As the EU moves forward with the implementation of the AI Law 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 operational demands, geopolitical pressures and increasing regulatory requirements.
While these initiatives aim 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's about scalability, security, and operational readiness.
Without the right infrastructure, even the most sophisticated AI initiatives risk stalling, undermining both Europe's organizational ambitions and aspirations in the global technology landscape.
Infrastructure as a decisive factor in the success of AI
Enterprises 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 related to AI deployment. Algorithms or data alone are not enough.
AI workloads demand computing power, 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 are successful or become another silo, and whether the EU achieves its strategic objectives of developing 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 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 to the private cloud for security or compliance concerns, underscoring the importance of a robust, well-governed infrastructure to realize the potential of AI.
Scalability and operational resilience
AI workloads are dynamic and evolve with data and demand. The infrastructure must be equally agile: scaling flexibly to avoid bottlenecks and ensure fast and secure access to data. A system slowed down 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 cope with surges in demand. Organizations that prioritize these capabilities maximize the value and reach of their AI initiatives, turning infrastructure from a limitation into a competitive advantage.
Resilience is not only an operational consideration but also a regulatory requirement. EU legislation for financial institutions, such as the Digital Operational Resilience Act (DORA), requires resilience in all aspects of financial services information technology infrastructure, with an emphasis on functions that support critical services.
The scalability of any AI application for the financial industry will need to take into account not only the likelihood that it will support a critical service within the meaning of DORA, but also the regulatory and compliance consequences arising from that determination.
Practical steps to scale 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 underscores a growing recognition that hybrid and private cloud strategies can offer both the flexibility needed for high-demand AI workloads and the governance controls needed for regulatory compliance and sovereignty.
This makes them a strong competitive alternative to the hyperscale model that calls sovereignty into question and presents known cost and governance challenges.
1. Assess and align infrastructure
For organizations looking to adopt AI more broadly, the first step is to assess current infrastructure against projected AI workloads, identifying gaps in computing capacity, data accessibility, and cost management.
Building or expanding infrastructure with a focus on scalability ensures that AI initiatives can grow without encountering bottlenecks.
2. Prioritize data integration and compliance
AI is fueled by data, but fragmented or isolated information can hinder both performance and compliance. Ensuring seamless data integration, secure access, and audit-ready channels is critical.
Leaders must prioritize architectures that support interoperability, secure storage, and high-speed processing, enabling AI models to deliver actionable insights quickly and reliably.
Leaders must also evaluate their use cases against the regulatory compliance requirements that impact their use scenario or industry. The uses covered by the EU AI Law are likely to require specific controls and governance that are linked to the data and 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 over infrastructure, supply chain and data provision. Sovereignty will continue to be a political priority, especially for the public sector or critical infrastructure customers.
Therefore, the ability to demonstrate independence from foreign interference in the operation of an AI infrastructure may become a key consideration in public procurement.
3. Incorporate continuous improvement
AI infrastructure is not a set-it-and-forget-it investment. It requires continuous adjustment, testing and optimization to stay aligned with evolving workloads and regulatory expectations.
By taking a proactive and forward-thinking approach, businesses can ensure their AI implementations remain effective and compliant.
Navigation regulation
The need for continuous optimization goes hand-in-hand with navigating a rapidly evolving regulatory landscape that is redefining how AI is developed and deployed, as well as the obligations that come with use cases or industry verticals.
For European organisations, these pressures are particularly pronounced. The EU AI Law is landmark legislation that aims to create a harmonized regulatory framework for the use of AI across member states. Its influence is already shaping business priorities, while more policy initiatives are underway to promote the use of cloud and AI.
In this complex environment, compliance is now a strategic imperative that can determine the success of one's efforts, not an afterthought. Companies must ensure that their infrastructure integrates governance, risk management and transparency to meet regulatory demands and build trust with customers, investors and regulators.
Deploying AI in a non-compliant manner, whether due to infrastructure choices or a lack of effective controls, risks not only reputational damage but also financial penalties and legal action. By integrating compliance into infrastructure design, organizations can turn regulatory challenges into opportunities for ethical and trustworthy AI.
Ensuring European leadership in AI
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 can struggle to deliver value, leaving organizations exposed to operational inefficiencies, high costs, and regulatory risks.
The success of AI in Europe will ultimately be determined not only by the ingenuity of the algorithms but also by the readiness of the infrastructure that supports them.
Leaders who prioritize scalability, operational resilience and regulatory alignment will position their organizations to unleash the full potential of AI, drive sustainable growth and strengthen Europe's competitive advantage.
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