without proper data often leads to derailed discussions and a lack of context.
We found that Figma Make was extremely effective at populating prototypes and concepts with realistic data, even allowing for dynamic interaction.
The end result was a much better way to test with better context, while reducing the time it takes to build prototypes in complex cases. By ensuring the data was realistic, we moved away from generic placeholders and allowed stakeholders and users to focus on the actual usage scenarios.
A clear case of where AI has a clear advantage of use.
Building user stories with Gemini Nano Banana
We identified a specific strength in using Gemini Nano Banana to generate user stories and storytelling vignettes. These small visual narratives became a powerful way to showcase interaction models and user journeys in a highly accessible format. By building structured, visual showcases of a journey, we helped key stakeholders understand pain points and opportunities with much more clarity than traditional documentation allowed.
This process made it possible to create multiple outcomes to demonstrate the direct impact of a new feature on the end user.
Consistently producing these visual artefacts opened new ways of presenting user insights for the business to make informed decisions. While the tool provided the initial structure and speed, the true value came from our iteration and validation, ensuring every story was accurate.
It was about storytelling that drove decision making, not just automated text generation.
User agents for UX research
One of the most promising areas we explored was the development of personas as AI agents. We built these agents to interact with our prototypes and concept screens, allowing them to provide feedback and perform tasks, so we could measure their success rates.
We treat what these agents report as hypotheses to check, not as findings about real users. They are highly useful for providing quick feedback loops on how the work is progressing.
While this is not a replacement for human testing, it is a powerful tool to get constant feedback and eliminate the first layer of friction when building products.
By using agents to flag likely problems early, our research with real people can focus on uncovering deeper insights and complex emotional responses, rather than basic usability errors. The core tasks are still tested with people.
Quick feedback loops on how the work is progressing. Flags to check, not findings.
Confirms or rejects what the agents flagged, and finds complex emotional responses.
The outcome
New UX AI protocols and rules to bring new ways to embrace the power and innovation AI brings to our field.
- 01Identify the relevant tools
- 02Define testing protocols
- 03Build real use cases
- 04Write clear guidance
What we found, tool by tool
The decisions
Three decisions, across two chapters.
Select a decision to read what I chose and why.
Chapter 01Workshops
Chapter 04User agents
Decision 1 of 3 · Chapter 01 · Workshops
Test tools on real use cases before writing rules
WhySo the guidance came from what actually worked in our projects, not from the hype around each tool.
By avoiding a rushed approach to AI, we established clear, documented processes that enable consistent and scalable ways of working across the organisation.
Contact
Open to design leadership roles.
I’m based in Singapore and can work in Singapore and the EU without sponsorship. Available now.