As AI becomes more prominent at GSX, security practitioners should move beyond capability demos and focus on operational outcomes. Vendors’ demonstrations can be impressive, but the priority for buyers is whether an AI product materially improves security performance, efficiency or resilience.
AI: What problem does it actually solve?
Security teams should ask which operational problem the product eliminates or substantially improves. Does it reduce nuisance alarms, shorten investigation time, improve detection probability, lower operator workload, accelerate response or let fewer people monitor more assets? Request that vendors quantify any claimed improvement: a compelling demo is interesting, but a measurable operational outcome is what delivers value. Deploying AI is a means to improve security performance, not an end in itself.
How accurate is it and under what conditions?
AI demonstrations are often run under favorable conditions; real environments rarely cooperate. Ask for false-positive and false-negative rates and how those figures were established. Probe performance across conditions: at night, in rain or snow, in crowded scenes, with partially obstructed subjects, at different camera angles, and across demographics, facilities or operational environments. A system that performs well in a controlled demo may behave very differently when deployed across hundreds of cameras at a complex facility. The practical question is how accurate it will be in your environment and what design requirements are needed to optimize performance.
What data does the AI need and where does that data go?
AI systems are fundamentally data systems. Understand what information the product collects, processes, retains and shares. Does processing occur at the edge, on premises or in the cloud? Is customer data used to train models? How long is information retained, and can data leave the customer’s environment? Who owns generated metadata, and what happens to that information when the contract ends? As physical security platforms become more cloud-connected and AI-dependent, data governance is increasingly part of physical security architecture.
What happens when the AI is wrong?
Every AI system will eventually err. Critical questions are what the system does next: does it flag items for human review, recommend an action or automatically initiate a security response? Can an operator understand why the system reached its conclusion and override it? The consequences of misclassifying a video clip differ sharply from those of autonomously denying access, dispatching personnel or escalating an incident. Security organizations will need clearly defined boundaries between AI-assisted decisions and AI-authorized actions.
How does it integrate with the rest of the security ecosystem?
A strong AI capability in isolation may offer limited operational value. Verify integration with your video management system, access control, intrusion detection, visitor management, identity platforms, incident management, threat intelligence and other enterprise systems. Ask whether integrations use open APIs or proprietary interfaces, whether data exchange is bidirectional, and whether the AI can initiate workflows across systems. Some of the most valuable AI applications will come from correlating information that historically lived in separate systems, making integration potentially more important than the intelligence of any single device.
Can you prove the business case?
Move beyond the demo and review economics: what does the technology cost to deploy, integrate, license, maintain and operate? Compare those costs with measurable benefits. If an AI platform reduces alarm volume by 80 percent, cuts investigations from 30 minutes to five, or allows one operator to monitor what previously required three, the business case may be compelling. AI that introduces a new capability without improving security outcomes or operational efficiency can become another technology platform to manage.
Look beyond the AI label
GSX offers a view of where security technology is headed, and AI will be prominent across the show floor and programming. Some offerings will represent significant advances, others incremental gains, and some conventional technologies will simply carry an AI label. As you move through exhibits, ask vendors what security decision their AI makes better, what operational problem it eliminates and what measurable outcome it improves. Those answers will reveal far more about a product’s future value than the letters “AI” on a booth.
