Convergint Calls Out Siloed Systems as Barrier to AI Progress

Why It Matters

Without a structured roadmap and strong governance, AI projects risk delivering little operational improvement, delaying the security industry’s shift toward automated, risk‑preventing solutions.

Key Takeaways

• Enterprises largely remain at AI Level 1 or 2.
• Siloed, proprietary systems hinder cross‑system AI correlation.
• Governance investment accelerates AI adoption more than technology.
• Metrics like triage time and alerts processed gauge ROI.
• Legacy hardware limits future visual intelligence capabilities.

Pulse Analysis

The physical security market has been awash with AI buzz, yet many organizations lack a clear path to operationalize those capabilities. Convergint’s five‑stage framework offers a disciplined progression that separates hype from practical implementation, helping security leaders prioritize data strategy, system integration, and governance before chasing feature‑rich but isolated tools. By mapping current maturity to concrete milestones, the model provides a common language for cross‑departmental discussions and aligns technology spend with business objectives.

A core obstacle identified by Convergint is the prevalence of siloed, proprietary solutions that impede the cross‑system correlation required for Level 3 and beyond. Legacy hardware—such as outdated proximity readers and end‑of‑life IP cameras—further restricts the deployment of advanced visual analytics. The company argues that addressing these architectural gaps, rather than merely adding AI algorithms, unlocks measurable gains in false‑dispatch reduction, evidence quality, and compliance readiness. Simultaneously, establishing robust governance structures clarifies which decisions can be automated and which demand human oversight, turning what appears as bureaucratic friction into a catalyst for faster adoption.

From a business perspective, the framework shifts the ROI conversation from speculative technology hype to tangible operational metrics. Time‑to‑triage, alerts processed per shift, and operator productivity become the yardsticks for evaluating AI’s impact on labor costs and security effectiveness. As organizations move toward Levels 4 and 5—automated recommendation and action—the potential for risk prevention and scalable response grows dramatically. Convergint’s guidance suggests that the next 12‑18 months are critical for setting priorities, investing in governance, and modernizing infrastructure, positioning firms to reap the strategic benefits of intelligent security before competitors catch up.