<![CDATA[This conceptual and integrative review develops a framework for responsible AI-assisted accounting in micro, small, and medium enterprises. It addresses the problem that accessible AI tools can strengthen accounting work while also creating risks of opaque outputs, weak source validation, privacy exposure, and excessive reliance on automated recommendations. Drawing on the resource-based view, dynamic capabilities, organizational learning, absorptive capacity, social capital, and resilience, the paper explains how task-risk classification, verified data inputs, human professional review, decision traceability, continuous control learning 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 accounting reliability, timely insight, professional accountability, controlled efficiency, auditable decisions and identifies managerial, institutional, and research implications suitable for resource-constrained enterprises.]]>
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