As the EU moves forward with the implementation of the AI Act and a broader strategy to promote and facilitate the deployment of AI and cloud infrastructure among Member States , organizations are under pressure to ensure their IT infrastructure can keep pace with the operational demands, the geopolitical pressures and the expanding regulatory requirements.
While these initiatives are intended to boost competitiveness and reduce administrative burdens, much will depend on how harmonization is implemented in practice.
Chief Technology Officer, EMEA, Broadcom.
Conversations around AI have evolved beyond the mere promise of innovation. Today, it is about scalability, security and operational readiness.
Without the right infrastructure, even the most sophisticated AI initiatives risk stalling, undermining both organizational ambitions and Europe’s aspirations in the global technology landscape.
Infrastructure as the Deciding Factor in AI Success
Businesses are investing heavily in generative AI, automation, and AI-driven decision-making, expecting transformative results—from operational efficiency to new services. The reality is that infrastructure underpins everything in AI deployment. Algorithms or data alone aren’t enough.
AI workloads demand compute capacity, seamless data access, and robust compliance controls, all while managing costs effectively. Without an effective cloud foundation, how infrastructure is built, maintained, and optimized will define whether these investments succeed or become another silo—and whether the EU achieves its strategic objectives to develop the right infrastructure that would further increase the success of cloud and AI in Europe.
The stakes are high: 48 percent of EMEA IT leaders report wasting at least 25 percent of their cloud spend, and 90 percent prioritize cost predictability. Infrastructure can either accelerate AI adoption or create bottlenecks, leaving organizations grappling with underutilized investments, performance issues, spiraling costs and serious questions about regulatory compliance and sovereignty.
In fact, 51% of global organizations are moving workloads back to private cloud over security or compliance concerns, underscoring the important of robust, well-governed infrastructure in realizing AI’s potential.
Scalability and Operational Resilience
AI workloads are dynamic, evolving with data and demand. Infrastructure must be equally agile—scaling flexibly to avoid bottlenecks and ensuring rapid, secure data access. A system slowed by inefficient storage or fragmented data environments directly impacts the speed and reliability of AI insights.
Operational readiness extends beyond technical performance. It requires resilience, security, and the ability to handle demand surges. Organizations that prioritize these capabilities maximize the value and reach of their AI initiatives, turning infrastructure from a constraint into a competitive advantage.
Resilience is not just an operational consideration but also a regulatory requirement. EU legislation for financial institutions such as the Digital Operational Resilience Act (DORA) mandates resilience in every aspect of the financial services information technology infrastructure with emphasis on functions supporting critical services.
The scalability of any AI application for the financial industry will need to factor not only the likelihood that it will support a critical service within the meaning of DORA, but the regulatory and compliance consequences that emerge from that determination.
Practical Steps for Scaling AI Strategies
For IT management, the question is no longer whether to invest in AI infrastructure but how to do so in a way that supports scale, cost control and resilience. 93% of organizations value private cloud as the deployment model of choice for their critical applications due to its financial visibility and predictability.
This underlines a growing recognition that private cloud and hybrid strategies can offer both the flexibility required for high-demand AI workloads and the governance controls necessary for regulatory compliance and sovereignty.
This makes them a strong competitive alternative to the hyperscaler model that calls into question sovereignty and has known challenges around cost and governance.
1. Assess and Align Infrastructure
For organizations looking to adopt AI more widely, the first step is to assess current infrastructure against projected AI workloads, identifying gaps in compute capacity, data accessibility and cost management.
Building or expanding infrastructure with a focus on scalability ensures that AI initiatives can grow without hitting bottlenecks.
2. Prioritize Data Integration and Compliance
AI thrives on data, yet fragmented or siloed information can hinder both performance and compliance. Ensuring seamless data integration, secure access and audit-ready pipelines is fundamental.
Leaders must prioritize architectures that support interoperability, secure storage and high-speed processing, enabling AI models to deliver actionable insights rapidly and reliably.
Leaders must also assess their use cases against regulatory compliance requirements either affecting their use scenario or their sector. Uses that are captured by the EU AI Act are likely to require specific controls and governance that is linked with the data and the algorithms as they flow through the infrastructure.
Requirements such as DORA and NIS2 that are linked to sectors are likely to prioritize organizational and technical controls on the infrastructure, the supply chain and the supply of data. Sovereignty will remain a political priority especially for public sector or critical infrastructure customers.
Therefore, the ability to demonstrate independence from foreign interference in operating an AI infrastructure may become a key consideration in public procurement.
3. Embed Continuous Improvement
AI infrastructure is not a set-and-forget investment. It requires ongoing tuning, testing and optimization to remain aligned with evolving workloads and regulatory expectations.
By adopting a proactive, forward-looking approach, enterprises can ensure their AI deployments remain both effective and compliant.
Navigating Regulation
The need for continuous optimization goes hand in hand with navigating a fast-evolving regulatory landscape that is redefining how AI is developed and deployed, as well as the obligations that come together with the use cases or the sector verticals.
For European organizations, these pressures are particularly pronounced. The EU AI Act is a landmark piece of legislation that aims to create a harmonized regulatory framework for AI usage across member states. Its influence is already shaping enterprise priorities while more political initiatives aiming to promote cloud and AI utilization are underway.
In this complex environment compliance is now a strategic imperative that may determine the success of one’s efforts, not an afterthought. Businesses must ensure their infrastructure embed governance, risk management, and transparency to meet regulatory demands and foster trust with customers, investors, and regulators.
Deploying AI in a non-compliant manner either because of the infrastructure choices or the lack of effective controls risks not only reputational harm but also financial penalties and legal action. By integrating compliance into infrastructure design, organizations can turn regulatory challenges into opportunities for trustworthy, ethical AI.
Securing Europe’s AI Leadership
Europe has a unique opportunity to establish itself as a global leader in AI, leveraging its regulatory foresight and commitment to ethical technology.
However, this advantage is not guaranteed. Without scalable, resilient and well-governed infrastructure, even the most advanced AI initiatives may struggle to deliver value, leaving organizations exposed to operational inefficiencies, high costs and regulatory risk.
The success of AI in Europe will ultimately be determined not just by the ingenuity of algorithms but by the readiness of the infrastructure that supports them.
Leaders who prioritize scalability, operational resilience and regulatory alignment will position their organizations to unlock AI’s full potential, drive sustainable growth and reinforce Europe’s competitive edge.
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