## 1 Introduction  
Small and medium-sized enterprises (SMEs) constitute the backbone of the global economy, representing approximately 90% of all businesses and more than 50% of employment worldwide [1]. Their critical role is reflected in their contributions to gross domestic product (GDP), productivity, innovation, job creation, export growth, and inclusive economic development [2]. To remain competitive in an increasingly digital and globalized environment, SMEs must overcome size-related limitations by enhancing operational efficiency and accelerating their capacity to adapt to technological and social change [3,4]. Among emerging technologies, artificial intelligence (AI) is widely recognized as the most transformative. AI refers to the development of intelligent systems--primarily software--that can perform tasks requiring human-like intelligence, such as learning, reasoning, and decision-making. Despite its potential to drive productivity, innovation, and customer engagement, AI adoption among SMEs remains limited and often unsuccessful, particularly in developing regions [5,6]. Although research acknowledges that SMEs can benefit from AI as much as large firms [7], SMEs face multiple and specific obstacles that cause early-stage adoption to be hindered by continuing practical constraints and challenges [8, 9, 10, 11]; advancing our knowledge of these obstacles is both urgent and necessary. This descriptive research has two main research objectives: (1) To understand the financial, technical, and organizational challenges that SMEs experience during AI implementation; (2) To provide an advisable executive summary aimed at facilitating the implementation of AI by SMEs based on the most recent (5-years old) empirical analysis and case studies. Our research objectives are founded in three key reasons: (1) Most existing studies focus on large firms whose abundant technical, financial, and human resources differ substantially from those of SMEs [12]. In contrast, SMEs--while resource-constrained--are typically more agile, closer to their customers, and less burdened by bureaucracy [13]. (2) Although digital maturity has been linked to better AI readiness [14], AI introduces unique challenges--such as system complexity, skill gaps, and uncertainty around returns--that are not fully addressed in general digital transformation literature [15,16]. (3) The early implementation phase of AI--where failure is most common--is still underexplored in academic SMEs literature [17]. Aiming towards this goal, this study presents an analysis on AI adoption in SME integrating the technology-organization-environment (TOE) framework developed by [PERSON] and [PERSON] (1990), [18], complemented by selected constructs from the diffusion of innovations (DOI) theory [19], following recent integration efforts proposed by [20]. The TOE framework analyzes technology adoption across three interrelated dimensions: technological readiness, organizational capacity, and environmental influence [21, 22, 23, 24]. It has received strong empirical and theoretical support in the SME context [25,26], and has been applied in diverse geographic and sectoral settings--including the European Union [27], India [28], Saudi Arabia [29], Ghana [30], Jordan [24], and Egypt [31]. More than its solid foundation, its value lies in its ability to surface the real-world challenges SMEs face during AI adoption. The remainder of this paper is structured as follows. Section 2 reviews the potential benefits of AI for businesses and the unique characteristics of SMEs that influence adoption. Section 3 introduces the TOE framework and explains the rationale for integrating selected elements of the DOI theory to analyze SME-specific adoption dynamics. From the analyzing of the TOE-DOI framework, we derive a structured set of challenges and corresponding solutions. Section 4 shows an empirical view, focusing on recent empirical studies in diverse regional and sectoral contexts to enhance the practical foundation.