Alex Segeda, EMEAI business development manager at Western Digital, asks whether AI adoption in Europe has reached a tipping point.
What was once experimental is now operational, with organizations in industries ranging from healthcare and manufacturing to traffic management, retail and logistics rushing to implement systems that promise competitive advantage and transformative benefits.
This shift is especially evident in the smart video space, where AI-powered analytics, real-time security detection, and intelligent monitoring are redefining how organizations protect assets and manage operations.
Beneath the surface of this momentum lies an important reality that is often underestimated. For smart video to realize the promise of AI, the choice of infrastructure is as important as the algorithm.
IT decision makers need to think beyond cameras and software stacks. They need to understand how their storage architecture fits today’s data demands, how to make existing surveillance data AI-enabled, and whether today’s systems can future-proof their systems with evolving regulations, privacy expectations, and scale.
Data explosion in smart video
According to IDC, the annual amount of data generated worldwide 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 is contributing to this explosion, driven by a continuous stream of high-definition 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 24/7 across thousands of endpoints, generating vast amounts of data that must be reliably stored, quickly accessed, and retained according to regulations.
AI further enhances this challenge. That desire for data exceeds the assumptions of traditional infrastructure planning.
In a typical AI-powered smart video deployment, video is continuously ingested, analyzed 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, doubles storage and performance requirements.
Underestimating the demands of these heavy workloads or deploying an oversized infrastructure can have a significant impact on your business.
Missed security events, poor retention, poor analysis accuracy, high storage costs, and inability to scale are not theoretical risks.
These are common outcomes when storage is treated as an afterthought rather than a strategic foundation.
Strategic questions for smart video and security leaders
Many security leaders recognize that AI-powered video analytics requires significant computing resources and high-bandwidth connectivity.
Few realize the importance of storage capacity, density, and scalable architecture for long-term success.
The difference lies in asking strategic questions early on. In other words, is my existing surveillance footage centralized or fragmented across sites? What types of workloads do I have and where do they reside? How much data does my system generate? Which workloads justify a cloud infrastructure and which are better served by an on-premises deployment?
These are not just technical considerations. These are fundamental decisions that shape system resilience, compliance posture, and operational efficiency.
Organizations that prioritize storage from day one operate more efficiently than those that try to retrofit capacity later.
Europe’s evolving smart video landscape
In Europe, the rise of a data-driven AI economy, smart video, and data sovereignty is adding even more complexity.
The push for digital sovereignty in the region is reshaping how smart video data is collected, processed, and stored.
Surveillance operators must be aware of requirements and regulatory frameworks such as GDPR, EU data law, AI law, NIS2, DORA, and other regulations.
Video footage containing biometric identifiers, facial recognition, and behavioral insights is now often subject to stringent requirements regarding privacy, transparency, security, and cross-border data movement.
Importantly, these pressures directly impact technical architectural 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.
Data hygiene: The basics of AI-enabled video
However, before deciding where to store video data, organizations should ensure that the available data is suitable for AI use.
Digital data hygiene—the discipline of maintaining data quality, relevance, security, and governance throughout its lifecycle—has become essential to effective AI-powered video systems.
Many organizations struggle with traditional smart video surveillance platforms, fragmented video repositories, unmanaged cloud storage, and duplicate footage.
This “data bloat” creates friction in AI pipelines, increases storage costs, and reduces the accuracy of analytical output.
An AI model is only as good as the data it learns from. Poor quality video leads to unreliable insights.
Strong data hygiene starts with standardization. That means consistent video formats, verified metadata at the time of capture, and removal of corrupt or duplicate footage.
Governance is equally important. Clear video repository ownership, role-based access controls, defined retention policies and audit trails help ensure traceability and compliance.
The role of automation is also increasing. Regular audits, anomaly detection for video quality issues, and ongoing 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.
Rethink storage architecture for AI-powered monitoring
AI-driven video demands are putting an unprecedented strain on traditional IT architectures.
Traditional approaches such as direct-attached storage (DAS), storage area networks (SAN), and traditional network-attached storage (NAS) were not designed with the scale, throughput, and concurrency of modern video analytics workloads in mind.
Hyperconverged infrastructure (HCI) has addressed several challenges by consolidating compute, storage, and networking into a single platform.
However, in surveillance environments where unstructured video data continually increases, tightly coupled architectures can lead to inefficiencies.
Expanding storage capacity also means expanding compute, leading to overprovisioning, higher costs, and architectural rigidity.
As smart video systems expand further, it becomes clear that a one-size-fits-all infrastructure is not the solution.
Scaling smart video with distributed storage
Disaggregated storage architectures are emerging as a key enabler for large-scale smart video deployments.
Separating compute, storage, and networking allows each layer to scale independently based on workload needs.
This flexibility is transformative for surveillance operations. Organizations can add compute resources to support advanced real-time analytics without replacing or expanding their storage platform.
Therefore, you can scale your storage capacity to meet your storage requirements without increasing your computing costs.
This model is suitable for video workloads that require high capacity, sustained throughput, and predictable performance.
Dense, high-performance storage platforms play a central role here, as they provide the performance, cost efficiency, density, and durability required to store vast amounts of video data over long periods of time.
Additionally, these high-capacity storage systems help reduce the footprint, power consumption, and cooling requirements of (edge) data centers while simplifying management at scale.
These efficiency and total cost of ownership (TCO) benefits over the life of a smart video surveillance system are particularly attractive to IT decision makers in airports, rail networks, ports, and smart city environments.
High-density storage systems can reduce data center footprint, power consumption, and cooling requirements while simplifying management at scale.
Over the life of a smart video surveillance system, these efficiencies translate directly into lower operating costs and increased budget predictability.
Infrastructure modernization practices
Despite the focus on AI-enabled cameras and advanced analytics software, the backbone of any intelligent smart video surveillance strategy remains the underlying storage architecture.
Without reliable and scalable storage, AI-powered video cannot function effectively.
Organizations should start by asking themselves, “What is my video data? Where is it stored? How quickly can I access it?”
This change in thinking leads to three key architectural principles:
- Designing for data locality: Deploy storage and compute close to your camera infrastructure, reducing dependence on distant cloud resources. Local deployment minimizes latency, improves analytical responsiveness, reduces bandwidth costs, and supports sovereignty and privacy requirements.
- Invest in scalable storage. Smart video can generate huge amounts of data and must be maintained cost-effectively. A right-sized storage solution is essential to meet retention obligations, support research, and enable AI model development without incurring unsustainable costs.
- Build an ecosystem that balances sovereignty and scale. Sensitive video data can remain in your local environment, while public cloud resources support non-sensitive workloads such as collaborative analysis, model training, and burst capacity.
Adopt an infrastructure-first mindset
AI and data sovereignty requirements are putting local data storage at the heart of Europe’s smart video evolution.
For surveillance operators, system integrators, and enterprise security teams, this is both a challenge and an opportunity.
Companies that recognize that successful AI-powered video relies on a robust, future-ready infrastructure can reap tangible security, compliance, and operational benefits.
As AI becomes integral to modern smart video systems, organizations will succeed by treating data storage as a strategic asset rather than a technical necessity.
Building the right foundation isn’t about being the fastest to implement the latest technology. It’s important to intentionally choose an architecture that supports data growth, respects privacy and regulatory obligations, and scales sustainably over time.
The smart video transformation is not just driven by computing power and software. It is formed by a powerful storage infrastructure that can scale efficiently.
Organizations that make these decisions today will define the next era of smart, safe, and responsible video systems.
