Artificial Intelligence as a Catalyst for SME Digital Transformation: Frameworks and Case Studies

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@article{1710477,
      author = {Nkemdirim Mbah},
      title = {Artificial Intelligence as a Catalyst for SME Digital Transformation: Frameworks and Case Studies},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {353-366},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710477.pdf},
      abstract = {This article explores the transformative role of Artificial Intelligence (AI) in driving digital innovation, operational efficiency, and strategic scalability within small and medium-sized enterprises (SMEs). Amid a global digital economy where agility, customer-centricity, and data-driven decision-making define competitiveness, SMEs are increasingly compelled to embrace AI as a practical survival imperative. Through an analytical survey of recent literature and real-world applications, this study presents a comprehensive AI Implementation Framework structured to SME-specific constraints that includes limited budgets, inadequate technology ecosystems, and workforce skill gaps. The research investigates AI?s application across three critical SME functions, which are intelligent customer service automation, decision intelligence and business analytics, and process automation. It further identifies persistent adoption barriers, including financial constraints, regulatory ambiguity, resistance to change, and vendor limitations, and proposes strategic interventions such as public-private partnerships, policy-driven incentives, and open-source AI ecosystems to address these challenges. The article contextualizes its findings through empirical case studies, which successfully deployed AI-powered digital assistants for public service optimization and integrated AI. These cases highlight AI's replicability across regions and governance models, showcasing how structured frameworks, human-in-the-loop governance, and continuous learning cycles can democratize AI adoption beyond large corporations. Lastly, the study emphasizes the need for granular, sector-specific AI frameworks, longitudinal research on SME digital trajectories, and a policy environment that supports inclusive, ethical AI deployment. The cross-regional comparisons emphasize that, while technological capabilities may be global, contextual enablers such as ecosystem support, regulatory clarity, and cultural readiness define successful AI transformations in SMEs.},
      keywords = {Artificial Intelligence (AI), SME Digital Transformation, Customer Service Automation, Decision Intelligence, Process Automation, AI Adoption Barriers, Public-Private Partnerships, Data Governance, UK Case Studies, U.S. Municipal AI, AI Implementation Framework, Inclusive Innovation, Human-in-the-Loop AI, Policy Implications.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV9I3-1710477}
  }