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Due to confidentiality reasons some content has been intentionally rebranded, modified, or omitted to protect client company information. For this case study, the product has been sanitized as Nova.
Tech Co. is a global technology company known for its computing and enterprise solutions across hardware and software ecosystems. The company is developing a new AI wearable, a discreet, voice-enabled device that clips to the collar, designed for on-the-go, real-time interactions with AI agents.
The global wearable AI market size is rapidly growing and is estimated to reach $166.5 billion by 2030, at a CAGR of 29.8% from 2022 to 2030. Specific demand for on-device AI has also risen, dominating the market at over 58% in 2022 due to the growing requirement for fast computing, less dependency on cloud-based AI for critical operations, and growing demand for low latency devices (Grand View Research).
Rapidly growing AI demand makes it strategic for Tech. co to expand into AI-driven wearables and next-generation interactive experiences, but they faced key challenges in defining their market, user experience, and core value proposition. That’s where my team came in.
Our challenge was not just to design an interface—but to define who this product was for, what workflows it should enhance, and how AI could integrate seamlessly into real-world environments.
How might we design a scalable AI platform that makes deploying and interacting with AI agents seamless, familiar, and approachable?
I designed a two-part ecosystem for Tech Co.. With this initiative, Tech Co. is positioning itself at the forefront of agentic AI and wearable technology, enabling users to create custom, adaptive AI assistants that enhance both productivity and personal interaction.
When Tech Co. first began conceptualizing their AI-powered wearable, they saw potential applications across five different industries:
While their instinct was to design for a broad market, we knew that choosing a single, well-defined user segment would allow for deeper insights, a clearer product vision, and a more impactful UX.
Finding the right fit: Who needs this most?
To determine which segment had the greatest need and highest adoption potential, we conducted preliminary interviews with representatives from each group. Through this research, one segment stood out: nurses.
✅ High volume of manual, time-consuming tasks—from documentation to patient monitoring.
✅ Existing reliance on technology—hospitals and healthcare facilities already integrate digital tools into their workflows.
✅ Strong potential for AI integration—AI-powered transcription, summarization, and real-time record-keeping could directly reduce cognitive load and increase efficiency.
With nurses identified as the primary users, we conducted interviews and “day in the life” mapping –– insights that allowed us to develop a deep understanding of the pain points and needs of nurses. We mapped out a user journey to better understand where critical information loss was happening and how AI could seamlessly integrate into their workflows.
Through our interviews and user journey, we could identify 2 major moments where information breakdowns occur:
When one nurse transfers responsibility to the next, they must verbally relay critical patient updates. However, if they try to be thorough, it takes too long, eating into the incoming nurse’s limited time. If they keep it brief, important details may get lost, leading to gaps in patient care.
Nurses often move rapidly between patients, prioritizing immediate care over documentation. As a result, they frequently batch chart at the end of a shift, relying on memory to record crucial details. This increases the risk of forgetting key observations, and the sheer time required to recall and input everything leads to overtime work on an already long shift.
How Wearables fit In
We saw a clear opportunity for Tech Co.'s wearable to bridge these gaps by integrating with hospital record systems (like Epic) to automate transcription, summarization, and search.
However, we also faced a major constraint:
This approach accelerated our ability to test, refine, and gather insights—not limited to just nurses, but widely applicable design principles that could affect other industries that could benefit from AI-driven assistance.
We began ideating potential nurse use cases for wearable devices based on the needs we uncovered during user interviews and via the journey –– ultimately coming up with 6.
We brought these 6 ideas to nurses in interviews, where they quantified the % increase in satisfaction they thought each idea would bring them. Aggregating their responses across 12 interviews, we were able to create a value-feasibility matrix to see where we should explore.
We identified the core functionalities the AI wearable would need. By focusing on these universal capabilities, we ensured that the design could scale beyond healthcare in the long run.
Accurate, real-time transcription that could be saved, referenced, and accessed repeatedly.
Auto-generated summaries that could be easily shared or integrated into existing workflows.
