AI Infrastructure and hybrid cloud driving channel evolution in India: Lenovo insights

As enterprise AI adoption expands across industries, technology vendors and channel ecosystems are increasingly focusing on building integrated infrastructure, hybrid cloud capabilities, and outcome-based solutions. Lenovo is working with partners to adapt to changing enterprise requirements where AI workloads, data management, and hybrid environments are becoming foundational to digital transformation.

In an interaction, Arvind Chabra, Director One Channel, Lenovo India, and Srinivas Rao, Managing Director, Lenovo ISG India, discussed how channel partners are evolving beyond traditional resale models toward AI-driven solution delivery models that combine infrastructure, services, and data capabilities.

AI Infrastructure strategy reshaping enterprise channel models

Enterprise customers are increasingly shifting from product-led purchasing decisions to outcome-driven technology adoption strategies. According to Lenovo leadership, this transition is influencing how channel partners design and deliver technology solutions across industries.

“Customer needs have shifted from specifications to outcome-driven solutions. Customers now expect complete solutions rather than individual components,” said Arvind Chabra.

Partners are adapting by integrating devices, edge infrastructure, data centres, and cloud platforms within unified architectures that support AI workloads across enterprise environments.

“The hybrid AI approach spans across devices, edge, data centre, and cloud, enabling partners to deliver integrated solutions rather than fragmented offerings,” he added.

Hybrid cloud and data centre infrastructure enabling AI adoption

Infrastructure continues to play a central role in enabling enterprise AI deployment, particularly as organisations invest in hybrid cloud environments and data management capabilities.

Srinivas Rao highlighted that partners are increasingly involved in delivering data centre solutions that combine infrastructure with services across hybrid environments.

“Infrastructure and data capabilities are critical. Lack of capability in either area can impact AI deployment success,” Rao said.

The growing need for scalable compute environments and efficient data management is also contributing to increased adoption of GPU-based compute and AI-ready data centres.

“Infrastructure is foundational to AI adoption, and we are seeing growth in GPU-based compute and AI-ready data centre investments,” he noted.

Channel ecosystem expanding role in AI and cloud Solutions

Channel partners continue to remain central to enterprise technology deployment even as hyperscalers and digital marketplaces expand their influence across cloud ecosystems.

Lenovo leadership emphasised that alliances with hyperscalers create new opportunities for partners rather than reducing their relevance in enterprise technology adoption.

“More than 80 percent of business continues to flow through the channel, and hyperscaler alliances expand opportunities rather than replace partners,” said Arvind Chabra.

Digital marketplaces are also contributing to broader access to enterprise technology adoption in Tier 2 markets, allowing partners to expand their reach across emerging regions.

“The partner ecosystem continues to play a central role in enabling hybrid cloud solutions and expanding market reach,” Srinivas Rao explained.

AI use cases emerging across BFSI, Manufacturing and Healthcare

AI adoption is expanding across industries, including BFSI, pharmaceuticals and manufacturing, where organisations are using data-driven automation to improve operational efficiency.

According to Lenovo leadership, enterprise adoption is not limited to specific verticals, although certain industries are demonstrating faster implementation cycles.

“AI is applicable across all sectors, but we are seeing faster traction in pharmaceuticals, BFSI and manufacturing,” said Srinivas Rao.

Examples of AI use cases include productivity improvements in quality processes and optimisation of supply chain operations across industries.

“In the pharmaceutical sector, productivity improvements of 20 to 30 percent in quality processes have been observed through AI-driven solutions,” he added.

Partner capability development critical for AI deployment success

As AI adoption expands, partners are required to build expertise across consulting, deployment, development and managed services capabilities to deliver end-to-end enterprise solutions.

“Foundational AI understanding is essential for all partners, while specialisation depends on whether partners focus on consulting, development, deployment or managed services,” said Arvind Chabra.

Lenovo leadership also emphasised the importance of data engineering and data management skills as key components of AI readiness.

“Both infrastructure and data capabilities are required for successful AI deployment,” Srinivas Rao said.

AI-ready infrastructure and edge computing are driving future growth

Over the next three to five years, AI infrastructure is expected to evolve through increased adoption of edge computing, liquid cooling technologies and high-performance compute environments designed to manage growing data volumes.

Emerging technologies are also influencing the way infrastructure is designed to improve efficiency and reduce operational complexity.

“Increasing demand for GPU-based compute, AI-ready data centres, and edge AI solutions will continue to shape enterprise technology adoption,” said Srinivas Rao.

Partners are expected to play a key role in deploying and managing these infrastructures as enterprises expand their AI capabilities.

“Demand is driven by the need for future readiness and the increasing scale of data processing requirements,” Arvind Chabra added.

AI Ecosystem development supporting local innovation in India

India’s technology ecosystem continues to evolve with increasing focus on building local capabilities in AI infrastructure and solution development.

Lenovo leadership indicated that local manufacturing capabilities, combined with partner-led solution development, are contributing to the broader AI ecosystem in India.

“Manufacturing compute infrastructure locally and collaborating with ISV ecosystems enables development of complete solutions rather than isolated infrastructure deployments,” Srinivas Rao said.

The growth of local innovation is expected to contribute to the expansion of AI use cases across industries and regions.

“AI ecosystems are evolving across regions with use cases emerging from India and across AP,” Arvind Chabra noted.

Read More:

14 Years of ASIRT: Strengthening India’s technology partner ecosystem