The next phase of enterprise AI will be defined not by isolated pilots, but by the ability to scale intelligence across complex, distributed environments. As organisations rethink their cloud and infrastructure strategies, flexibility, openness, and trust have become essential prerequisites for success. Ravi Jain, Vice President, Systems & Cloud Sales, IBM India & South Asia, explains how hybrid cloud, workload-centric architectures, and digital sovereignty are helping enterprises build resilient foundations for AI-driven growth.
As enterprises accelerate AI adoption, why is a ‘hybrid by design’ approach becoming the preferred foundation for scalable and resilient AI transformation?
A recent study by IBM Institute for Business Value and IndiaAI shows that 85% of Indian organisations are still locked in pilot AI initiatives. A key challenge found was the need for better infrastructure to support AI adoption more effectively across the enterprise. A hybrid by design model lets you place each AI workload where it makes the most sense, be it on premises, in a private cloud, or in a public cloud, based on data location, latency requirements, and regulatory constraints. This flexibility eliminates silos, strengthens governance, and improves resiliency, enabling AI to scale across the enterprise without being limited by legacy environments.
That’s why our strategy has been to help our clients with an open, consistent hybrid cloud platform, that provides the unified control needed to orchestrate AI and other workloads across any environment while preserving data sovereignty and compliance.
How are business leaders today redefining cloud strategy, not just as an IT modernisation initiative, but as a direct driver of growth, innovation, and competitive advantage?
Many organisations still view cloud merely as an IT modernisation project, which leaves growth opportunities untapped. But for the leaders, cloud strategy today is being viewed through a business-outcomes lens, how cloud – combined with AI – can accelerate product cycles, deepen customer engagement, reduce TCO, and generate new revenue opportunities.
What is changing significantly is the shift from infrastructure-centric thinking to platform-centric thinking. Organisations are looking to build digital foundations that support continuous innovation, ecosystem collaboration, intelligent operations, and more efficient resource utilisation. Cloud is now directly linked to competitive advantage. IBM’s platform delivers the foundation that turns cloud investments into measurable business differentiation.
With AI and data-intensive applications reshaping enterprise priorities, how do you see organisations moving toward workload-first infrastructure architecture?
Treating every workload with the same results is impractical, and it leads to over-provisioning for some workloads and under-provisioning for the rest. Recognising this, organisations are adopting a workload-first approach where infrastructure decisions are guided by application and business needs rather than by a one-size-fits-all cloud model. This ensures trusted, real time data feeds AI models efficiently, regardless of where the data resides, and therefore significantly improves performance, responsiveness, and governance.
IBM recently acquired Confluent that provides a robust, governed, streaming layer that lets AI agents act on continuously flowing data across distributed environments. With Confluent and IBM’s hybrid cloud services, businesses get a purpose built, real time data fabric that aligns infrastructure exactly to workload demands.
In the AI era, enterprises are under pressure to balance innovation with efficiency. What strategies are proving most effective for achieving cost-performance optimisation at scale?
Enterprises are focusing on simplifying their operating models while becoming more deliberate about how workloads are deployed and managed. Cost optimisation today is driven by better visibility across environments, intelligent workload placement, and increased use of automation to improve resource utilisation.
Organisations are also increasingly adopting smaller, fit-for-purpose AI models that are easier to operationalise, consume less computing power, and deliver faster, more cost-efficient outcomes for specific business use cases. Standardising open, consistent platforms further helps reduce complexity and operational overhead, allowing enterprises to scale innovation efficiently while keeping spend tightly linked to business value.
Many organisations are concerned about vendor lock-in and long-term flexibility. How important are open and interoperable ecosystems in enabling sustainable AI and cloud growth?
Open and interoperable ecosystems are fundamental to long-term success in AI and cloud. Enterprises today need the flexibility to integrate applications, data, and AI models across platforms without being restricted by proprietary architectures. At IBM, we strongly believe that openness drives innovation.
We announced Project Lightwell, our $5 billion initiative with Red Hat focused on strengthening open-source security through AI-driven capabilities and a global engineering ecosystem. By helping enterprises secure open-source software and software supply chains at scale, this project aims to build trust and resilience across the open AI ecosystem, with no lock-in.
Security, governance, and compliance are becoming more complex in distributed hybrid environments. How should enterprises build trust and control into their AI and cloud strategies from the outset?
Building trusted AI systems today requires enterprises to have greater control over their data, infrastructure, and operations across hybrid environments. Digital sovereignty is, therefore, becoming a critical priority, ensuring organisations can decide where data resides, how it is processed, and who can access it in line with evolving regulatory requirements. Achieving this requires governance and security to be embedded into the architecture from the outset through consistent policies, encryption, identity management, and auditability across distributed systems. A sovereignty-first approach enables enterprises to scale AI with resilience, compliance, and confidence.