Microsoft
2025
Designing intuitive AI experiences within Microsoft Fabric.
Designing a mobile experience that helps users discover new music, explore playlists, and connect with artists through a simple and engaging interface.
Background
Context + Problem Space
Microsoft Fabric is an all-in-one analytics platform that brings data, analytics, and insights together in one place. It helps teams easily work with data, from ingestion to visualization, so they can make faster and more informed decisions without juggling multiple tools.
The main guiding question we worked with was…
How might we help users feel more in control and confident when navigating AI features within Microsoft Fabric and make the platform more accessible and personally relevant to their needs?
In short, we were tasked with implementing AI in Microsoft Fabric's ecosystem in a more seamless way!
Research
Platform Research, Competitor Analysis, User Interviews,
To gather both qualitative and quantitative research, we conducted a very thorough competitor analysis, 21 user interviews, and intensive research on AI UX patterns.
Platform Research
We started this process by doing in depth research on Microsoft Fabric itself. As college students, data analytics and business intelligence platforms aren't something that many of us have used. Through watching numerous YouTube videos, reading articles, reaching out to Fabric users, and inputing our own data to play around with the platform, we were able to build a very strong understanding of the platform and its functions.
Additionally, we created a very detailed Information Architecture Map to understand the various user flows within the application.
Doing this was super helpful in understanding how the various complex data workflows in Microsoft Fabric are organized.
Competitor Analysis
We then analyzed 6 competitors: Snowflake Data Cloud, DataBricks LakeHouse Monitoring, Amazon Sagemaker, SAP HANA Cloud, IBM Cloud Pak, and Google Vertex AI. Our analysis delved into their UX patterns, data processing methods, onboarding experience, and more.
User Interviews
We also conducted 21 user interviews and found that users wanted more accessibility to data flows, greater flexibility in customizing their experience with Fabric, AI support without obstruction, and wanted to stay in flow without jumping between screens and tools.
“I don’t want a one-size-fits-all assistant. I need something that understands my workflow and adapts, not something that treats everyone the same.”
Data and AI Solutions Architect
Avande
“It’s frustrating when AI tools take over the screen or block what I’m working on. I want support, not something that gets in the way.”
Data Engineer
Phoenix Dataworks
Understanding AI UX Patterns
Additionally, we did in-depth research on AI UX patterns, as this was something we had never worked with. Learning about the difference between wayfinders, nudges, governers, tuners, and all the ways of implementing AI into tech platforms seamlessly, taught us about all of the nuances in this field. In a growing world of AI, this research was incredibly helpful and useful for future projects!
Design Process
Wireframes to Hi-Fidelity Designs
Solutions
2 Main Directions
To combat the listed issues, we created and prototyped multiple different features into hi-fidelity designs!
Focus 1: Invisible, Context-Aware AI
We designed a suite of AI features that integrate naturally into existing workflows rather than interrupt them:
Copilot Flow: A modular, building-block system that lets users create data pipelines using a mix of structured inputs and natural language.
Adding tasks Tackles: Customization and Organization
Importing sources Tackles: Customization and Organization
Lets users build data workflows using freeform natural language, seamlessly combined with modular data sources. Tackles: Flexibility
AI suggestions are created from prior behavior, frequently used data flows, and Copilot's understanding of the imported data. Tackles: Personalization
Copilot Search: A conversational, intent-aware search experience that surfaces relevant assets even when users don’t know what to ask for.
Simply put, this feature surfaces what matters before you ask for it. It finds the right asset, even if you don't know what its name is. It gives you results with context and AI visibility. Tackles: Navigation and Relevance
Together, these features reframed AI as a quiet collaborator, present when needed and invisible when not.
Focus 2: System Transparency Through Visualization
To help users understand complex data relationships, we introduced Topology Map & Trace. These were created with the goal of providing a clear, time-aware view of data relationships that help users quickly identify issues, understand system behavior, and take action.
Topology Map: This feature is a dynamic, AI-enhanced visualization that shows the relationships between datasets, pipelines, dashboards, and users across Microsoft Fabric. It helps users trace lineage, diagnose issues, and understand system-wide dependencies through interactive nodes, time-based views, and contextual Copilot insights.
Features seen in this prototype are the ability to see your entire data ecosystem at a glance, centralizing diagnostics within the monitor hub, and being able to filter results based on your user need. Tackles: Transparency + Lineage
Here, I open the AI sidebar that explains every alert. This allows us to visualize data health without combing through messy logs. Copilot is used to break down the error so you don't have to.
Topology Trace: This feature is focused on understanding the system behavior before, during, and after an incident.
After choosing a date, drag the timeline and watch your data evolve. A calendar is also included for the ease of finding dates to check your data.
Hover to reveal the "why" behind every failure. Click into any error for instant Copilot analysis. Understand the root cause without having to leave the map.
Delivery
Our team had the opportunity to present our work to key stakeholders at Microsoft, including Senior and Principal Product Designers. Receiving direct feedback from industry leaders was an incredibly rewarding experience that helped us validate our ideas and refine our thinking.
What I Learned
Leadership and Technical Insights
As one of the project managers of this team, I was tasked with leading a group of 5 consultants through the entire product development lifecycle. Faced with a rocky start, this was not an easy process. However, this was one of the most rewarding experiences of my college experience thus far. Working through early ambiguity, learning the platform, prototyping complex flows, and iterating as a team taught me the importance of time management, staying calm under pressure, and leading with empathy. I also learned that effective leadership doesn’t mean having all the answers - it means knowing when to ask for help and creating space for collective learning. Many of our best insights came from leaning on one another and growing together throughout the process.
Beyond leadership, this project deepened my understanding of AI, specifically AI UX patterns. In an increasingly AI-driven landscape, I learned how critical it is to design AI in ways that support user flow rather than dominate it. Thoughtful, non-intrusive AI integration can empower users, enhance confidence, and improve productivity, without disrupting their work.
Our Team ⭐️
