The growing role of artificial intelligence (AI) in professional monitoring was a major focus during The Monitoring Association (TMA)’s recent mid-year meeting, where industry leaders, monitoring professionals, technology providers and public safety stakeholders spent two days exploring the operational realities of emerging technologies in the monitoring environment. 

Across multiple education sessions and technology discussions, a consistent theme emerged: while AI is delivering meaningful improvements in video analytics, event prioritization and operational efficiency, successful implementation still depends heavily on trained operators, clearly defined workflows and responsible deployment strategies. 

At many monitoring centers today, operators are processing more video events than ever before — often across construction sites, logistics yards, multifamily properties, retail environments and commercial facilities, where customers increasingly expect proactive security rather than reactive response. AI is helping manage that scale. Advanced analytics can now distinguish between people, vehicles, animals and environmental movement with far greater accuracy than earlier motion-based systems. Monitoring centers are using AI-assisted filtering to reduce nuisance alarms, prioritize critical events and improve operational efficiency. 

But despite rapid advances in analytics, most monitoring professionals agree on one thing: AI works best when paired with experienced operators — not when replacing them. 

As the industry continues adopting AI-driven video services, the conversation is evolving beyond the technology itself and is now focused on a more practical question: How can AI improve monitoring operations while still preserving the human judgment customers and first responders rely on? 

AI Is Solving a Longstanding Industry Challenge 

For years, one of the biggest operational obstacles in   video monitoring has been nuisance activity. Traditional motion-based analytics frequently generated alarms caused by shadows, insects, weather conditions, moving vegetation, headlights or wildlife. Operators still had to review those events, creating unnecessary workload and limiting scalability. 

Advancements in AI are improving that equation. By pre-screening events for specific objects and behaviors, modern video monitoring software reduces non-actionable activity and helps prioritize events for operator review. Instead of simply detecting motion, AI can detect human and vehicle activity, such as a person entering a restricted area or a vehicle approaching a gate after hours. For many monitoring centers, the operational impact has been significant. 

Monitoring providers across commercial construction sites, logistics facilities, auto dealerships and remote perimeter applications report meaningful reductions in nuisance activity and improved operator efficiency when AI-assisted filtering is properly deployed. By reducing non-actionable video events and helping prioritize activity for operator review, these systems can improve workflow efficiency and help monitoring centers manage growing event volumes more effectively. 

“AI-driven analytics have radically changed the equation for defining and pricing video monitoring,” said Anthony Iannone, director of innovation and industry relations, Affiliated Monitoring. “Now, operators spend less time reviewing nuisance activity and more time focused on incidents where intervention may actually be needed. This means we can monitor more different types of installations with faster response times and more attractive economics that we could just a year ago.” 

The improvements are particularly important as monitoring centers continue facing staffing pressures while managing growing volumes of video data. 

The Shift From Reactive Monitoring to Proactive Intervention 

AI is also accelerating a broader transformation in the monitoring industry: the move from passive surveillance to proactive security intervention. 

Historically, many video systems functioned primarily as forensic tools. Cameras recorded incidents for later investigation, but intervention often occurred after a crime or loss event had already taken place. Today’s monitoring environments are beginning to change that model. 

AI-assisted analytics can identify suspicious activity in real time, while trained operators evaluate context and determine the appropriate response path. Depending on the situation, that may involve: live audio intervention (talk-down), contacting on-site personnel, dispatching a guarding resource, escalating to law enforcement (call for service) or dismissing harmless activity before unnecessary dispatch occurs. 

Construction site monitoring has become one of the clearest examples of this evolution. Sites are frequently active in low-light environments and exposed to changing weather conditions, making them difficult to secure using traditional motion analytics alone. With AI-assisted monitoring, operators can more accurately identify unauthorized after-hours activity while filtering out environmental movement that previously generated nuisance alarms. 

Video Verification Is Improving Response Credibility 

As public safety agencies continue emphasizing verified response policies and enhanced situational awareness, video verification is becoming increasingly valuable. 

