From June 2019 to October 2020, I worked as a UX designer on one of IBM Watson’s Internet of Things (IoT) products. I was primarily responsible for working on data visualization, information architecture, wireframes, and prototypes for triangulating different IoT data sources to visualize and quantify utilization and energy insights inside buildings.
The link to my full case study is here. Please reach out to me if you'd like the password to view. While I’m unable to share the exact details of what I worked on publicly, below are some assorted learnings and thoughts on my experience designingaround the AI and IoTspace.
designing around AI --
While context is important in any design industry and space, it is increasingly important with newer technologies that are more mystified. The term “artificial intelligence” can invoke a range of sentiments from people, from curious and excited to anxious and fearful. It is crucial to be able to balance the exposure of what, how, and why the AI is being used within any product or system in order to build trust. All examples have been modified to remove sensitive information. The screens below are not reflective of real UI.
How might we provide a level of transparency throughout moments that include the use of AI in our products?
Explaining what data was affected and why.
Calling out when AI capabilities are used.
Striking a balance between under and over-explaining AI can be challenging. In many cases, starting with a high-level, approachableexplanation can serve as an effective entry point for those who want to dig deeper into denser documentation.
designing experiences within building IoT --
To better understand design considerations within the touchpoints of buildings, it is important to consider two general user groups: the space manager and the building occupant. While these user types look different across all kinds of industries and areas, the general user needs are similar.
The space manager: the person who is keeping track of data about the space, from utilization and occupancy to energy and sustainability.
The space occupant: the person using and experiencing the space.
While these two user groups may seem distinct by nature, the sensor-based data they consume can be seen as two sides of the same coin.
The space occupant
For the spaceoccupant, the need focused less on understanding mass amounts of data, and more on surfacing relevant information about their space (i.e. providing occupancy predictions for a certain day and time for a room) and adding transparency to their surroundings (i.e., “How recently was this desk cleaned? ”).
Below are some assorted steps from various userflowstoryboards I created for user testing and concept validation.
The space manager
From the spacemanager’s perspective, the needs are primarily focused on understanding the IoT data in their space in order to make informed decisions.