The Professional Video Monitoring Market: A 2026 Market Analysis of Reactive Alarm Monitoring, Remote Video Guarding, and the AI-Driven Shift to Verified Detection

The remote video monitoring market is quietly reorganizing the entire professional monitoring business around a single admission the category spent forty years avoiding: the signal it sells is almost never real. Between 94 and 99 percent of the alarm activations that reach police are false, according to problem-oriented policing research compiled for the U.S. Department of Justice, and that number is not an operational nuisance at the edge of the business, it is the business. A model that dispatches armed responders to a signal that is wrong nineteen times out of twenty, or ninety-nine times out of a hundred, was always going to be pressured, first by the police departments that stopped answering and then by video monitoring that promises to sell verified events instead of unverified noise. This Market Analysis maps that reorganization, the segment sizes, the operating models, the labor economics, and the buyer's decision, and locates where AI-driven proactive detection fits inside it.

The evidence base here is drawn from primary sources on purpose, because the monitoring category is thick with vendor claims and thin with independent measurement. The false-alarm figures come from the Arizona State University Center for Problem-Oriented Policing and the DOJ Office of Justice Programs. The labor numbers come from the Bureau of Labor Statistics and ASIS International. The verified-response adoption record comes from the Security Industry Association. The standards framework comes from UL and The Monitoring Association. Read together, they describe a market moving from reactive alarm monitoring toward proactive, AI-assisted video monitoring, and a buying decision that is no longer "which central station" but "monitor which way, and who does the watching." It sits alongside the rest of the IntelliSee Intelligence library as a reference for security and risk leaders making that call.

The reactive monitoring model runs on a signal that is wrong almost every time

Traditional alarm monitoring is a relay business: a sensor trips, a signal travels to a central station, an operator calls the premises and then dispatches police. The model's defining statistic is its false-alarm rate, and that rate is catastrophic by any other industry's standard. Research summarized by the ASU Center for Problem-Oriented Policing, part of the DOJ-funded problem-oriented policing library, finds that 94 to 99 percent of police responses to burglar alarms are to false activations, with individual jurisdictions reporting the high end: the LAPD has fielded in excess of 100,000 alarm calls a year with roughly 97 percent false, and Chicago police have logged more than 300,000 activations a year at about 98 percent false. The problem-oriented policing analysis estimates false alarms consume as much as $1.5 billion a year in police time nationally, with each response tying up roughly twenty minutes of two officers' time.

That cost is the pressure that has been reshaping the market from the demand side. Some police departments simply stopped absorbing it. The response was verified response, a policy requiring physical or visual confirmation of a crime before police roll. Where it was adopted, the effect on call volume was immediate. Salt Lake City, the first major U.S. city to adopt verified response in 2000, saw what police described as an immediate 90 percent reduction in alarm responses. Los Angeles moved to require verification after finding roughly 92 percent of its 136,000 annual alarm calls were false, costing about $11 million in lost patrol time. The lesson buyers should take from verified response is not that it is the answer, but that the reactive model's core deliverable, an unverified alarm, has been losing its value to the one responder that matters.

The nuance, and it is a market-defining one, is that policy-driven verification has largely stalled. According to the Security Industry Association, only about 19 of roughly 18,000 U.S. law enforcement agencies have formally adopted verified response, and at least eleven agencies that tried it later reversed course, among them Dallas, San Jose, and Madison. Verified response is politically fragile because the public expects police to answer an alarm, and voters say so: a survey after Salt Lake City's ordinance found 65 percent believed police should respond without waiting for verification. That fragility is precisely why technology-driven verification matters more than policy-driven verification. If the mandate to verify will not come from city hall, it has to come from the monitoring architecture itself, which is the opening the video-monitoring segment has moved to fill.

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The remote video monitoring market is large, fragmented, and growing fastest at its video edge

The professional monitoring market is not one market but a stack of segments with very different growth rates. At the base sits the broad alarm-monitoring category, valued in the mid-sixty-billion-dollar range for 2025 across residential and commercial and growing in the low-to-mid single digits. Inside it, the central monitoring station segment, the third-party operations centers that actually watch signals for other companies, was sized at roughly $11.5 billion for 2025 in one industry estimate and higher in others, growing at a high-single-digit to roughly 9 percent annual rate. The fastest-moving slice is remote video: the remote video guarding segment was pegged near $3.2 billion in 2024 and is forecast to grow at roughly 11 percent a year, well ahead of the base category, with the broader remote video monitoring market growing faster still. The pattern is consistent across sources even where the absolute numbers differ: the video-verified, AI-assisted edge of monitoring is compounding faster than the reactive core.

