With the early days of 2025 just beginning, video surveillance technology and all it encompasses celebrates approximately 83 years of innovation and evolution. This marks a crucial point in its history where video systems will evolve into business intelligence systems. In the early days, video systems were rudimentary and fundamental in technology, generally black and white cameras connected to monitors via coaxial cables. Today, cameras are being used for safety and security in cities, buildings, and schools and are being leveraged for business intelligence capabilities in Retail and Entertainment.
Traditionally, video relied on human monitoring for threat detection. AI-driven analytics now provides real-time, automated insights. Algorithms can analyze large volumes of video data to identify anomalies and potential threats humans might miss. By harnessing AI, security teams can respond proactively, reducing response times and enhancing overall safety. It’s time to leverage AI to extend security and business capabilities and unlock new opportunities.
AI-Driven Video Technology
We have discussed video analytics advancements for years. Have you evaluated how to use analytics to extend your organization’s value with proactive monitoring and predictive capabilities? Basic analytics often come with cameras, but many integrators haven’t used advanced features yet. Now is the time for integrators to review offerings, assess partners and vendors, and build an advanced customer roadmap. Practitioners should assess their ecosystem, use cases, and business impact and establish roadmaps and incubation programs to maximize organizational impact.
AI video analytics are expected to grow beyond $52B by 2032, with a CAGR of 22.7% from 2024 to 2032. Video is now the main workload at the edge, enabling scene analysis, object detection, tracking, classification, identification, 3D depth, and navigation. This capability presents new security opportunities.
According to the 2025 Security Industry Association Megatrends Report, “Of the estimated 90 million cameras installed globally, very few use AI, but that is changing. If you further divide those 90 million cameras, a low percentage will use standard analytics (be that in the camera or the video management system (VMS)), and even fewer will use what you might call “real AI.” Most existing security cameras still function as simple scene-recording devices and do not yet provide the “visual intelligence” that this trend conveys. But all of that is about to change. Practitioners today are looking at their video surveillance investments as tools to provide visual oversights of their operations, and that makes this trend inherently connected to 2025 Megatrend No. 3, the undervaluation of security.
After all, if your security cameras are no longer “video surveillance” but provide “visual intelligence,” those systems are worth more to the business. Of course, providing these additional values requires AI or at least “analytics,” creating a vast opportunity for video product makers, VMS vendors and systems integrators. As practitioners build their technology roadmap and add-on capabilities, understanding key analytics functions they can leverage today will benefit their organization.
Visual Language Models will soon surpass traditional video analytics. Integrating computer vision and natural language processing will transform how we understand and interpret visual content.
What AI Offers
AI enhances new security capabilities and introduces unprecedented operational functions. Understanding how AI can improve prevention, prediction, and response is essential. Agentic AI automates decision-making, while Explainable AI offers transparency in AI decisions. These technologies transform physical security by enabling proactive measures, enhancing situational awareness, and streamlining operations. Agentic AI autonomously detects and responds to threats, reducing response times and mitigating risks. Explainable AI ensures clarity and understanding of AI decisions, fostering trust and aiding human interpretation. Together, they enhance security and optimize operational efficiency.
AI can now use Explainable AI with Retrieval-Augmented Generation (RAG) to quickly provide security teams with security manuals, training, and operations data.
Manufacturers can use Explainable AI with RAG to quickly provide product information for integrators and customers, improving training, product specs, and RFP responses. This method is more accurate and reduces the risk of errors compared to mainstream GenAI.
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