As Director of UX at Citrix, I partnered closely with the Data Science team to integrate AI/ML into multiple products. As a UX Design team, our focus with AI features was:
• Translating algorithmic output into user-consumable insights. In addition to presenting data or information, UX also focused on providing consistent language (labels, help text) and interaction models.
• Providing in-product resources for users to clarify how insights are derived, while not disclosing the algorithm. This helped promote trust and transparency in our AI/ML content.
• Exploring with prototypes new applications for the quickly emerging technology
Our technology was focused on providing insights and predictions to help our enterprise customers understand:
Security risks users present, based on their behavior. This included automating system security safeguards based on discovered risks.
Predicting and addressing application performance issues with enterprise application environments. This included automating behavior like auto-scaling of resources based on discovered issues. Over time the system was able to leverage historic data to make predictions prior to events.
Usage and utilization of resources. In the context of users, insights about system and application access based on geography, time of day, or user attributes. In the context of application or network resources, insights about system load and distribution through the day, month, year, or any time horizon.
This product video features UX representations of various AI/ML features, including predicted issues and resulting auto-scale behavior.
AI/ML Application in Citrix Networking