Article provided by Anna Acopian SafegateAI, moderator of the Integrate AI Conference 2nd September Tech Talks Theatre.
The surveillance and AV sector is not trending toward AI. It has silently arrived. The global AI in video surveillance market was valued at USD 6.51 billion in 2024 and is projected to reach USD 28.76 billion by 2030, growing at a CAGR of 30.6%. For integrators, installers, and managed service providers, that trajectory represents active procurement decisions happening right now, not projections to plan for later.
The Environmental Dividend You Are Likely Underselling
The strongest commercial differentiator in this market is one most security business is not yet monetising effectively: the environmental ROI of AI-integrated infrastructure.
Permanent 24/7 surveillance fixtures in high-rise buildings and mass public spaces generate continuous data streams. When AI analytics are layered on top, that same infrastructure becomes an energy management instrument. A peer-reviewed meta-analysis published in Energy Informatics (Springer, 2025), covering 126 studies, found reinforcement learning achieves energy savings of 22.3% in smart buildings, while hybrid AI methods achieve up to 28.1%. The payback period on AI-driven building energy systems ranges from 2.1 to 5.8 years.
For businesses serving ESG-committed commercial real estate or green building clients, this matters. A CCTV camera in a commercial tower monitoring an unoccupied floor at 2am can, when connected to the right AI platform, simultaneously inform the building management system to scale back HVAC. The hardware is already installed. The value is in the analytics layer on top of it.
Buildings consume approximately 36% of global energy and contribute nearly 40% of global CO₂ emissions, which means clients in this space face real regulatory and board-level pressure to reduce consumption. Predictive AI that turns security infrastructure into environmental management infrastructure is a genuine value proposition, not a marketing add-on.
The Regulatory Gap Is Both a Risk and an Advantage
Government adoption of FRT and AI surveillance is accelerating well ahead of legal frameworks in most countries. London’s Metropolitan Police reportedly scanned around one million faces in 2025 alone and announced plans to install permanent live facial recognition cameras in South London. FRT is now a core component of security, commerce, and digital identity management worldwide, while legal and regulatory responses remain inconsistent and lack clarity across jurisdictions.
The EU AI Act’s rules on prohibited AI practices and AI literacy obligations came into effect in February 2025, classifying AI systems into risk categories with specific legal obligations or outright prohibitions.
For businesses in this sector: the clients deploying FRT fastest are often the ones least focused on compliance architecture. Building privacy-by-design and regulatory compliance into your integration model is a competitive differentiator today and risk mitigation for your client tomorrow.
The Cost Problem Nobody Fully Budgeted For
This is where the conversation in this industry needs to mature. The benefits of AI integration are real. So is the financial exposure from poorly governed AI adoption.
In 2024, the average enterprise monthly spend on AI was USD 62,964. It is projected to rise to USD 85,521 in 2025, a 36% increase. The proportion of organisations planning to invest over USD 100,000 per month is set to more than double, from 20% in 2024 to 45% in 2025.
The structural problem is that AI cost models behave nothing like traditional hardware or software contracts. Continuous monitoring agents, compliance surveillance systems, and background inference workloads consume tokens against every event they process, regardless of whether any user requested a response. These workloads were minimal in most 2024 deployments but represent a meaningful and rising share of the monthly inference bill in 2026, and they cannot be throttled without degrading the business function they provide.
For managed service providers running 24/7 AI-powered CCTV analytics, this is not a theoretical risk. It is a margin problem already arriving in invoices. Roughly 30 to 50% of AI-related cloud spend evaporates into idle resources, overprovisioned infrastructure, and poorly optimised workloads. Hybrid pricing models drive surprise charges, and 78% of IT leaders report unexpected charges on SaaS due to consumption-based or AI pricing models.
What Your Strategy Needs to Include
Three viable positions exist for businesses in this sector. You can integrate AI analytics as a value-added service layer on existing contracts, justifying margin expansion. You can build AI as a managed service product, carrying the infrastructure risk but owning recurring revenue. Or you can position as the integration and deployment layer for AI platform vendors, capturing margin without carrying the model cost risk.
None of these is wrong. All three require the same discipline: model your AI costs the way you model hardware deployment, with unit economics, utilisation assumptions, and contractual protections agreed before the first camera goes live.
They businesses that adopt AI with a defined commercial model, a compliance framework, and genuine visibility into what the monthly bill represents are the AI ready strategists. The reality is, AI is fast and it’s ready. The question is whether your business model is.
Published by Anna Acopian
Founder & CEO Australia | UK | UAE
SafegateAI