Assessing Consumers’ Readiness for the Adoption of AI-based products: implications for Product Management

Artificial Intelligence (AI) is rapidly transforming the way products and services are designed, delivered and experienced. However, the growing technological sophistication of AI-based solutions does not automatically ensure their acceptance by end users. Understanding consumers’ readiness to adopt AI-driven products therefore represents a critical challenge for both researchers and business decision-makers. Therefore, this thesis aims at investigating the factors leading to consumers’ willingness to adopt such solutions. Building on established technology adoption frameworks, such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Innovation Diffusion Theory (IDT), this study develops a conceptual model to assess how perceptual variables influence adoption readiness among consumers. Specifically, particular attention is given to perceptual drivers such as perceived usefulness, perceived ease of use, trust in AI, perceived risk and digital competence, together with selected socio-demographic characteristics. In order to test the proposed relationships, an online survey was distributed through Qualtrics to Italian respondents aged over 18. The findings reveal that perceived usefulness and perceived ease of use are the only significant predictors of willingness to adopt AI-based products across both the baseline and the extended models, which is coherent with what argued by AI and technology literature. Conversely, variables such as perceived risk, digital competence and socio-demographic factors do not seem to exert a significant direct impact. This suggests that AI adoption remains value-driven and usability-driven within the examined sample. The study contributes to the emerging literature on consumer readiness for AI by providing an empirically assessment of the determinants of AI adoption and by offering insights relevant for product managers involved in the development of AI-enabled solutions. Following some proposed implications, the research concludes with a discussion on the identified limitations, along with some directions for the future.