In today's world, video surveillance systems generate a massive influx of data, often overwhelming human operators. This deluge of information makes it challenging to effectively monitor and analyze events in real-time, hindering proactive intervention and efficient post- event investigation. Artificial intelligence (AI) offers a powerful paradigm shift in addressing these limitations. By endowing surveillance systems with the ability to automatically perceive, reason, and learn from visual data, AI promises to unlock unprecedented levels of efficiency, accuracy, and actionable insights. This exploration delves into the crucial need for an efficient mechanism to harness the transformative potential of AI within video surveillance. Efficiency, in this context, encompasses several key aspects: optimized resource utilization (computation, storage, bandwidth), rapid and accurate processing of video streams, scalability to handle large deployments, and seamless integration with existing infrastructure. Without a robust and efficient underlying mechanism, the promise of AI-powered surveillance risks being bottlenecked by practical limitations. The subsequent discussion will highlight the challenges and opportunities in building such an efficient mechanism, exploring various AI techniques like deep learning, computer vision algorithms, and edge computing strategies. It will also touch upon the importance of data management, model optimization, and system architecture in achieving truly efficient and impactful AI-driven video surveillance applications. The goal is to pave the way for surveillance systems that are not merely passive recording devices but intelligent sentinels capable of proactively safeguarding our communities and assets.