🤖 AI Summary
As AI agents become deeply integrated into core enterprise workflows, designing effective human-AI interaction to enhance user experience, foster adoption, and support user-centered decision-making has emerged as a critical challenge. This study addresses this issue through a mixed-methods approach, combining qualitative interviews and quantitative experiments to systematically investigate interaction patterns between humans and AI agents in business contexts and identify key design elements that shape user experience. Grounded in empirical findings, the work proposes a set of human-AI interaction design guidelines tailored for commercial environments, along with a quantifiable evaluation framework. These contributions offer both theoretical grounding and practical guidance for development teams seeking to optimize and deploy large-scale human-AI collaborative systems.
📝 Abstract
As AI agents are increasingly integrated into core business processes, understanding and designing effective interaction patterns between humans and AI agents becomes crucial for value creation. This study identifies and evaluates principles and criteria for a positive User Experience (UX) with AI agents, along with methods for its measurement. We identify user expectations and needs to facilitate adoption, build trust, and support user-centered decision-making by development teams. Using a mixed-methods approach that combines qualitative and quantitative techniques, we explore interaction patterns between humans and AI agents. The findings from this exploratory research serve as the basis to develop a survey experiment which evaluates the effectiveness of specific design elements on a larger scale. This foundational research contributes to the development of more intuitive and effective human-AI agent interactions in business settings.