Partners need to ‘build AI adjacencies’ beyond initial hardware sales, says Dell's Venkat Sitaram

Cyber recovery, private AI infrastructure, lifecycle upgrades and edge deployments are emerging as the next wave of channel revenue as enterprises move AI into production.

Indian enterprises moving artificial intelligence from experimentation into production are expected to reshape where channel partners generate long-term revenue, with opportunities increasingly extending beyond one-time infrastructure sales into cyber resilience, private AI deployments, lifecycle services and edge infrastructure.

Speaking to CRN India, Dell Technologies India senior director and country head – infrastructure solutions group, Venkat Sitaram, said partners should view AI infrastructure as the starting point of a broader services engagement rather than the end of a hardware transaction.

According to Sitaram, while infrastructure remains the entry point for most AI deployments today, sustainable profitability will come from building adjacent services around those environments as enterprise AI adoption matures.

"Partners need to look beyond the initial hardware sale," Sitaram said. "Once PowerStore Elite is deployed, they can build cyber recovery on top of it. Then our data domain and data protection portfolio comes into the picture, creating adjacent opportunities over the medium and long term."

Infrastructure opens the door, services build long-term profitability

Dell believes AI infrastructure sales will evolve into multi-year customer engagements rather than one-time product transactions.

According to Sitaram, once customers establish an AI-ready infrastructure foundation, partners gain opportunities to expand into cyber recovery, data protection, lifecycle modernisation and surrounding services.

He said PowerStore Elite's cyber-resilient architecture enables direct integration with Dell's broader protection portfolio, allowing partners to attach additional capabilities after the initial deployment rather than treating infrastructure as a standalone transaction.

Beyond cyber recovery, lifecycle upgrades are also expected to become an important revenue stream.

"As newer generations become available, they can be introduced into the existing cluster non-disruptively," Sitaram said. "Customers can bring in the new systems while continuing to use the existing infrastructure."

That gradual expansion model, rather than periodic infrastructure replacement, creates recurring engagement opportunities for partners while allowing customers to modernise without operational disruption.

"The initial hardware sale provides the primary opportunity, and partners can then build services and adjacent offerings on top of it. That's where they can generate higher margins and additional revenue over time,” said Sitaram.

Challenges the public cloud-first AI narrative

Sitaram also argued that one of the biggest misconceptions among Indian enterprises is the belief that AI workloads must primarily run in hyperscale public cloud environments.

"The biggest misconception is that AI has to run in the public cloud," he said. "It can also be deployed on-premises, and it doesn't have to start at a very large scale."

Instead, Dell expects enterprise AI infrastructure to evolve around hybrid environments where organisations balance private infrastructure with public cloud resources depending on workload, governance and economics.

Sitaram said customer control remains one of the strongest reasons enterprises continue evaluating private AI infrastructure.

"The first advantage is that it is on-premises and under the customer's control. You're building a cloud on your own terms, within your own environment."

He added that economics are becoming equally important as organisations begin deploying AI at production scale.

"As the volume of data grows, the cost of querying and inferencing also increases, and token-related costs on the public cloud can become very high."

According to him, private AI infrastructure allows enterprises to execute larger inferencing workloads while maintaining lower latency and better cost control.

Those conversations are expected to become relevant for partners helping customers evaluate long-term AI infrastructure strategies rather than individual technology purchases.

Edge AI expands the deployment opportunity

Dell also expects AI inferencing to become distributed rather than centralised, opening another area of opportunity for the channel ecosystem.

Sitaram said hybrid cloud and distributed architectures are likely to define the next phase of enterprise AI deployments.

"Hybrid cloud and distributed architectures are the future," he said. "Not everything needs to be centralised."

He argued that as more inferencing workloads move closer to users, devices and operational environments, edge deployments will become increasingly important.

"Most inferencing workloads are happening at the edge, which is why edge deployments are becoming increasingly relevant."

For partners, he said, that creates incremental deployment and services opportunities as customers extend AI beyond central data centres into distributed enterprise environments.

Integration, governance and sovereignty becoming customer priorities

Beyond performance, Sitaram believes enterprise customers are evaluating AI infrastructure around openness, governance and regulatory compliance.

He said organisations want platforms capable of integrating existing infrastructure with modern AI environments while avoiding vendor lock-in.

"PowerStore Elite is purpose-built for AI, but at the same time it integrates with legacy infrastructure," Sitaram said. "Customers want infrastructure that supports heterogeneous environments and avoids lock-in."

He also pointed to governance and data sovereignty as becoming fundamental AI infrastructure requirements as enterprises deploy autonomous AI agents using sensitive customer information.

"When AI agents are helping you make decisions, they need to operate within approved guardrails," he said. "If you're dealing with customer data, you need to store it in the required region or country and comply with applicable regulations."

According to Sitaram, those considerations are making private AI infrastructure increasingly relevant for organisations operating in highly regulated industries.

Looking ahead, Sitaram expects two verticals to generate some of the strongest AI infrastructure demand for the partner ecosystem.

"Definitely BFSI, without any doubt," Sitaram said. "The other major opportunity is the government sector, especially with the large government investments that are coming in."

As AI deployments expand beyond pilot projects into production environments, Dell believes partners that combine infrastructure expertise with cyber resilience, hybrid cloud architecture and lifecycle services will be better positioned to capture long-term customer relationships than those focused primarily on hardware fulfilment.