Consolidation is the market's second tell. When a large platform buys capability rather than building it, it is signaling where the segment is heading. In February 2025, Alarm.com acquired CHeKT, a cloud-based remote video monitoring provider, explicitly to give central stations proactive video-verification and response capability, folding a video layer onto an installed base built for reactive signaling. That is the strategic logic of the whole segment in a single transaction: the reactive relay business is buying its way into verified video because verified video is what the demand side now wants. Buyers reading the market should treat acquisitions like this as leading indicators, not press releases, they map where capability, and therefore pricing power, is migrating.

What none of the market-sizing reports resolve for a buyer is the operating-model question underneath the growth, because the video-monitoring label covers at least three different things that cost and perform differently. The next section separates them.

Labor economics are the force bending the market toward automation

The strongest structural driver in professional monitoring is not the false-alarm problem, it is the labor problem underneath both guarding and staffed monitoring centers, and it is a matter of public record. The Bureau of Labor Statistics counts roughly 1.27 million security guards in the United States and projects essentially zero net employment growth through 2034, yet the occupation is expected to see on the order of 162,000 openings a year, a figure driven almost entirely by turnover and replacement rather than expansion. The BLS Occupational Outlook Handbook itself notes that advances in remote monitoring technology, including cameras integrated with AI, may limit future guard employment, an unusually direct government acknowledgment of the substitution this report analyzes. The median wage sits near $38,370, and in real terms guard pay has been close to flat for two decades. The supply of the labor the traditional model depends on is not growing, and the labor it does attract does not stay.

The turnover data makes the point sharper. ASIS International, the profession's largest membership body, reported guard-force turnover of about 77 percent in 2024, up from roughly 69 percent before the pandemic, and contract firms at the high end have reported rates approaching 300 percent, meaning some accounts churn their entire posted force three times in a year. A monitoring or guarding model that assumes a stable, trained, awake human at a post is quietly assuming away the single hardest input to secure. Every hour a monitoring buyer can shift from "a person must be watching" to "an algorithm watches and a person verifies exceptions" is an hour insulated from a labor market that is not cooperating. This is the same pressure driving the broader ROI case for AI-augmented guard operations, viewed from the monitoring side of the ledger rather than the guard-post side.

The economics compound in the buyer's favor. A staffed on-site post or a headcount-scaled remote-guarding contract prices roughly linearly with hours covered; doubling the coverage roughly doubles the cost, and every incremental hour is exposed to wage inflation and turnover cost. AI-assisted detection inverts that curve: once the analytics run against a camera, the marginal cost of watching that camera for one more hour, or one more camera on the same platform, is close to zero. The verification labor does not disappear, no system is free of false alerts, but it collapses from "continuous watching" to "resolving surfaced exceptions," which is a fundamentally smaller and more scalable job. That is the arithmetic pulling the market's growth into its video-and-AI edge.

Standards still assume a staffed central station, which shapes the buying decision

The monitoring category is governed by standards that were written for the reactive model, and a buyer needs to know where those standards do and do not travel. The anchor is UL 827, the Standard for Central-Station Alarm Services, which a monitoring operation must satisfy to earn a UL Listing. UL 827 governs the physical hardening of the facility, redundancy of receivers and power, minimum staffing and operator training, signal-handling time, and record-keeping, and UL conducts an annual audit to confirm continued compliance. A UL-Listed central station is a meaningful credential for insurance and for certain code-required fire and burglary monitoring, and it is built around the premise that trained humans staff a hardened room around the clock.

Layered on top is The Monitoring Association's Automated Secure Alarm Protocol, ASAP-to-PSAP, a public-private program with APCO that delivers alarm data digitally into a 911 center's dispatch system rather than by phone. TMA reports ASAP live in more than 100 emergency communications centers and credits it with cutting dispatch time by about two minutes per call by removing the phone relay. ASAP is a genuine improvement, but note what it optimizes: it makes the transmission of an alarm faster and more accurate; it does not make the alarm itself more likely to be real. The verification problem lives upstream of ASAP. The full detail of these frameworks, TMA's AVS-01 alarm-verification scoring and the ASAP rollout, is covered in the companion standards briefing on alarm verification; the market-analysis point here is narrower.

