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This study explores factors promoting and inhibiting advanced technology adoption in small- and medium-sized manufacturing firms (SMEs). With AI’s rapid advancement impacting productivity and efficiency across industries, understanding the challenges that SMEs face to remain competitive is crucial. Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) model as a theoretical framework, we analyzed managers, engineers, and line workers’ observations on workforce challenges, training needs, and opportunities faced by SMEs to provide insights into their smart manufacturing deployment experiences. Our findings highlight social influence’s role in promoting technology adoption, emphasizing community, shared experiences, and collaborative networks. Conversely, effort expectancy emerged as the largest inhibitor, with concerns about the complexity, time, and resources required for implementation. Individuals were also influenced by factors of facilitating conditions (organizational buy-in, infrastructure, etc.) and performance expectancy on their propensity to adopt advanced technology. By fostering positive organizational environments and communities that share success stories and challenges, we suggest this can mitigate the perceived effort expected to implement new technology. In turn, SMEs can better leverage AI and other advanced technologies to maintain global competitiveness. The research contributes to understanding technology adoption dynamics in manufacturing, providing a foundation for future workforce development and policy initiatives.