For decades, cameras only recorded what happened, and nothing more. While they saw everything, they understood nothing; just as indifferent to leaves blowing in the wind as they are to a critical security breach. Plus, with up to 95% of enterprise video data left unanalyzed, the result was reactive, inefficient surveillance.
Today, AI-powered video analytics is changing how we think about security and building management. With the market expected to grow to over $12 billion by 2030, AI can detect intent, learn patterns, and connect visual data with other building systems in real time. This shift from reactive monitoring to proactive intelligence drastically changes what cameras can do for people, safety, and operations.
From “or” to “and”: The quantum leap of AI analytics
The journey to this point has been incremental. While we moved from grainy analog to digital IP, the analytics remained primitive: simple, programmable “if-then” statements based on pixel changes. The system could tell you if it saw motion or an object, but its logic was finite and unreliable.
AI shatters those constraints. We’re no longer in the realm of or, but of and. For example, AI-powered video analytics can identify a person and:
- Their direction of travel
- The color of their clothing
- Any objects they’re carrying
Further, it can simultaneously cross-reference all of this information against access-control logs. It’s been projected that organizations implementing these formal AI frameworks will likely achieve a 50% improvement in achieving business goals compared to those without them.
The human in the loop: Building trust with guardrails
This inevitably raises the question of autonomy. The fear that AI will replace human security professionals, while understandable, is misplaced. The goal isn’t to supplant security teams; it’s to support them. While AI handles the analysis of a thousand camera feeds, humans manage the context, nuance, and final decisions.
Think of these policies as the guardrails and training manual for an exceptionally eager new employee. AI requires clear boundaries and continuous human validation to ensure its decisions align with organizational values and real-world context.
Just like that eager new employee should check with their manager for guidance when faced with a situation they aren’t familiar with, AI should be routing low-confidence alerts to a human for confirmation. With every validation, the system becomes smarter and more accurate.
This co-pilot model, where humans set the strategy and AI executes the tactical analysis, builds trust and prevents the “hallucinations” or overeager interpretations that can undermine system integrity.
Beyond security: The strategic intelligence layer
The most compelling applications of this technology extend far beyond security. Video AI is quickly becoming a strategic layer in smart buildings.
For example, in a sports arena, AI monitors for threats and analyzes crowd flow, which can provide intelligence where you’d least expect it. Imagine finding out that most guests arrive from a nearby entertainment district rather than the main parking lot, so the smallest entrance gets the largest influx of visitors. With this real-time insight, managers can redeploy staff instantly, transforming a 30-minute bottleneck into a 5-minute flow. When you calculate that each minute saved can mean $5 more in concession spending per fan, the AI pays for itself, not in risk mitigation, but in direct revenue generation.
This strategic layer integrates with everything. AI can ingest open-source intelligence (news feeds, weather data, traffic reports) to make proactive decisions. Say, for example, a demonstration is forming nearby. AI can proactively secure access points and alert staff to ensure everyone’s safety. Or imagine there’s an impending snowstorm with business and school closings. It can predict a drop in morning traffic and recommend staffing adjustments.
Turning vision into action: How to get started
Implementing AI-powered video analytics is a process of building capability through partnership. At Knight Watch, that journey typically unfolds in four key steps:
- Define the policies. Before the first model is trained, clear boundaries and objectives are set. Policies establish what the AI should detect, how it should respond, and when human oversight is required.
- Start with a proof of concept. A limited deployment helps calibrate accuracy, confirm ROI, and build internal confidence. This stage is where organizations learn how AI interprets their unique environments.
- Govern the data. Transparency is essential. Audit logs, retention policies, and access controls ensure AI systems learn responsibly and remain compliant with privacy regulations.
- Scale with partnership. Once the foundation is set, organizations expand capabilities across sites, continuously refining and retraining models alongside trusted experts.
Each stage is iterative, guided by the principle that AI should always serve human judgment. As a Schneider Electric™ EcoXpert partner, we’re trained and certified to integrate these advanced systems with the highest standards of safety, interoperability, and performance. Transparency and accountability are built into every stage of deployment.
The future is a partner, not a product
At Knight Watch, we believe AI isn’t a product you buy, but a capability you build. It starts with a partnership. From policy design and proof-of-concept to governance and ongoing calibration, we help organizations take practical, measurable steps toward smarter, safer operations.
If your cameras already see everything, maybe it’s time they started understanding it. Contact us to learn how AI-powered video analytics can transform your security and building intelligence.
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