## 2 AI's Benefits for Business and Peculiarities for SMEs
Moreover, AI can improve the use of resources such as energy, materials, and personnel,leading to cost savings and improved efficiency [47]. In SMEs, predictive analytics represents one of the most impactful applications of AI, to schedule maintenance proactively and avoid costly downtime. As a result, SMEs become more agile and responsive to shifting market dynamics, consumer behavior, and competitive pressures. By leveraging advanced data analysis, SMEs can make informed decisions, improve objectivity, and foster a culture of continuous improvement--aligning offerings with consumer preferences [32]. Despite these benefits, challenges persist--particularly for SMEs. Data availability and quality remain major barriers. Additionally, the complexity and lack of interpretability of AI models, coupled with ethical concerns such as data bias and privacy risks, complicate the real-world deployment of predictive AI [48]. **(d) Innovative product and service development**  
Innovation is a cornerstone of business growth, and AI is a powerful enabler of this transformation. By integrating AI into product design and optimizing production processes, enterprises can develop more advanced offerings, address evolving customer demands, and accelerate time to market. For SMEs, AI opens new possibilities to reimagine their market role and unlock creativity and efficiency, making innovation a strategic imperative. New Product Development: AI can assist in the design and development of new products by analyzing market trends and customer preferences, leading to faster innovation cycles [37]. However, innovation, especially disruptive innovation such as AI, often entails high failure rates and uncertain short-term returns [49]. While failure can be a valuable source of learning, many SMEs lack the financial and organizational buffers that allow larger firms (MNEs) to absorb such risks [14]. In general, SMEs face distinct constraints compared to larger enterprises, including limited absorptive capacity, narrower margins, and resource scarcity [50]. This underscores the importance of adopting a tailored approach that accounts for SME-specific challenges and innovation dynamics. Therefore, while AI offers tremendous innovation potential for SMEs, its successful implementation requires a structured and context-sensitive framework. To address this, the following section introduces the TOE framework, complemented by selected elements from the DOI theory. Critical Analysis of AI Adoption in SMEs: Internal and External Factors Through the TOE-DOI Framework  
This section provides a critical analysis of the internal and external factors that influence the adoption of AI in SMEs, using the TOE framework as the main analytical structure. To enhance this analysis, selected constructs from the diffusion of innovations DOI theory are integrated into the TOE model to better capture the perceptual and human-centered dimensions of adoption. The section begins by introducing the TOE and DOI frameworks, discussing their individual relevance, and the rationale for combining them to analyze AI adoption in SMEs. This conceptual foundation is developed in Section 3.1, which presents the theoretical integration and its suitability for SME contexts. Following this, the three core dimensions of the TOE framework are examined in detail: technical, organizational, and environmental. Section 3.2 explores technological determinants, including compatibility, complexity, and infrastructure readiness. Section 3.3 discusses organizational factors such as leadership, skills, financial capacity, and cultural readiness. Section 3.4 analyzes environmental influences, including competitive pressure, regulatory environments, and public-private ecosystem support. Section 3.5 introduces a short discussion of the empirical findings on SMEs and AI adoption, including a table with examples from the literature on empirical studies on the different dimensions of the TOE mode.