Alex Segeda, Business Development Manager, EMEAI at Western Digital, asks if AI implementation has reached a turning point in Europe.
What was once experimental is now operational and organisations across industries – from healthcare and manufacturing to traffic management, retail and logistics – are racing to deploy systems that promise competitive edge and transformative returns.
This shift is especially visible in the smart video sector, where AI-powered analytics, real-time security detection and intelligent monitoring are redefining how organisations protect assets and manage operations.
Beneath the surface of this momentum lies a critical and often underestimated reality: For smart video to deliver on AI’s promise, infrastructure choices matter as much as algorithms.
IT decision makers must think beyond cameras and software stacks. They must understand how storage architectures align with today’s data demands, how existing surveillance data can be made AI-ready and whether today’s systems are future-proofed for evolving regulations, privacy expectations and scale.
According to IDC, the annual volume of data generated globally is expected to more than double to 527.5 zettabytes (ZB) by 2029 (Worldwide IDC Global DataSphere Forecast, 2025–2029, May 2025, Doc #US53363625).
Smart video data contributes to this explosion, driven by continuous streams of high-resolution footage from 4K, 8K and even 12K cameras across transportation hubs, retail sites, manufacturing and smart cities.
Unlike other data types, smart video data is persistent, unstructured and highly redundant. Cameras record around the clock, across thousands of endpoints, producing vast volumes of data that must be stored reliably, accessed quickly and retained in compliance with regulatory mandates.
AI intensifies this challenge. Its appetite for data extends beyond traditional infrastructure planning assumptions.
In a typical AI-powered smart video deployment, video is ingested continuously, analysed in real time for anomaly recognition, stored for forensic investigation and reused for model training.
Each stage of the AI lifecycle (from ingestion and training to inference and new data creation) multiplies storage and performance requirements.
Underestimating these workload-heavy demands or deploying undersized infrastructure can have business-critical consequences.
Missed security events, insufficient retention, degraded analytics accuracy, spiralling storage costs and inability to scale are not theoretical risks.
They are common outcomes when storage is treated as an afterthought rather than a strategic foundation.
Many security leaders recognise that AI-powered video analytics require significant compute resources and high-bandwidth connectivity.
Fewer appreciate the importance of storage capacity, density and scalable architecture for long-term success.
The difference lies in asking strategic questions early: Is existing surveillance footage centralised or fragmented across sites? What type of workloads will I have and where will they live? How much data will my system generate? Which workloads justify cloud infrastructure and which are better served by on-premises deployments?
These are not just technical considerations. They are foundational decisions that shape system resilience, compliance posture and operational efficiency.
Organisations that prioritise storage from day one operate more efficiently from those that try to retrofit capacity later.
In Europe, the rise of the data-driven AI economy, smart video and data sovereignty adds another layer of complexity.
The region’s push for digital sovereignty is reshaping how smart video data is collected, processed and stored.
Surveillance operators have to be mindful of requirements and regulatory frameworks such as GDPR, the EU Data Act, the AI Act, NIS2, DORA and other regulations.
Video footage containing biometric identifiers, facial recognition and behavioural insights is now often subject to strict requirements around privacy, transparency, security and cross-border data movement.
Crucially, these pressures directly influence technical architecture decisions.
Where video data resides, how long it is retained, who can access it and how quickly it can be audited all depend on the underlying storage infrastructure.
But before deciding where video data lives, organisations must ensure that the available data is suitable for AI consumption.
Digital data hygiene – the discipline of maintaining data quality, relevance, security and governance throughout its lifecycle – has become a necessity for effective AI-powered video systems.
Many organisations struggle with legacy smart video surveillance platforms, fragmented video repositories, unmanaged cloud storage and duplicated footage.
This “data bloat” introduces friction into AI pipelines, inflates storage costs and undermines the accuracy of analytics outputs.
AI models are only as good as the data they learn from, and poor-quality video directly translates into unreliable insights.
Strong data hygiene practices start with standardisation: Consistent video formats, validated metadata at capture and removal of corrupted or duplicate footage.
Governance is equally important. Clear ownership of video repositories, role-based access controls, defined retention policies and audit trails help ensure traceability and compliance.
Automation plays a growing role as well. Regular audits, anomaly detection for video quality issues and continuous cleansing routines help prevent data drift and maintain model performance over time.
In large-scale environments like smart cities and transportation hubs, these practices are essential for operational sustainability.
The demands of AI-driven video are placing unprecedented strain on traditional IT architectures.
Legacy approaches such as direct-attached storage (DAS), storage area networks (SAN) and conventional network-attached storage (NAS) were not designed for the scale, throughput and concurrency of modern video analytics workloads.
Hyperconverged Infrastructure (HCI) has addressed some challenges by combining compute, storage and networking into a single platform.
However, in surveillance environments where unstructured video data grows continuously, tightly coupled architectures can lead to inefficiencies.
Scaling storage capacity means scaling compute simultaneously, leading to overprovisioning, higher costs and architectural rigidity.
As smart video systems expand further, it becomes evident that an one-size-fits-all infrastructure is not the solution.
Disaggregated storage architectures are emerging as a critical enabler for large-scale smart video deployments.
By decoupling compute, storage and networking, they allow each layer to scale independently according to workload needs.
For surveillance operations, this flexibility is transformative. Organisations can add compute resources to support advanced real-time analytics without replacing or expanding storage platforms.
Accordingly, they can scale storage capacity to meet retention requirements without increasing compute costs.
This model is suited for video workloads that demand massive capacity, sustained throughput and predictable performance.
Highly dense, performative storage platforms play a central role here as they deliver the performance, cost efficiency, density and durability required for storing vast volumes of video data over long periods.
Furthermore, these high-capacity storage systems can help to reduce (edge) data centre footprint, power consumption and cooling requirements, while simplifying management at scale.
Over the lifespan of a smart video surveillance system, these efficiencies and Total Cost of Ownership (TCO) advantages can be especially attractive to IT decision makers in airport, rail network, port and smart city environments.
Highly dense storage systems can reduce data centre footprint, power consumption and cooling requirements, while simplifying management at scale.
Over the lifespan of a smart video surveillance system, these efficiencies translate directly into lower operational expenditure and greater budget predictability.
Despite the focus on AI-enabled cameras and sophisticated analytics software, the backbone of any intelligent smart video surveillance strategy remains the storage architecture beneath it.
Without reliable, scalable storage, AI-powered video simply cannot function effectively.
Organisations should start by asking themselves: “What is my video data, where is it stored and how quickly can I access it?”
This mindset shift leads to three key architectural principles:
AI and data sovereignty requirements are placing local data storage at the heart of Europe’s smart video evolution.
For surveillance operators, system integrators and enterprise security teams, this represents both a challenge and an opportunity.
Those who recognise that successful AI-powered video depends on robust, future-ready infrastructure will gain measurable security, compliance and operational advantages.
As AI becomes integral to modern smart video systems, organisations will thrive when they treat data storage as strategic assets rather than technical necessities.
Building the right foundation is not about deploying the newest technology fastest. It is about making deliberate architectural choices that support data growth, respect privacy and regulatory obligations and scale sustainably over time.
The transformation of smart video will not be driven by compute power and software alone. It will be shaped by powerful storage infrastructure capable of scaling efficiently.
The organisations that make these decisions today will define the next era of smart, secure and responsible video systems.
