Building Smarter AI: Why Indian Enterprises Need To Rethink Cloud Foundations

Artificial Intelligence is reshaping industries in India, with GenAI spurring changes in workflows, customer experience and service delivery models, cyber and compliance management, as well as predictive diagnostics. Yet, many companies struggle to scale beyond pilot projects despite significant investments.

The disconnect lies not in the vision, but in the infrastructure, specifically, the lack of a robust and future-ready cloud foundation that can support AI at scale. This disconnect becomes especially acute when transitioning from proof-of-concept models to real-time, production grade deployments.

Indian enterprises are grappling with fragmented systems, legacy infrastructure, and regulatory concerns over public cloud usage. For many Indian enterprises, the cloud environment is a patchwork—comprising a mix of on-premises systems, disparate data silos, and loosely connected public or private cloud platforms. This fragmentation slows down data access, increases operational costs, and ultimately stifles AI performance.

These limitations are not just theoretical, business of all sizes face challenges due to fragmented cloud environment in their AI initiatives. In fact, many enterprises find scaling GenAI solutions difficult due to the challenges of maintaining performance and responsiveness across a fragmented infrastructure. Often, these fragmented environments span on-premises systems, private cloud setups, and public cloud services, creating latency issues and bottle necks in data access and compute. Ultimately, enterprises need to rethink their cloud strategy and move toward integrated, scalable architectures to unlock the full potential of GenAI and drive enterprise-wide transformation.

In today’s AI-driven enterprise, the cloud is no longer a backend utility — it is the operating system that powers innovation. From training large models and orchestrating complex workflows to delivering real-time recommendations, cloud-native capabilities play a crucial role. This is particularly true for modern GenAI systems and large language models, which demand rapid scalability, continuous learning and rigorous data governance.

In healthcare industry, for example, providers leverage cloud-native architectures to transform clinical decision-making. Migrating to intelligent data lakes and microservicesbased platforms enables faster deployment of AI-based diagnostic tools. These tools can analyze patient vitals and historical data to assist doctors in predicting critical events like cardiac events. Further, a region-specific cloud setup ensures compliance with India’s strict data localization laws, making it possible to scale solutions responsibly and securely.

These advancements in healthcare are just one example of how cloud-native architectures are enabling real-world AI impact. But as GenAI becomes more central to enterprise strategy, the demands on cloud infrastructure are intensifying. Organizations are quickly realizing that the systems built for yesterday’s analytics and transactional workloads are no longer sufficient. Enterprises must rethink how their cloud environments are designed, deployed, and scaled.

GenAI is pushing the boundaries of what traditional cloud infrastructure was designed to handle. Most existing systems, built for basic analytics or transactional workloads, are falling short when tasked with supporting the modularity, elasticity and orchestration needs of today's AI models.

To deliver business-grade GenAI, cloud architecture must evolve in three critical ways:

• Cloud architecture must become modular and composable. Traditional systems built as single blocks can’t keep up with the pace of AI innovation. Instead, organizations need flexible setups where components like large language models, applications programming interfaces (APIs), vector databases, and prompt tuning tools work independently but connect seamlessly. This modularity allows businesses to adapt quickly, whether they are launching a new AI service or meeting compliance needs. Thus, making industry clouds more agile and future ready.

• Elastic infrastructure is now essential for delivering real-time AI experiences. Static cloud setups don’t cut it when demand spikes, like during customer interactions or code generation. Industry clouds must automatically scale GPU (graphic processing unit) clusters and model endpoints based on usage. This dynamic provisioning ensures performance stays high and costs stay low. It’s the difference between AI that’s experimental and AI that’s enterprise-grade.

• Data must shift from rigid warehouses to intelligent lakes built for AI. GenAI thrives on data which could be text, images, or conversations. AI-ready data lakes support unstructured data with metadata tagging, semantic search, and scalable storage. They turn raw information into strategic insight. For industries aiming to lead with AI, these lakes are the foundation for smarter decisions and faster innovation. 

As GenAI continues to evolve, Indian enterprises must rethink and modernize their data and cloud infrastructure. Those that invest in scalable, intelligent, and sustainable cloud strategies today will be best positioned to lead the next wave of digital transformation — turning AI ambition into enterprise-wide impact. The journey from AI pilots to enterprise-wide deployments relies on a robust and future-ready cloud foundation to ensure seamless delivery, allow for innovation and provide a competitive advantage. 

Kiran Minnasandram is the vice president and head, enterprise applications, technology transformation and advisory, Wipro Ltd.

Disclaimer: The views expressed here are those of the author and do not necessarily represent the views of NDTV Profit or its editorial team.