## 7 Discussion: Lessons Learned, Limitations, and Future Research Directions
### Limitations and Future Research  
Although this study offers a comprehensive and structured framework for understanding AI adoption in SMEs through the TOE-DOI lens, several limitations must be acknowledged. * First, the proposed challenge mapping matrix (Table 4) is constructed from secondary literature and has not yet been validated through primary data collection or empirical fieldwork. * Second, the findings are context sensitive and may not generalize uniformly across all industries, regions, or firm sizes, particularly given the rapidly evolving nature of generative AI technologies and regulatory landscapes. Third, the framework assumes a relatively linear adoption trajectory, whereas real-world implementation processes are often iterative, adaptive, and path-dependent. These limitations point to the need for an iterative multi method research agenda that can empirically test, contextualize, and refine the framework across business environments and stages of digital maturity. Building on the challenge mapping matrix presented in this study, several future research opportunities emerge that can strengthen both theoretical understanding and practical implementation of AI adoption in SMEs. 1. The proposed model would benefit from empirical validation across sectors and geographies. Although the framework is grounded in robust literature, its operationalization should be tested through case studies, surveys, and longitudinal studies. As shown by [123], AI assimilation affects firm performance via dynamic capabilities such as absorptive capacity and customer agility. Future research could explore how these mediators vary between different levels of digital maturity and organizational size. 2. Studies such as [6; 13] highlight structural disparities in AI adoption between SMEs and larger enterprises. These include differences in project management integration, support levels, and digital readiness. Comparative analyzes and longitudinal research could uncover which internal or contextual factors allow certain SMEs to overcome common adoption bottlenecks such as infrastructure gaps, talent shortages, and strategic misalignment. 3. Strategic alignment remains an underexplored area. Refs. [115; 124] propose structured adoption roadmaps tailored to SMEs, yet these models require broader testing across industries and regions. Future work could examine how such phased approaches perform under varying degrees of resource constraint, leadership involvement, or policy support. 4. Generative AI (Gen-AI) tools represent a transformative opportunity, particularly for smaller firms. However, as noted in [3; 119], there is limited empirical data on how SMEs integrate Gen-AI into workflows or innovation cycles. Future studies should investigate adoption pathways, productivity outcomes, and organizational learning processes associated with Gen-AI tools such as ChatGPT, Copilot, or Jasper. Furthermore, exploring how Gen-AI supports sustainability goals, as discussed by [3], could guide AI strategies oriented to climate in SMEs. 5. Implementing ethical and responsible AI remains another urgent area. Ref. [67] calls for lightweight governance models that can be embedded in SMEs with limited regulatory capacity. More research should explore how such models can be institutionalized, including stakeholder engagement practices, explainability protocols, and ethical impact audits, especially in environments with emerging or fragmented regulatory oversight. 6. Technology-wise, infrastructure readiness and integration continue to be critical barriers, as detailed in [97; 112]. Future research should address modular, cloud-based, and API-driven architectures that support interoperability and scalability, especially for resource-constrained SMEs in emerging economies. 7. In addition, studies at the policy and ecosystem level could build on and [100], investigating the role of public-private partnerships, national digital strategies, and innovation funding to support the AI adoption journeys of SMEs.