Traditional surveillance systems relied heavily on human operators to monitor multiple camera feeds simultaneously, but numerous studies have shown that human attention is not suited for this kind of task over extended periods. Operators can effectively monitor only 4 to 8 camera feeds at once, and performance begins to decline sharply after just 20 to 30 minutes of continuous observation. This phenomenon, known as vigilance decrement, leads to a 20-30% reduction in detection accuracy, making human surveillance less reliable as time progresses. After 8 hours of uninterrupted monitoring, the effectiveness drops significantly, increasing the likelihood of missing important events and compromising overall security. When surveillance monitors display a large number of camera feeds - especially in high-activity environments - operator fatigue becomes even more problematic. Research indicates that displaying more than 9 to 16 camera feeds on a single screen overwhelms the human brain, reducing situational awareness and increasing the risk of overlooking critical incidents. In large-scale operations where hundreds or even thousands of cameras are deployed, such as in transportation hubs, city-wide surveillance networks, or industrial facilities, relying exclusively on human monitoring becomes operationally unfeasible. AI-powered CCTV PC software addresses these challenges by using advanced algorithms and machine learning models to analyze massive volumes of video data in real time. It enables automatic object detection, facial recognition, license plate identification, motion analysis, and behavioral interpretation, significantly reducing the burden on human operators. AI systems can detect and prioritize relevant events, eliminating irrelevant footage and minimizing false positives, allowing security staff to focus only on genuine threats. Additionally, the scalability of AI ensures that increasing the number of connected cameras does not require a corresponding rise in human resources. One intelligent system can manage input from thousands of cameras, processing visual data simultaneously and efficiently. Although AI dramatically enhances surveillance capabilities and compensates for the limitations of human attention, it currently serves best in a hybrid model where humans are involved in oversight and decision-making. Nevertheless, for environments with hundreds of cameras and frequent activity, AI-driven CCTV PC software is a critical component for maintaining effective, real-time security monitoring.