AI Builder is a tool to improve business performance by predicting outcomes and automating processes. The easy point-and-click experience targets users without a coding or data science background and allows them to easily incorporate AI into their business workflow. The growing number of AI Builder templates offer a wide range of solutions for use in applications and automated flows.
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Because the target AI Builder users typically do not have machine learning experience, it is crucial to help them feel they can trust and have confidence in this new technology. The entire experience is designed to be simple, clear, and to only reveal AI complexity if necessary. We regularly measured AI Builder through user studies and customer interviews to make sure we were on track with our simplified and transparent approach.
As a Senior Designer on the team I helped shape the initial structure and design of AI Builder from the ground up prior to its June 2019 launch. Although I continued to influence a wide number of AI Builder areas, my main focus shifted to simplifying the Prediction template and enhancing the value proposition of AI Builder through home page and introduction page improvements. My design workflow requires deep remote collaboration with the global team and has taught me how to best work with teams located around the world.
With any new feature for AI Builder there is a lot of exploration prior to final designs being developed. For larger efforts, I often led brainstorm sessions with our broader team to gather innovative ideas outside of our design group. Userflows and wireframes are a crucial step to help the team arrive at the most successful design direction.
AI Builder is fortunate to have a dedicated researcher to conduct user studies, create benchmark reports, and gather feedback from customers. Design research is always a key element which shapes and informs the direction for our design work.
AI Builder was released to the public in June 2019 and started with just a handful of templates. The public’s reaction and general excitement for AI Builder was very encouraging and motivated us to work even harder to improve AI Builder. As new challenges arise, our team continues to leverage research and our iterative approach to ensure that we create the experiences our customers want and love. AI Builder has brought measured value to the Power Platform business and continually extends the possibilities of Power Apps and Power Automate.
To further illustrate my design process from conception to final design, I have outlined an example feature our team executed recently. Although design is rarely a linear process, these steps help describe how I approach creating a design that our customers want.
Form Processing is an important AI Builder template that can be trained to automatically extract, organize, and save data from routine documents such as invoices and tax forms. From research and user feedback it became clear that Form Processing needed to allow for more user control. In addition to the auto detected fields in a form, users wanted control to select and edit the areas for the model themselves. The mixture of auto and user generated areas required a lot of exploration and iteration. In order to arrive at a solution quickly we ran a week long design sprint that ended with a successful research study and clear path forward.