## 2 AI's Benefits for Business and Peculiarities for SMEs
### TOE Plus DOI Framework Relevant to SMEs  
This study is directed at the business community, where the goal of AI adoption is not adoption for its own sake, but rather the generation of net long-term value. This value must take into account the full cost of implementing and using AI technologies. Although conceptual gains from AI, such as improved productivity, efficiency, and innovation, are widely acknowledged, they are not easily measurable. This article does not attempt to quantify net gains directly, but rather focuses on the factors that enable or hinder the adoption and integration of AI into business strategy. Post-adoption performance issues and unrealized gains are therefore beyond the scope of this study. The second key issue is generalizability. The net value of the adoption of AI is context-dependent: it does not apply equally across all firms, but is shaped by firm-specific characteristics [51]. To address this, the study distinguishes between SMEs and large enterprises. Although SMEs themselves form a heterogeneous group, they tend to face common constraints such as limited financial capacity, infrastructure gaps, and knowledge shortages [52; 53; 54; 55]. Given that AI adoption affects data, processes, strategy, and people, a dual perspective is required: one that includes both a process-oriented and an organization-oriented lens. The **Diffusion of Innovations (DOI)** theory, developed by [PERSON] [19], takes a process-based user-centered approach and emphasizes perception-based attributes such as relative advantage, complexity, compatibility, trialability, and observability. Although DOI provides insight into how individuals and organizations perceive innovations, it lacks consideration of environmental and structural factors [56]. Complementing this, the **technology-organization-environment (TOE)** framework, developed by [PERSON] and [PERSON], [18], offers a broader organizational view by categorizing adoption factors into three core dimensions: technological readiness, organizational capacity, and environmental influences. TOE has received strong empirical support in the context of business technology adoption [23; 24; 57; 58]. It is particularly relevant for SMEs, where the dynamic interplay of internal capabilities and external conditions determines the success of digital innovation. Several studies have affirmed that TOE factors are not static; they vary according to the type of technology and organizational setting [59]. The size of the company, in particular, has been identified as a key moderating variable in the adoption of technology [60]. The TOE framework thus provides a holistic view to understand how firms adopt innovations and has proven effective in multiple settings, including digital transformation [61], data analytics [62], and e-commerce [63; 64; 65; 66]. Despite its strengths, TOE presents two limitations. First, it does not capture the cognitive biases or individual characteristics of decision-makers, a critical issue in SMEs, where leadership is often centralized. Second, it overlooks how perceptions of innovation characteristics influence adoption [25; 64]. To address these gaps, several authors have proposed the integration of DOI attributes into the TOE model. This integration enriches the TOE framework by incorporating individual-level DOI insights. While TOE explains organizational and environmental readiness, DOI addresses how users evaluate innovations, focusing on perceived ease of use, usefulness, trialability, and observability. This combined approach is particularly suitable for SMEs, where adoption decisions are often informal, perception-driven, and highly context-specific. To reflect emerging challenges, including ethical compliance and regulatory adaptation, we further expand the TOE-DOI model to include dimensions of responsible and inclusive AI governance, particularly relevant for SMEs navigating complex digital ecosystems.