As technology has improved, it has led to an ongoing evolution of video management systems (VMS) from traditional, forensic recording tools into dynamic, proactive platforms driven by artificial intelligence and advanced analytics.
Many have prognosticated what will become of the VMS in this new era of machine learning, deep learning, and neural networks. In a world where AI chipset makers are seen as the driving force behind security technology advancement, it is easy to assume that the VMS might be heading toward obsolescence. However, experts, prognosticators, and even the executives at these chip-making companies have a different perspective.
“It doesn’t mean that the VMS becomes redundant – it just means it has a different role,” says Yaniv Iarovici, VP of Business Development for AI chip maker Hailo. “It might not be called a VMS anymore. It is traditionally called VMS, but, when you think of it, even when you take VMS at face value – video management system – it doesn’t mean a set of human eyes looking at a video at any given time, or even in retrospect as forensics. It is not even implied by the name; it is a management system.”
Expert technologist and consultant Pierre Bourgeix agrees wholeheartedly. “The chip is simply an ingestion vehicle for massive amounts of data,” he says. “We program the chipset to do what we want it to and disseminate that data from that chip. The chip is important because it is so much more powerful than it has ever been. But remember – that chip is just producing zeros and ones. Now we have the challenge of moving that data, and where does it go? It goes to the software.”
In speaking with the experts, it is clear we are now entering a fourth evolution of the VMS – a point in time where it isn’t as much about “video management” as it is about sensor and data management. A time when it is less about reactive forensics and more about proactive analysis. But to understand why much of the industry is mischaracterizing the role of the VMS, it makes sense to look back at its path – where it came from, where it is today, and where it will be tomorrow.
Stage One: The Age of Human-Based Forensics
Historically, the VMS served as a “traffic cop” of sorts. Early systems were designed to record events – often for forensic purposes – relying on analog connections, coax cables, multiplexers, and on-premises recording devices (VCRs, NVRs, DVRs).
The early VMS required heavy user interaction. This model worked well when video was stored as evidence for court cases or to confirm an incident after it occurred.
“A person was always involved because they had to determine what to do based on video events they typically never captured until that point,” Bourgeix explains. “It became a forensic tool primarily for some form of a response from law enforcement, or eventually in a court case. It was never intended initially to be a proactive element because there was no way to respond unless someone was live-viewing the event as it happened.”
Stage Two: The Digital Transition
The transition from analog to digital brought the VMS into the digital domain, which also changed the human element. Instead of watching what happened after the fact on recorded video via VCR or DVR, humans could live-view what was happening and direct an immediate response. The problem is that it’s incredibly difficult for humans to watch hours upon hours of live video waiting for something to happen, which brought about an evolution in video storage and retrieval.
“That is what changed the VMS,” Bourgeix explains. “VMS were still forensic tools, but purpose-built video storage created a way to give a dashboard – a GUI/UX experience to help the human monitoring it able to move cameras from one panel to another to bring them in when there’s an alert.”
This brought about the advent of cloud-based storage and the need to manage increasingly vast amounts of data. As cameras became commoditized, companies began layering in analytics. Early pioneers like Bosch introduced tracking software and basic algorithms, and later solutions such as BriefCam expanded the forensic use of video by enabling faster retrieval of relevant data.
“Analytics, in effect, became layered on top of a VMS,” Bourgeix says. “At the time, you couldn’t put that type of computing at the edge – you needed some kind of ‘box’ on-prem. You had companies coming into the industry with these boxes, and they all had some base analytics and functions. This was really the beginning of the verticalized VMS, which were tailored to certain vertical markets like banking, for example.”
Meanwhile, the increasing volume of data – moving from terabytes to petabytes – necessitated advanced analytics to “de-noise” and extract critical information from an overload of video stream data. And this data needed to be analyzed outside of “the box.”
“The analytics companies developed algorithms so that they can use that traffic cop data, that ingestion data,” Bourgeix says. “More ingestion meant more cameras, and you could manage a terabyte of data, but then it moved to petabytes, and today we’re at multiple petabytes. The VMS was getting overloaded with data to the point it was unmanageable.”
This led to the bandwidth challenges of the 2010s. At that time, the cloud couldn’t handle that amount of data; however, it also led to a boom in the creation of analytics, deep learning, and machine learning to compress that data and reduce the bandwidth needed to leverage just the information we needed.
Stage Three: AI and Edge Computing of Today
Being able to extract the relevant info from the vast amount of video data is what we have come to know of today’s VMS; in fact, today’s landscape is defined by AI’s ability to bring proactive intelligence to management software. Via chipsets and edge computing, AI not only enhances the analytics, but they are embedded directly into hardware – enabling data to be processed at the camera level, reducing noise, and speeding up decision-making before data even reaches the VMS.
