## 8 Conclusions  
This study has argued that for SMEs, adopting AI is no longer optional--it is a critical driver of strategic transformation, innovation, and long-term competitiveness. Although AI offers unprecedented opportunities to automate operations, personalize customer engagement, and improve decision-making, successful implementation requires navigating a complex landscape of technological, organizational, and environmental barriers. To address this complexity, the paper applied an integrated TOE framework enriched with selected attributes of DOI theory. This combined lens enabled a multidimensional analysis that incorporates both structural conditions and perception-driven adoption dynamics. Eleven key challenges were identified across the TOE dimensions, and each was matched with actionable context-sensitive strategies, ranging from talent development and modular infrastructure design to responsible AI governance and public-private ecosystem support. In addition to the challenge mapping matrix, the study proposes a six-phase structured roadmap methodology for AI adoption, detailed in Section 6. This methodology addresses the need for a clear, phased approach tailored to SMEs' constraints, guiding firms through readiness assessment, solution selection, piloting, and scaling. Table 5 and Figure 2 provide a visual and practical synthesis of this process. By offering a structured methodology aligned with TOE-DOI dimensions, the paper contributes operational guidance to a domain often dominated by abstract strategy. The proposed model and primary roadmap are particularly relevant in a fast-evolving environment shaped by the rise of generative AI and emerging regulatory norms. An emerging enabler in this landscape is the rapid development of open-weight high-performance LLMs, such as DeepSeek-R1, Mistral, and Falcon. These models democratize access to advanced AI capabilities by offering SMEs greater transparency, customizability, and control over deployment. Unlike proprietary systems, open-weight LLMs allow for local or cloud-based hosting, fine-tuning, and integration aligned with specific organizational and regulatory contexts--especially critical for SMEs navigating data sensitivity, compliance, and cost constraints. Although the framework offers strong conceptual and practical relevance, its broader applicability depends on continued empirical validation and contextual refinement. A dedicated limitations-and-future-research agenda have been outlined to align strategic, technical, and ethical insights with real-world implementation contexts. These directions reinforce the need for adaptive, inclusive, and scalable pathways to AI adoption in SMEs. Ultimately, this paper contributes to the growing body of research focused on enabling small firms to participate meaningfully in the era of digital transformation. It calls for policies, infrastructures, and leadership models that empower SMEs to adopt AI not merely as a tool, but as a strategic enabler of inclusive and sustainable innovation. The central message is clear: In a world defined by accelerating innovation cycles, SMEs that delay AI adoption today may risk irrelevance tomorrow. **Author Contributions**: Conceptualization, E.S., R.C. and F.H. ; methodology, E.S., R.C. and F.H. ; investigation, E.S., R.C. and F.H. ; writing--original draft preparation, E.S., R.C. and F.H. ; writing--review and editing, R.C. and F.H. ; supervision, R.C. and F.H. ; funding acquisition, F.H. All authors have read and agreed to the published version of the manuscript. **Funding**: This publication is part of the project \"Ethical, Responsible, and General Purpose Artificial Intelligence: Applications In Risk Scenarios\" (IAFER) Exp.