When monitoring centers can provide real-time visual confirmation of criminal activity, emergency communication becomes more credible and actionable. Rather than reporting only that an alarm activated, operators may be able to confirm the number of individuals on site, whether weapons are present, vehicle descriptions, points of entry or whether a crime is actively in progress. That additional information can significantly improve situational awareness before officers arrive on scene. 

The industry has also taken important steps toward standardizing how validated alarm information is communicated to emergency communications centers. Developed collaboratively between TMA and public safety representatives, the AVS-01 Alarm Validation Scoring standard establishes a framework for conveying the level of information available during an alarm event, including insights derived from video verification and other enhanced validation sources. As AI-assisted video monitoring continues evolving, standards such as AVS-01 are becoming increasingly important because they help create greater consistency, clarity and communication between monitoring centers and public safety agencies. 

For monitoring providers, operational credibility is becoming an increasingly important differentiator — particularly as customers and public safety agencies place greater value on verified, actionable intelligence rather than unverified alarm activity alone. 

AI Still Has Operational Limitations 

Despite the progress AI has made, most monitoring professionals acknowledge that analytics and automation still have important limitations, especially in life-safety environments where reliability, consistency, escalation procedures and auditability matter. 

That reality was reinforced during technology discussions at the meeting, where industry experts emphasized that monitoring centers are fundamentally different from traditional customer service or chatbot environments. 

AI performs best when systems are properly configured, narrowly scoped and operating within clearly defined workflows. However, monitoring centers routinely encounter variables that can affect performance: poor lighting, severe weather, camera obstructions, inconsistent camera angles, network interruptions, rapidly changing environments and unpredictable human behavior. 

More importantly, AI still struggles with contextual decision-making. A person climbing a fence at midnight may represent a criminal intrusion at one property and an authorized contractor at another. A vehicle parked near a loading dock could indicate suspicious activity or simply an early delivery. 

AI can identify patterns, but it does not fully understand operational context or intent. That distinction remains one of the strongest arguments for maintaining experienced operators at the center of monitoring workflows. 

Industry discussions are also increasingly focused on the risks associated with deploying open-ended or poorly controlled AI systems in monitoring environments. During TMA’s mid-year technology sessions, speakers highlighted concerns including inconsistent responses, hallucinations, missed escalation opportunities, scope drift and AI systems operating outside intended workflows. 

At the same time, many monitoring providers are confronting growing pressures that are accelerating interest in AI-assisted workflows, including unpredictable event volumes, staffing shortages, operator burnout, overtime demands and rising customer expectations for faster response. As a result, the industry’s approach to AI is becoming increasingly pragmatic. Rather than pursuing unrestricted automation, many organizations are focusing on orchestration — using AI to absorb repetitive, high-volume tasks while maintaining human oversight for critical decisions and life-safety events. 

The most successful deployments today are typically focused on narrow, repetitive, high-volume tasks where AI can improve efficiency while remaining within controlled boundaries. Effective implementations require bounded workflows, deterministic operational paths, deep integration with monitoring platforms, configurable guardrails, human escalation protocols, role-based permissions, auditability and accountability. 

Many organizations are also implementing “human oversight gates” within AI-assisted workflows, ensuring that critical actions such as dispatch escalation, law enforcement engagement, use-of-force situations and life-safety incidents continue to require human review and authorization. 

Increasingly, AI is being positioned as part of a broader support system, helping verify, prioritize, route, document and escalate events within predefined procedures while operators remain responsible for high-risk judgment calls and situational assessment. Rather than replacing operators, many monitoring providers see AI as a way to elevate operator effectiveness by reducing operational noise and allowing personnel to focus on complex incidents where human judgment matters most. 

In many deployments, the process surrounding AI ultimately matters as much as the analytics themselves. Or, as one speaker summarized during discussions: “Good AI knows when to stop.” 

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