<![CDATA[This conceptual and integrative review develops a framework for human-AI collaboration in micro, small, and medium enterprises. It addresses the problem that AI tools can widen MSME analytical and service capacity, yet uncritical adoption may introduce opaque errors, privacy exposure, skill erosion, and customer distrust. Drawing on the resource-based view, dynamic capabilities, organizational learning, absorptive capacity, social capital, and resilience, the paper explains how task-appropriate augmentation, human review, data minimization, explainability, continuous capability development can be organized as mutually reinforcing routines. The proposed pathway moves from problem diagnosis and capability mapping to bounded experimentation, evidence review, resource reconfiguration, and learning retention. No primary survey, interview, experimental, administrative, or statistical data are claimed. The framework links these mechanisms to service quality, responsible innovation, employee learning, customer value, controlled scalability and identifies managerial, institutional, and research implications suitable for resource-constrained enterprises.]]>
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