Why AI matters at this scale

Hexagon's Physical Security Solutions (formerly Qognify) provides enterprise-grade video management software (VMS) and incident response solutions for critical infrastructure, transportation hubs, city centers, and large campuses. As part of the global Hexagon AB conglomerate, the company serves a massive, security-conscious clientele that generates petabytes of video and sensor data daily. At this enterprise scale (10,000+ employees globally), manual monitoring and forensic review are economically unsustainable and operationally inefficient. AI is not a novelty but a core operational necessity to derive actionable intelligence from this data deluge, enabling proactive security and measurable business value for customers.

Concrete AI Opportunities with ROI Framing

1. Automated Threat Detection & Triage: Deploying convolutional neural networks (CNNs) for real-time video analytics can identify security threats—from loitering to unauthorized access—with high accuracy. The ROI is compelling: reducing false alarms by over 90% decreases wasted responder time, while instant genuine alerts can prevent asset loss or safety incidents, directly protecting customer revenue and reputation.

2. Predictive Risk Analytics: Machine learning models can analyze historical incident data, weather patterns, and crowd density metrics to forecast security hotspots. For a transportation client, this could mean pre-deploying staff before a predicted disturbance. The ROI manifests as optimized resource allocation, potentially reducing required personnel by 15-20% during normal operations while improving coverage during high-risk periods.

3. Intelligent Forensic Investigation: Natural Language Processing (NLP) allows investigators to search hours of video using plain-language queries (e.g., "find the person in a blue jacket near exit 3"). This reduces investigation time from days to minutes. The ROI is clear: slashing the labor cost of post-incident reviews and accelerating resolution, which minimizes operational disruption for clients.

Deployment Risks Specific to Large Enterprises

For a company of this size and within the sensitive security sector, AI deployment carries distinct risks. Integration complexity is paramount, as solutions must interoperate with a vast array of legacy on-premise hardware and software across customer sites, requiring robust APIs and potentially lengthy certification cycles. Data privacy and compliance present a significant hurdle, especially for global operations navigating regulations like GDPR. Processing biometric or personally identifiable information (PII) in video feeds demands stringent governance. Finally, organizational inertia can slow adoption; coordinating AI strategy across a large, matrixed organization and aligning it with the parent company's broader goals requires strong executive sponsorship and clear change management to avoid pilot projects stagnating.