Data centers are now regarded as the invisible foundation of the digital economy. They are severely under the spotlight amid the artificial intelligence (AI) boom – according to ICIS, a commodity market intelligence firm, European data center capacity is projected to surge over the next decade, from approximately 9.2GW today to 26.6GW by the year 2035.

Although these facilities have the potential to drive massive economic growth in the regions they are constructed in, they are concurrently placing increased pressure on already stressed power grids. The top four consuming nations, Germany, the UK, France, and the Netherlands, make up roughly 62 percent of Europe’s entire data center demand, and current energy usage is already under question in each nation.

Up until now, a large portion of the conversation around sustainability has centered on the duties of those operating the data centers, such as enhancing cooling technologies, hardware efficiency, and the use of renewables. However, another essential factor exists: the way enterprise clients architect their systems to tap into the resources of these facilities.

By migrating from outdated and inefficient continuously running AI models toward reactive, real-time frameworks, businesses can significantly lower the demands of data transfer and computing. This eases the burden on power infrastructure while continuing to capitalize on the potential of AI expansion.

Transitioning to ‘always available’ means eliminating architectural bloat

In the AI economy, speed is strategic, but the traditional “request-response” model is failing the sustainability test. In legacy environments, systems continuously poll one another for updates, staying “awake” simply to wait for a request.

The result? A staggering waste: 60 percent to 80 percent of server power is typically wasted during idle periods. This is the architectural equivalent of leaving a car engine running for hours, just in case it’s needed for driving.

As AI workloads intensify, these inefficiencies create a “distributed monolith” – a tightly coupled system that appears modular but remains functionally inseparable. When one system in the chain experiences latency, the entire resource stack remains engaged, burning compute cycles and electricity while waiting for a handshake.

This “always-on” dependency drives a costly cycle of over-provisioning for customers. To avoid crashes during peak demand, organizations are forced to “overbuild,” sizing their infrastructure for the busiest hour of the year. The consequence is massive amounts of hardware powered on but underutilized, leading to a drain on both capital and the environment – one that tightening sustainability mandates can no longer accommodate.

Intelligent data movement = more efficient processing

Breaking this cycle of waste requires an architectural pivot, away from rigid, overprovisioned systems toward a more responsive, event-driven foundation that optimizes how data moves.

At the core of this shift is the event mesh – a dynamic network of interconnected event brokers that allows data to flow continuously across systems and in real-time, without the overhead of legacy polling. Instead of systems constantly requesting updates, information is shared as it changes, delivered instantly to wherever it’s needed.

This means digital infrastructure components like applications, services, Edge devices, and AI agents publish events as their state changes – whether a workload completes, a threshold is breached, or a failure occurs. These events trigger immediate, decoupled responses across systems, enabling real-time orchestration without tight dependencies.

This transforms how compute resources are utilised in two critical ways:

Reduces idle polling: Systems are triggered only when a relevant event occurs – such as an AI inference request or operational signal – allowing unused resources to scale down or be redeployed instead of sitting idle.

Decouples system speeds: Event brokers absorb spikes in demand, enabling backend systems to process workloads at a steady, optimized pace, rather than maintaining excess capacity for short-lived peaks.

For AI workloads, which are notoriously spiky and unpredictable, this precision ensures that processing power is applied precisely when and where it matters, reducing both infrastructure strain and overall energy consumption.

Managing AI processes with maximum efficiency

As data centers evolve to support increasingly complex AI workloads, the challenge extends beyond moving data efficiently to coordinating how that data is acted upon. This is where an agent mesh comes into play.

Building on an event-driven foundation, an agent mesh introduces a distributed layer of intelligence that operates with shared, real-time context to automate AI agent workflows and maintain situational awareness across data center operations.

By ensuring events are delivered reliably and in the correct sequence, customers can avoid misaligned decisions, reduce redundant processing, and maintain stable, energy-efficient operations.

This shared intelligence allows them to optimize how AI resources are used, rather than simply scaling infrastructure by throwing more GPUs at a problem. It’s the difference between a hundred people shouting in a room and a well-coordinated team sharing a single, real-time script.

In essence, event mesh delivers the real-time flow of information to drive operational efficiency, while the agent mesh determines how best to act on it with full, real-time context. Together, they support enterprise customers with a more coordinated, adaptive system that improves utilization while minimizing wasted energy, supporting both performance and sustainability objectives.

An essential focus for sustainable European AI and data center growth

Managing the resources required to power AI-enabled processes efficiently is no longer optional; it is rapidly turning into a mandatory requirement for doing business.

The road ahead for data centers will not just be determined by the construction of physical infrastructure or what powers the server rooms. Rather, enterprise clients will increasingly be judged on the efficiency of their own IT architecture, especially regarding the intelligence, accuracy, and sustainability of their own AI deployments.

By harnessing a smart approach to AI computing usage, Europe has the chance to establish a worldwide standard for the efficient utilization of data centers. These environments can be built to be not just high-performing, but also resilient, highly efficient, and designed for long-term sustainability.