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Objectives: Small and medium-sized enterprises (SMEs) are vital to economic development worldwide, contributing significantly to employment and innovation. However, these businesses often face challenges when adapting to rapid technological advancements, particularly in the area of artificial intelligence (AI) for remote work. In the context of Turkey, SMEs encounter barriers such as limited resources and a strong preference for in-person collaboration, which hinder effective remote work management (RWM). Methods/Approach: This exploratory study investigates how AI-augmented digital leadership can improve remote team performance. Using a mixed-methods design, the research combines a systematic literature review with survey data from 200 respondents (77% response rate). Results: AI-driven performance monitoring and AI-enhanced digital training are analyzed through multiple linear regression, with sensitivity analyses further supported by a 20% remote employee sample and qualitative themes to mitigate managerial bias and deepen contextual understanding. Conclusions: The findings indicate that AI-driven monitoring significantly enhances effectiveness. Grounded in the Technology Acceptance Model (TAM), Resource-Based View (RBV), Transformational Leadership Theory (TLT), and Algorithmic Management, with collectivism as a moderator, this study introduces a novel framework, actionable toolkit, and policy recommendations. Comparisons with global research emphasize the contributions made in collectivist contexts, offering SME leaders practical guidance for optimizing remote work in the digital era.