Designing a pickup display that makes order handoff fast and calm. I led UX for Burger King’s in store pickup display. We focused on clear queues, fewer repeated calls, and smoother peaks.
Burger King stores needed a revamp so guests and staff could see the same truth at a glance. Other brands and food courts also needed a pickup display. Burger King was closest to the deadline, but at that time we did not have one product that could fit them all.
From store visits and video reviews we learned:
We designed a modular, responsive display that stays readable from far away and adapts per store and per brand.
The new display is live in stores. Guests find orders without asking staff. Staff mark states in one place and focus on handoff.
The picture looked different from the mock on day one. The store used a 4K TV. I supported the regional PM remotely to set the TV output to 1920 by 1080, which aligned the layout and spacing with our design.
The TV colors were slightly brighter than our themeThe on site color profile pushed red. After the PM spoke with the Burger King manager, the store kept operating that day and planned to tune the TV color profile themselves.
The same display patterns adapt to Food Junction in Singapore and other stores with different screens and layouts.
Preparation pays off. Research, technical reviews, and clear requirements with the PM created a product that balances reuse for the company and meaningful configuration for each client. Build the system once and let stores tune what matters without a redesign.
The original 10-day sprint was a profound success, delivering a flexible and structurally scalable digital pickup display. However, extreme time constraints meant the foundational process was largely informal.
Designers who use AI well will absolutely replace those who do not.
In this 2026 update, I revisited my foundational architecture to demonstrate a modern, AI-Augmented Double Diamond methodology. Rather than shrinking the problem space, I utilized generative AI to drastically expand discovery, align business stakeholders, and drive a rapid 'idea to prompt to prototype' cycle. By integrating tools like Gemini and Figma AI, I optimized requirements gathering and completely automated developer handoffs without sacrificing scalability. This is how I bridge deep operational knowledge with high-speed, enterprise-ready execution.
During the rapid original sprint, I relied on indirect requirement gathering by speaking with Project Managers and Sales. While valuable, second hand information often misses the granular nuances of real world user friction. To formalize this phase, I utilized Gemini as a synthetic research partner to fill the informational gaps.
The biggest mistake in modern design is shrinking the first diamond to rush into solutions for a problem that might not exist. In this phase, I refused to skip primary research. Instead, I used AI to leverage my position. I leveraged generative AI to draft strategic communications for business stakeholders, securing a seat at the discovery table early. Furthermore, I utilized AI to craft a robust primary user interview plan and generate sharper recruiting emails and interview questions before ever touching a design file.
A design brief focused solely on the user perspective leaves a massive business gap, making projects feel unrealistic. To ensure this architecture solved real commercial kitchen constraints, I translated my synthetic primary research and informal stakeholder chats into Gemini. By instructing the AI to act as a Lead Business Analyst, I generated a comprehensive Product Requirements Document (PRD). This formalized my operational intuition into concrete, trackable metrics, defining exact business objectives, hardware limitations, and potential risks.
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I used AI to generate rapid design variations. While current AI outputs are rarely flawless, they are highly effective for divergent brainstorming before converging on a final solution. These AI explorations helped me uncover blind spots in my initial layout, leading to two major operational upgrades:
The original architecture was scalable, but the delivery mechanism required modernization. The foundation of this delivery relies on advanced Figma Variables and Design Tokens, enabling the entire interface to pivot brand identities seamlessly. To elevate this delivery for enterprise engineering teams, I utilized AI to automate the translation of design into development. AI tools analyzed the Figma Variables to instantly generate comprehensive documentation, annotated component states, and export-ready CSS token structures.
Moving fast often results in horrible design documentation, which always backfires when a new stakeholder asks to see past explorations. I maintain pristine file hygiene: keeping one major iteration per page with the latest version at the top. To save time and avoid repetitive strain, I used Granola for meeting summaries and talked to ChatGPT to generate clear, point-form annotations detailing exactly what changed in each version, why the change was made, and who provided the feedback.
The new display is live in stores. Guests find orders without asking staff. Staff mark states in one place and focus on handoff.
This retrospective validates my original strategic intuition. The MVP I built was inherently scalable, but by running it through an AI-augmented Double Diamond process, I elevated it from a solid design file to a fully documented, enterprise-ready product ecosystem. This workflow proves that when deep foundational architecture skills are combined with the analytical and generative power of modern AI, designers can solve complex business problems with unprecedented speed, precision, and alignment with engineering teams.
AI can amplify the speed and technical precision we bring to the table, but it cannot replace empathy, patience, or how we make cross-functional teams feel during a tense sprint. By offloading documentation, content generation, and rapid prototyping to my AI stack, I reserve my core energy for human connection, user empathy, and strategic leadership.