Two of IBM’s trailblazing designs recognized as a 2022 AI Excellence Awards Winner
IBM products continue to integrate AI capabilities in new and innovative ways. It’s our goal at IBM Design to create seamless integration experiences for our users, so it is exciting when our products are recognized for their excellence in this area. I am proud to announce IBM Supply Chain Intelligence Suite (SCIS) and AI Modeling within IBM Watson Studio (as part of IBM Cloud Pak for Data) have both won a 2022 Artificial Intelligence Excellence Award. I’ve seen the hard work our designers put into these products, their commitment to the design process and their impressive collaboration with the broader product teams.
AI Modeling in IBM Watson Studio leverages enterprise design thinking and cloud capabilities to propel a classic product to the next level
IBM SPSS Modeler was originally released in the ’90s as the first drag-and-drop visual product in the market which enableddata scientists to build machine learning models without code. Traditionally, data science required heavy statistical skills, limiting the field to users with coding experience. The design team recognized the opportunity to leverage the power of the classic SPSS Modeler with cloud capabilities to create a friendly and scalable product for the modern user, resulting in AI Modeling within IBM Watson Studio, as part of IBM Cloud Pak for Data.
With over 150 user interviews in one year, the design team for AI Modeling focused heavily on research. Using IBM’s Enterprise Design Thinking framework, the design team connected with data scientists and subject matter experts to better understand the needs of their target user. Consistent A/B testing and concept testing during these user sessions led to highly actionable insights which catalyzed the design.
AI Modeling enables both data scientists and business analysts to maximize the potential of their data — empowering users to understand and prepare their data, then build and deploy models using a graphical user interface, all in one unified environment. Extensive user interviews uncovered the need for easy onboarding and productivity. The design team created interactive tours, documentation, templates, and video tutorials to help onboard users more effectively. They also enhancedthe node property and output center to help users quickly configure nodes and view outputs in a single place to speed up their workflow.
Without writing a single line of code, data scientists and business analysts can now take advantage of intuitive drag-and-drop features to complete the same task which used to require coding capabilities. The cutting edge visual modeling features of AI Modeling empowers users who may understand business data and use-cases, but do not necessarily have coding skills. As a result, more people can now access and leverage the power of machine learning while working with their data.
The team’s user-centric approach also led them to understand the need for scaling capabilities. While AI Modeling’s code-less characteristic makes it easy for first-time users, advanced capabilities like scripting and extension notes makes AI Modeling extremely scalable. With extended open-source support, users can also install machine learning packages of their own to easily expand and scale their projects, making it easy to evaluate different renditions and find a model which fits users business needs.
These updates are helping AI Modeling users. Recent research on the new design displayed a task completion increase of 250%. Positive feedback shows the excitement our users have for the new features.
“You guys have added a whole bunch of useful features and I hope that it continues to grow and find a larger and larger audience because I feel like it’s a great tool. It’s gotten better and better.” — Data Scientist User
IBM Supply Chain Intelligence Suite uses Watson and machine learning to significantly reduce time-to-resolution while troubleshooting supply chain issues
Our designers work hard to create the best user experiences in highly technical products. The design team for IBM Supply Chain Intelligence Suite (SCIS) used a design framework specifically for AI capabilities in first version of the product release which empowers users to make more informed supply chain decisions.
The initial problem driving the SCIS design was the need to evaluate relevant supply chain data from disparate sources in one place. However, extensive interviews with subject matter experts exposed a deeper need than data accumulation and visibility. The design team quickly realized that supply chain professionals also needed to act on issues which surfaced while users were evaluating this data, and realized users needed insight to make more informed business decisions.
This discovery led the design team to lobby to include AI capabilities as a necessary component of that decision making process. Integrating AI into the product helps provide context behind user data and intelligently recommends different resolutions to supply chain problems which surface. SCIS uses machine learning to evaluate the data and provide the fastest, cheapest, or historically consistent solution.
It was the design team’s commitment to user research, and equivalent commitment from product management and engineering which changed the entire outcome of the product they created. Users are now able to log in to their account and evaluate the health of their supply chain and react in a more informed and efficient way.
The teams’ commitment to design thinking allowed them to uncover truths about the user which they hadn’t known before. Their collaboration with product management and data scientists advanced the product to integrate AI from the very beginning, making this a more innovative and productive solution for our users.
Designers always know the importance of being pulled in early to the product lifecycle, but it’s crucial that the entire product team recognizes this necessity too. Our General Manager of IBM Design, Katrina Alcorn, has called on our entire organization to develop stronger cross-functional partnerships to Design. The design team for SCIS exemplified the success this collaboration can have on our products, and I’m very proud of their commitment to the process and the exceptional user experience they’ve created.
Congratulations to the winning design teams
AI Modeling within IBM Watson Studio
Jamie Davis, Sean Hale, John McCabe, Devin O’Bryan, Analuisa Del Rivero, Lesedi Khabele-Stevens, Alex Swain, Wen Xiong
IBM Supply Chain Intelligence Suite
Chris Hammond, Ciera Raines, Kelsey Gonzales, Josh Fan, Sumaiya Fairuz