What drives AI adoption in SMEs? Key factors for sustainable innovation

According to the study, trust in AI systems is essential for adoption. Trust acts as a bridge between user perceptions and behavioral intentions, mediating the effects of other factors such as personalization. When users believe that AI systems are reliable, competent, and act in their best interests, they are more willing to rely on them.

The rapid digitalization of business is reshaping how small and medium-sized enterprises (SMEs) innovate and compete. While artificial intelligence (AI) promises to boost efficiency and sustainability, understanding what drives its adoption remains a pressing question.

A new study published in Sustainability investigates this dynamic. Titled "Modeling AI Adoption in SMEs for Sustainable Innovation: A PLS-SEM Approach Integrating TAM, UTAUT2, and Contextual Drivers", the research offers a data-driven model explaining how cognitive, emotional, and contextual factors influence AI adoption among SMEs.

What drives AI adoption in SMEs?

The study identifies a comprehensive set of factors shaping AI adoption intentions. Drawing on the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT2), the researchers enhanced these frameworks by adding variables such as AI knowledge, workplace integration, trust in AI, passion for AI, and AI-driven marketing personalization. Using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, they found that the interplay between these factors determines whether users are willing to embrace AI technologies.

Among the strongest predictors, AI knowledge emerged as critical. Users who understand AI concepts and functions are more likely to see the technology as useful and easier to adopt. This knowledge also enhances trust in AI systems, reducing fear of the unknown and increasing confidence in automated decision-making.

Another key driver is passion for AI, defined as an intrinsic motivation to engage with new technologies. Passionate users are more inclined to experiment, persist through challenges, and integrate AI tools into both professional and personal contexts. This emotional factor, often overlooked in traditional adoption models, plays a decisive role in shaping positive attitudes toward AI.

How does trust and personalization influence adoption?

According to the study, trust in AI systems is essential for adoption. Trust acts as a bridge between user perceptions and behavioral intentions, mediating the effects of other factors such as personalization. When users believe that AI systems are reliable, competent, and act in their best interests, they are more willing to rely on them.

The study also highlights the role of AI-driven marketing personalization in shaping trust. Personalization makes digital experiences more relevant and engaging, increasing user confidence in AI tools. However, this effect comes with a caveat. While personalization strengthens trust when done responsibly, intrusive or opaque personalization strategies can raise privacy concerns and erode user confidence. The authors emphasize that transparency and ethical design are crucial to maintaining trust while leveraging personalization.

Workplace exposure to AI tools also emerged as an influential contextual factor. Employees who use AI in professional settings tend to develop familiarity and confidence, which spills over into personal adoption behaviors. This finding suggests that businesses adopting AI internally not only improve operational efficiency but also prepare their workforce for broader digital transformation.

What are the implications for SMEs and sustainable innovation?

The findings of this study carry significant implications for SMEs aiming to enhance competitiveness through AI. By combining cognitive, emotional, and contextual drivers, the research provides a roadmap for fostering AI adoption in ways that support both business growth and sustainability objectives.

From a practical perspective, SMEs can take several strategic actions based on these insights. First, they should invest in AI literacy programs to enhance user knowledge, which in turn boosts perceived usefulness and trust. Educational initiatives and explainable AI interfaces can demystify the technology and build confidence among employees and customers.

Second, encouraging passion for AI through engaging marketing campaigns and gamified learning experiences can stimulate adoption. Passionate users are more likely to embrace new tools and advocate for their benefits within their organizations and networks.

Third, workplace integration of AI should be viewed not only as a productivity enhancement but also as a catalyst for cultural change. When employees gain hands-on experience with AI, they are more receptive to adopting similar tools in other contexts, creating a positive feedback loop for innovation.

Finally, marketing personalization must be approached carefully. While it enhances trust when aligned with user expectations, it can also trigger privacy concerns if perceived as invasive. SMEs must prioritize transparency, consent, and ethical data practices to ensure that personalization strengthens rather than undermines trust.

The study also ties AI adoption to broader sustainability goals, particularly the United Nations Sustainable Development Goals (SDGs) related to innovation, infrastructure, and responsible consumption. By leveraging AI to optimize energy use, reduce waste, and support eco-friendly business models, SMEs can position themselves as leaders in sustainable digital transformation.

A blueprint for the future of AI in SMEs

The research confirms that AI adoption in SMEs is not just a technological issue but a multidimensional process shaped by knowledge, passion, trust, and context. The authors demonstrate that when these factors align, AI can become a powerful driver of sustainable innovation, helping SMEs remain competitive in fast-changing markets.

However, the study also warns that adoption is not automatic. Without efforts to build trust, foster emotional engagement, and address ethical concerns, even advanced AI systems may fail to gain user acceptance. For policymakers, educators, and SME leaders, the challenge lies in creating an environment where AI tools are not only implemented but embraced.