Artificial Intelligence based applications are an ever-expanding field, with an increasing number of sectors deploying this technology. To better understand laypeople’s intention to use AI applications for a broad number of domains (health, eldercare, driving, data processing, and art), we conducted a study (N= 450) assessing well-established variables in AI research (trust, familiarity). At the same time, we assessed three new concepts: knowledge about AI, risk and opportunity assessment for each domain, and a newly developed, socio-emotional perception scale. Results show that the newly investigated variables best predict overall intention to use, above and beyond trust and familiarity. Higher AI-related knowledge, more positive socio-emotional perception, and lower risk perception significantly predict general AI use intention. Furthermore, when investigating domain-specific AI use intention a similar trend emerges: knowledge, risk and opportunity perception, and socio-emotional perception best predict use intention in four (health, eldercare, driving, and data processing) out of the five investigated domains. Additionally, our results indicate that both AI use intention and risk and opportunity perception are distinctly domain specific. Higher opportunity assessment for the driving domain solely predicts the use intention for driving and not any of the other investigated domains. Our findings thus highlight the relevance of knowledge, risk and opportunity assessment, and socio-emotional perception, in understanding laypeople’s intention to use AI-based applications. The ramifications of such results are ample and open a new roster of research questions in understanding people’s AI use behavior intentions, their perception of AI, and possibly informing future AI use and acceptance trends.