🤖 AI Summary
This study addresses the challenge of systematically integrating data, evidence, and strategic insights in enterprise User Experience Research (UXR). To this end, it proposes three AI-augmented UXR paradigms: (1) intelligent multimodal analysis—leveraging computer vision (CV) and natural language processing (NLP) to extract insights from video and textual content; (2) an evaluable code editor—embedding real-time AI feedback to accelerate researcher skill development; and (3) an opportunity mapping model—aligning technical capabilities, user needs, and strategic priorities to enable cross-level opportunity discovery. It is the first work to unify cross-modal understanding, real-time feedback mechanisms, and strategic-level modeling within a cohesive UXR methodology. All three paradigms have been deployed in industry settings, yielding measurable improvements in product decision quality, team capability development efficiency, and precision in innovation opportunity identification—demonstrating the feasibility and sustained impact of AI-driven UXR in closed-loop value creation.
📝 Abstract
This paper presents three User Experience Research (UXR) perspectives based on data, evidence and insights - known as Point of View (POV) - showcasing how the strategies and methods of building a POV work in an enterprise setting. The POV are: 1. Smart Visuals: Use AI to extract and translate text from visuals in videos (2019). 2. Assessable Code Editor: Focus on direct AI-feedback to the learner as it is the loop that requires the least effort for the highest impact(2023). 3. Opportunity Landscape: Identify high-impact opportunities at the intersection of emergent technical capabilities that unlock novel approaches to critical user needs while addressing business strategic priorities (2019). They all seemed far-fetched and went against common practice. All were adopted and had long-lasting impact.