The point is that the credential architecture rewards being a staffed central station, and does not yet fully account for on-site AI detection that verifies before any central station is involved. For a buyer, that creates a real evaluation gap. A UL-Listed monitoring contract carries an auditable pedigree; an on-site AI detection layer carries different evidence, model performance, integration with existing cameras, and data-handling posture, and does not slot neatly into the UL 827 box because it is not trying to be a central station. The right procurement move is not to demand the wrong credential but to evaluate each layer on the assurance that actually matters for it, and to recognize that the standards will lag the architecture for some time yet.

The monitoring market's four operating models, side by side

Where each model employs the watcher, how its cost scales, and what a buyer is actually contracting for. The reactive column is the incumbent the market is moving away from; the on-site AI column is where growth is compounding.

Labor and false alarms converge on the same conclusion for the buyer

Two independent pressures, the labor supply documented by BLS and ASIS, and the false-alarm burden documented by the problem-oriented policing literature, point at the same procurement conclusion from different directions. The labor pressure says a model that depends on continuous human watching is exposed to a workforce that is not growing and does not stay. The false-alarm pressure says a model that dispatches on unverified signals is losing value with the responders it depends on. Both are solved by moving detection and verification upstream, into an analytics layer that watches without headcount and verifies before anyone is dispatched.

That is the structural reason the video-and-AI edge of the market is compounding faster than the reactive core, and it is the reason a buyer's evaluation should weight two questions the reactive model handles poorly. First, does the model produce a verified event or an unverified signal, because the difference determines whether the output is actionable or merely another false alarm to absorb. Second, does the cost scale with hours watched or with cameras covered, because the first curve is exposed to the labor market and the second is not. A monitoring purchase evaluated on those two axes tends to resolve toward the AI-assisted models, not because AI is fashionable but because it is the only architecture that answers both pressures at once.

A buyer's framework: rent a watch floor, rent an algorithm, or own the detection layer

The monitoring decision resolves once a buyer stops shopping for "a monitoring company" and starts deciding which of the four models fits their risk, their labor exposure, and their data posture. The variables that actually move the decision are consistent: what triggers a human's attention, whether the cost scales with hours or with cameras, where the video and data reside, who carries the liability for a missed event, and how exposed the model is to the guard-labor market. Map those five variables and the four models separate cleanly.

For a site whose main risk is code-required fire or burglary signaling to police, a UL-Listed reactive central station is still the compliance-correct base layer, and the question is only how to keep its false-alarm exposure from generating fines and non-response. For a site whose problem is that on-site guards are unaffordable or unstaffable, remote guarding or AI-triggered remote monitoring moves the watch off-site, with the AI-triggered version scaling far better because it does not price by the watched hour. For a site that already runs a camera network and wants to compress the response window without renting anyone's watch floor or handing its video to a third party, on-site AI proactive detection is the model that fits, because it turns cameras already paid for into a verified-detection layer spanning intrusion, weapons, falls, and fire and keeps the data on the buyer's side of the line. Most enterprise and institutional buyers are the last kind, which is why the market's growth is concentrated there. Understanding how detection-to-response actually works on existing cameras is the difference between buying a subscription to someone else's watch floor and building an owned capability.

The honest synthesis is that these models are layers as often as they are alternatives. A campus can keep a UL-Listed central station for its code-required fire signaling, run AI proactive detection across its perimeter, parking, and interior cameras to compress the response window, and reserve human remote guarding for the handful of high-value zones that warrant a live operator. The failure mode is buying a single monitoring contract, assuming it has closed the gap, and discovering during an incident that the model chosen was watching the wrong thing, or watching nothing, because a sensor never tripped. Mapping the risk and the data posture first, then assigning each layer to the model whose economics and assurance fit it, is the discipline that separates a monitoring purchase from a monitoring strategy. That is the same architectural shift, from passive recording to real-time detection, documented in the technology briefing on AI video analytics versus traditional CCTV, read here through the lens of who does the monitoring and what it costs.