Bridging the Gap Between UX Practitioners' Work Practices and AI-Enabled Design Support Tools

User interface (UI) and user experience (UX) design have become an indispensable part of today's tech industry. Recently, much progress has been made in machine-learning-enabled design support tools for UX designers. However, few of these tools have been adopted by practitioners. To learn the underlying reasons and understand user needs for bridging this gap, we conducted a retrospective analysis with 8 UX professionals to understand their practice and identify opportunities for future research. We found that the current AI-enabled systems to support UX work mainly work on graphical interface elements, while design activities that involve more 'design thinking" such as user interviews and user testings are more helpful for designers. Many current systems were also designed for overly-simplistic and generic use scenarios. We identified 4 areas in the UX workflow that can benefit from additional AI-enabled assistance: design inspiration search, design alternative exploration, design system customization, and design guideline violation check.