An exploratory study of factors influencing AI technology acceptance among product managers

Abstract

Artificial intelligence (AI) is reshaping knowledge work, yet little is known about how product managers (PMs), who connect business, technical, and customer concerns, accept and use AI tools. This study explores the factors that influence AI technology acceptance among PMs and how they use AI in their work. The study adopts a qualitative, exploratory approach based on semi-structured interviews with eight PMs in software and technology driven organizations. It is guided by the technology acceptance model (TAM), which centers on perceived usefulness and perceived ease of use, and extends it with three AI-specific factors: trust in AI, perceived risk of AI, and AI literacy. The interviews were analyzed using thematic analysis. All five factors shaped acceptance, but in different ways. Perceived usefulness was the strongest driver, though task-specific rather than general. Perceived ease of use was high for basic use but depended on prompting skill for professional-quality output. Trust was partial and calibrated by task type, earned through systematic verification. Perceived risk, including a notable concern about overreliance and cognitive decline, functioned as a regulator of how AI was used rather than as a barrier to use. AI literacy operated as the upstream enabler connecting the other factors. PMs used AI widely but selectively, following a consistent draft-and-refine workflow in which AI produces a first draft that the PM reviews and finalizes, while deliberately avoiding AI for sensitive-data, relational, and judgment-heavy tasks. Organizational context further moderated individual acceptance. The study offers role-specific insight into AI technology acceptance, showing that for PMs it is a task-specific, actively managed process in which AI assists but does not replace human judgment.