Agentic UX for Google’s Manufacturing Software, Powered by AI AI/ML
Google, DSPA — March 2022 – Present
My Role
Lead Product Designer - Feature Scoping, User Research, Data-Driven UX, Interaction Design, Visual Design, Prototyping, AI-Driven Workflows
Team: 20+ Members
TPMs, Data Scientists, Engineers, and Product Operations.
Timeline & Status
3 Years | Ongoing
Challenge
- How might we design agentic AI workflows that empower users to make informed decisions within Google’s smart manufacturing ecosystem?
- How might we leverage AI/ML to transform complex manufacturing data into clear, actionable insights while maintaining user autonomy and trust?
Overview
A centralized system for gathering and examining data produced in manufacturing plants is offered by a manufacturing data analytics tool. This centralized solution enables the creation of an autonomous, intelligent global operating system by facilitating Smart Manufacturing.
I led the holistic user experience for manufacturing software by applying data-driven analysis and machine learning techniques, incorporating practical use cases.
As the Product Designer for an innovate smart manufacturing software team, my task involved developing UX solutions that present practical insights in a way that users can readily understand and apply through data visualization.
AI/ML UX
Leveraging AI and ML to Unlock Actionable Insights from Manufacturing Data
Pioneering the Future with Agentic AI UX workflow
Explore how AI and ML empower users to extract deeper, more meaningful insights from complex manufacturing datasets.
With a deep focus on Agentic AI UX Workflow, I bridge the gap between human intent and machine intelligence—creating scalable, adaptable, and context-aware interactions.
Here are the key milestones from the recent research with our latest AI solution:
With a deep focus on Agentic AI UX Workflow, I bridge the gap between human intent and machine intelligence—creating scalable, adaptable, and context-aware interactions.
20% Increase in User Engagement
• Redesigned the dashboard with AI-driven insights, leading to a 20% rise in average page views per user within a month.
25% Boost in Task Efficiency
• AI-powered anomaly detection enabled test engineers to identify root causes faster, optimizing workflows and reducing time and cost.
3-5% YoY Growth in CSAT
• Introduced a user-driven CSAT workshop, addressing top-requested features and enhancing customer satisfaction by 3–5% YoY.
View the full case study here.
Works
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Made with love © 2025 Bryan BeomSeok Oh
2025 Bryan Oh • Product Designer 😉
Designing for clarity, solving with intent. Currently crafting UX for manufacturing analytics—powered by Bubble tea🧋.
“The technology we design to make life easier should not only understand our needs but also recognize our struggles, reminding us that true innovation begins with empathy.”
"AI-driven UX should be empathetic, empowering users with seamless agentic workflows that adapt to their needs”