Ironically, this machine learning is all necessitated by the need for human oversight. “This change has been brought about due to our incapacity to think, or rather, the decline of critical thinking,” Bourgeix says. “All of this innovation happened to streamline a machine’s ability to recognize and learn data sets that, in turn, allows people to make better decisions using software.”
That is where we are right now. Today, the VMS has its place in security by empowering those who evaluate security threats and vulnerabilities with tools to ingest and orchestrate data. And while the majority of that data still comes from video streams, truly it can come from any source.
It has been called sensor fusion. Bourgeix calls it “fusion VMS.” Whatever you call it, the old-school “video management system” has become a dated misnomer.
“We are starting to see this amazing fusion of AI, analytics, and VMS, which means the traffic cop has ‘fused’ into the analytics as a whole – they have become one,” Bourgeix says. “You don’t have to nail down a name for it, but really, it is more about data dissemination – to dumb it down enough for we humans to understand it all. We have a hard time assimilating this much data and information, but the human still exists in this conversation.”
Adds Bret McGowan, SVP of Strategic Accounts for video surveillance manufacturer Vicon: “Due to the benefits of AI in analyzing massive quantities of video, triggering alerts, and identifying hazards in real-time, we expect edge AI to continue growing and see accelerated adoption. That said, incorporating AI into VMS should not come as an afterthought and is so much more than simply using it for video analytics. AI can be used to enhance video quality, summarize and enable free-text search on live and stored footage, and more.”
Stage Four: Moving Forward with Software as the Glue
In a fusion VMS, sensor data (not just video) is correlated and analyzed to provide targeted, proactive outcomes. Whether it is integrating video, access control, environmental sensors, or behavioral models, the goal is to offer a single “traffic hub” that orchestrates all incoming data, thus transforming a reactive system into one that is predictive and agile.
While the chip is vital for data ingestion and initial processing, the software is what binds the system together. As illustrated, the modern VMS is evolving to become much more than simply a platform for reviewing video – they are now comprehensive data management systems.
“Software has a play in every aspect of what we’re talking about,” Hailo’s Iarovici says. “I can easily think about a very simple architecture where you have the AI-related software – not the firmware within the device, but the software or the drivers and so on – that is completely disconnected from the VMS, and a third party arbitrates between the two. It is about democratizing it and creating that traction where the rubber meets the road. Regardless of what industry it is – security, surveillance, retail, medical, industrial automation – it is about what building blocks are necessary to become the glue between great hardware and great solutions.”
The modern VMS is no longer about having eyes on every camera at all times; instead, it is about prioritizing alerts and providing users with clear, actionable intelligence. “The future control room may even feature a dark screen that only lights up when a true anomaly is detected,” Bourgeix says.
This approach not only minimizes false positives but also ensures that human operators can focus on critical situations, thereby achieving a proactive rather than reactive security posture. The intention is not to eliminate human involvement but to augment it, ensuring that critical thinking and ethical oversight remain central to decision-making.
What it Means for Integrators
For integrators and other industry stakeholders, the evolution of VMS into a sensor fusion platform has profound business implications.
The transition to a fusion model, where AI is integrated into every layer, presents both opportunities and risks. On one hand, enhanced predictive capabilities and streamlined operations can drive significant return on investment for integrators. On the other, it means they must invest in new skills, such as IT programming and verticalized market strategies, to stay competitive.
“The integrator today has to be in a position to be able to program systems to achieve the results that the client is actually asking for,” Bourgeix says. “They need a more verticalized understanding of the technology because if they don’t, they will be squeezed out of the opportunity. Enterprise integrators like Convergint, Allied Universal, and others realize that if they don’t speak to this evolution and don’t apply modernized systems to it, they will be shut out. They can’t just go with the same-old, same-old.”
The fusion of systems becomes critical to how this all works, he says. “There will be no more barriers – it is an open, accessible infrastructure. The reality is, we will no longer have to go through this agonizing path to true integration.”
It has created a world where integrator customers expect value and are not just shopping for features, explains Dr. Boghos Boghossian, CTO of Ipsotek, a manufacturer of AI and video analytics solutions. “Some VMS providers recognized this shift and strategically partnered with computer vision companies to create the pull and offer business-critical insights powered by AI. The near future will take this into another dimension with the introduction of Agentic AI – which will automate and streamline many tasks that operators currently perform manually within a VMS.’’
Bourgeix adds that AI models that are being created are going to be a challenge for older VMS – a fact that integrators must be mindful of. “The [older systems] can’t assimilate the new AI models, which means the users will have to depend on multiple software systems to use and monitor different systems,” he says. “The customer will not want all those software systems – they want all of it in an interface that can be used as a holistic system.”
In the end, the future of the VMS in the modern era of AI is not a simple matter of replacing old systems with new technology. It means a complete rethinking of how data is ingested, processed, and acted upon. As VMS continues its evolution, the ultimate goal remains clear: To enhance human capabilities, not replace them.
