By Annadurai Elango
AI has moved from hypothetical promise to real-world impact, transforming how businesses prepare for the future. According to the Google Cloud 2025 State of AI Infrastructure Report, 39% of organisations are implementing generative AI in production, while 98% are actively exploring its use. As superior customer experiences become a priority, businesses are eager to unlock AI’s full potential to accelerate innovation and drive sustained growth. However, AI alone cannot deliver scale and agility without a robust technology backbone capable of handling the increased demands.
Successful AI implementation isn’t just about hiring skilled resources. It’s a comprehensive transformation that requires a well-thought-out approach spanning strategy, infrastructure, culture, and governance. The need for compute power, data storage, governance, and scalability underscore the importance of cloud, data, and IT infrastructure, making them fundamental to AI success at scale.
While AI has long been a topic of industry hype, the transition from concept to widespread adoption is proving more challenging than anticipated. Organisations often underestimate the technical, organisational, cultural, and ethical hurdles that can derail AI implementation. Therefore, they need a solid AI infrastructure, spanning application development, deployment, and maintenance, to process, store, and analyse massive volumes of data.
Data readiness is paramount. AI systems not only consume vast amounts of data but also require high-quality data to ensure accurate insights and effective decision-making. One major challenge for AI or ML projects is inconsistent data quality and diverse data sources. In contrast, a modern data infrastructure fosters trust in AI systems, boosts stakeholder confidence, and delivers desired outcomes. Well-defined policies and guidelines enable consistent data governance. Virtualisation provides a consolidated view of data, real-time access, and analysis, without complex integration.
AI systems demand high-volume data processing and intensive computational power in a secure and agile environment, only a resilient IT infrastructure can meet these requirements. Yet, many organisations are burdened with outdated IT systems that pose integration challenges and lack standardised APIs. These legacy assets may become the weakest link, especially in the face of increasingly sophisticated cyber threats.
Migrating legacy infrastructure to the cloud empowers organisations with scalable and flexible computing resources, accelerated deployment, and access to ready-made AI services. Cloud computing provides much needed scalability, flexibility, speed, and ecosystem support that modern AI workloads demands. Depending on specific security and processing needs, businesses can choose the optimal cloud setting, public, private, hybrid, or multi-cloud, to optimise storage and networking for AI workloads.
Emerging technologies will continue to shape AI infrastructure. To future-proof their systems, organisations must continuously evolve their cloud and data strategies, ensuring agility, security, and scalability in a rapidly changing digital landscape. In a world where models, tools, and standards evolve constantly, the key is to design for longevity as much as for functionality.
The writer is president – Core Technologies and Insights, Cognizant
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