Smart AI search & aggregation to eliminate manual counting, tracking, and information retrieval.
Next, I began designing the screens, first starting in low-fidelity sketches to ensure alignment between internal stakeholders (PMs and Devs).
After aligning on what the overall vision of how we wanted the mobile and web devices to come together, we began to work on the low-fi screens:
Daily standups and T-shirt sizing build efforts for each prototype were helpful in prioritizing which screens were needed in high-fidelity when (to support both developers and accommodate testing timelines)
As the development team began implementing the designs, we conducted usability testing with both nurse user testers and client stakeholders. Through this process, we uncovered 3 key challenges across the mobile and developer platform.
📱 Mobile Challenge 1: Recording Functionality Wasn’t Clear or Accessible
Nurses didn’t feel the recording functionality was prominent enough, making it difficult to quickly start or stop. Privacy concerns meant that nurses wanted a quick and obvious way to pause or resume recording at any moment, even for small, private conversations.
We redesigned the interface to make recording status explicit—whether the device was actively recording or paused was now immediately visible at all times. A quick-access toggle allowed users to pause and resume with ease.
📱 Mobile Challenge 2: Simple Summaries Didn’t Fully Solve Charting Issues
Nurses use different charting formats depending on the hospital or system. A generic AI-generated summary wouldn’t always fit their workflow. Travel nurses and part-time staff were especially concerned about having differently-formatted charts, which could imply lower quality work to colleagues.
We introduced a custom formatting option, allowing users to adjust the structure of AI-generated summaries to match their specific charting requirements. This ensured the AI’s output was aligned with existing documentation standards.
💻 Developer Platform Challenge: Developers Didn’t Want a Step-by-Step Workflow
The sequential model of setting up AI agents didn’t match how developers worked. Developers viewed AI agent creation "almost as an art form", where multiple elements had to be fine-tuned simultaneously. If any single part wasn’t right—even if everything else was correct—the agent wouldn’t work as expected.
We shifted from a sequential setup to a workspace model where users could see and adjust all agent components at once. This allowed for more intuitive, flexible, and iterative building.
These user-driven design improvements ensured that both end users (nurses) and developers had seamless, intuitive experiences—whether they were using the AI wearable in the field or building agents on the platform.
This project was a deeply rewarding experience, shaping not only a new AI-driven product but also a long-term innovation roadmap for Tech Co. While our initial work focused on defining the first iteration of the AI wearable and developer platform, the company has continued to build upon this foundation within their internal innovation lab.
Tech Co. has since publicly hinted at this work, making high-visibility talent acquisitions from smaller AI and wearable firms to accelerate development. Seeing this product evolve beyond our initial designs—and become a key strategic focus for the company—has been incredibly exciting.
What I learned…
🚀 New Technology Doesn’t Always Mean New Patterns
When working with emerging tech, it’s easy to assume that the UX should be completely novel. Initially, I thought AI wearables would require entirely new interaction models—but in reality, users don’t want to feel alienated by futuristic interfaces.
The best AI tools don’t disrupt—they integrate seamlessly into users’ lives. By making the product feel intuitive and non-threatening, adoption becomes frictionless.
🤖 AI Isn’t Just About What It Can Do—It’s About What It Can’t
One of the biggest takeaways from this project was that AI adoption isn’t just about technical capability—it’s also about user trust. The core function of this wearable was to record and transcribe conversations, but if we didn’t give users a way to pause recording, they wouldn’t use it at all.
AI isn’t just about automation—it’s about giving users the right level of control so they feel comfortable integrating it into their daily routines.
What the team said about working with me…
“I really appreciated having Jennifer as a teammate and design partner. She consistently put in the time and effort to create extraordinary results for the client. I knew I could always count on Jennifer to independently take on a task.”"
– Design Manager, Bain & Company
“I was very impressed with how Jennifer took on wireframing and excelled at quickly and efficiently generating new ideas. She was often 2 steps ahead, and produced high-quality Figma designs that kicked off productive conversations.”
– Design Manager, Bain & Company