Variable Frequency Drives (VFDs) are critical components in industrial motor control systems, essential for energy efficiency but prone to complex failures that cause costly unplanned downtime. While Industry 4.0 has enabled advanced monitoring through IoT and Predictive Maintenance (PdM), a significant "autonomy gap" persists: current systems generate alerts but require manual intervention for diagnosis and corrective action. This paper presents a conceptual framework for an AI-enabled, closed-loop autonomous maintenance and efficiency optimization system for IoT-connected VFDs. We propose a novel five-layer architecture that integrates sensor telemetry, edge/cloud AI analytics, a structured autonomy decision layer, and automated execution systems. The framework explicitly addresses the integration between predictive insights and prescriptive actions, incorporating Digital Twin technology for virtual validation and cybersecurity considerations for safe autonomous operation. As a conceptual design study, this work provides a comprehensive architectural blueprint and implementation pathway, identifying key challenges and industry-specific applications. The framework aims to transition industrial maintenance to a supervised autonomous paradigm. We introduce a Hybrid Level 3 Autonomy framework distinct by domain: the system achieves full closed-loop autonomy for software-defined control actions (e.g., efficiency tuning, load derating, error resetting) after virtual validation. Conversely, for mechanical degradation requiring physical intervention, the system employs administrative autonomy, automatically generating diagnostic reports and scheduling precise work orders for human maintenance teams promising significant advancements in operational reliability, energy efficiency, and maintenance cost reduction.