New Delhi, May 21 -- The cloud was once largely viewed as a tool for reducing infrastructure costs and improving operational efficiency. Today, as enterprises move beyond AI pilots and begin scaling deployments, cloud strategies are being reshaped around intelligence, data and AI readiness. In a conversation with TechCircle, Sunil Golani, Cloud Solutions Head at Ingram Micro, spoke about how AI is changing cloud adoption, the rise of multi-cloud strategies, India's growing AI opportunity and what will define winners in the next phase of enterprise transformation. Edited excerpts:
How is AI-led cloud adoption different from the first wave of cloud migration?
The first wave of cloud migration was largely about moving infrastructure to the cloud to reduce costs, improve scalability and modernise systems. The objective was operational efficiency and smoother workflows.
Today, AI is fundamentally changing that equation. Cloud adoption is increasingly driven by the need for advanced computing capabilities, specialised processing power, integrated data environments and real-time insights. Organisations are designing systems with AI at the core rather than simply migrating legacy infrastructure.
This evolution is creating more agile enterprises that can act on data faster and build intelligent systems. Businesses now need more than automation. They need intelligence through technologies such as agentic AI, where systems can learn, adapt and make decisions based on context.
Are enterprises now choosing cloud providers based more on AI capabilities than infrastructure scale?
Yes. Enterprises are increasingly evaluating cloud providers based on the strength of their AI ecosystems rather than infrastructure scale alone. Reliability and global reach remain important, but differentiation today comes from access to foundational AI models, computing resources, data tools, machine learning platforms, governance and security capabilities.
Organisations are looking for integrated environments that can support the entire AI lifecycle - from model development and deployment to optimisation. This has also accelerated multi-cloud strategies, where enterprises choose providers based on specific business and AI outcomes.
What's the biggest challenge enterprises face when scaling AI workloads today?
The biggest challenge is moving AI from pilot projects into enterprise-wide deployments. Many organisations have tested AI successfully, but scaling requires robust data environments, governance frameworks, secure infrastructure and specialised skills.
One of the biggest misconceptions is assuming AI alone can solve problems. AI systems are only as effective as the quality and accessibility of underlying data. Many organisations continue to struggle with fragmented data environments, siloed systems and inconsistent data structures, which directly affect AI accuracy and performance.
Data security is equally important. As AI adoption grows, enterprises need stronger governance frameworks and compliant infrastructure to protect sensitive information and reduce risks associated with data exposure.
How is the role of distributors like Ingram Micro evolving in the AI-cloud era?
The role of distributors is changing significantly. Earlier, distributors largely focused on product aggregation and sales. Today they are becoming ecosystem enablers.
At Ingram Micro, this includes helping partners access cloud marketplaces, AI solutions, technical training, financing and solution-design expertise. Platforms such as Xvantage are also using intelligence and automation to simplify processes such as solution discovery, quote generation and lifecycle management.
The goal is to help channel partners build stronger capabilities around AI, cyber security, data and multi-cloud environments.
Are channel partners truly ready for the growing demand around AI, data and multi-cloud integration?
Readiness varies, but the market opportunity is significant and evolving rapidly. Partners investing in certifications, specialised skills and consulting-led capabilities are likely to be best positioned.
Customers increasingly expect partners to provide strategic guidance rather than simply implement technologies. They want support across AI deployment, data modernisation, cyber security and managed services. Distributors have an important role in accelerating this readiness through enablement programmes and technology ecosystems.
What makes India such a strategic market in the next phase of AI and cloud growth?
India has several structural advantages - a large digital economy, strong engineering talent and rapidly increasing enterprise adoption of AI and cloud technologies. Government initiatives such as the India AI Mission are further accelerating investments in infrastructure and innovation.
Companies across sectors, including banking, manufacturing, healthcare and public services, are experimenting with AI-led transformation. Combined with innovation coming from Global Capability Centres (GCCs) and domestic enterprises, India is emerging as a major AI growth hub.
Over the next three to five years, what will separate the winners from the rest?
The biggest differentiator will be the ability to convert AI investments into measurable business outcomes. Organisations that move beyond experimentation and identify the right use cases will gain an advantage.
Some of the strongest opportunities lie in improving employee and customer experiences and using intelligent automation to optimise processes. Businesses are already using AI to create smarter workplaces, personalise customer interactions and automate repetitive workflows.
Organisations that can identify and scale these use cases effectively will be better positioned to stay ahead as AI increasingly becomes a core business capability rather than just a technology initiative.
Published by HT Digital Content Services with permission from TechCircle.