AI inferencing is enterprises' next infrastructure challenge as customers move from pilots to production, says Dell

The company expects enterprise AI investments to extend beyond GPUs into storage, networking, data platforms and end-to-end deployment services.

Enterprise AI is entering a new phase where inferencing, not model training, is becoming the workload reshaping infrastructure investments, according to Dell Technologies, as organisations move beyond generative AI pilots into production-scale deployments.

“Customers are treating AI as a core business capability rather than an experimental technology. That transition is fundamentally changing enterprise infrastructure requirements around storage, automation, networking and data management,” said Venkat Sitaram, senior director and country head, Infrastructure Solutions Group, Dell Technologies India, speaking during an exclusive media briefing in New Delhi.

According to Sitaram, enterprises that initially focused on generative AI are now expanding into agentic AI, creating demand for infrastructure capable of supporting inference-heavy workloads while simplifying deployment and operations.

"Most enterprises, most customers that we talk to are saying we are moving from pilots into production. It's no longer an experiment; it's becoming an integral part of their business strategy," Sitaram said.

Dell argues that the next phase of enterprise AI will be driven by inferencing, as organisations focus on running AI models inside production environments rather than simply training them.

While model training established the foundation for AI adoption, Sitaram argued that the majority of enterprise value is now being created when organisations run models against business data to generate decisions, recommendations and automated actions.

"Inferencing is where most of the work is happening. That's the business layer, that's the intelligence, and that's where the conversation begins," he said.

According to Dell, this is also changing AI's role inside enterprises. Rather than functioning only as an advisory tool, AI is participating in business operations by supporting decision-making, predictive analysis and workflow execution.

The company believes this transition will require organisations to rethink traditional infrastructure that was originally designed for conventional enterprise applications rather than continuous AI inferencing.

Agentic AI driving infrastructure redesign

Dell sees enterprises rapidly expanding beyond generative AI into agentic AI, where intelligent agents execute tasks across multiple business functions with minimal human intervention.

According to Sitaram, this represents a broader shift in knowledge work itself.

He pointed to software development as one example, where AI coding assistants are reducing development cycles from days to hours and, in some cases, minutes. Similar changes, he said, are beginning to appear across research and development, sales, marketing, operations, supply chains and logistics.

"AI is becoming a fundamental operator, no longer an advisor," Sitaram said.

"Deploy agents across workflows, reduce human intervention and execute tasks at a scale and speed that has never happened before."

As enterprises adopt these workloads, Dell expects infrastructure conversations to extend beyond compute into storage, networking, automation and data platforms.

Storage becoming part of the AI conversation

Dell positioned its latest PowerStore Elite storage platform in response to these changing infrastructure requirements.

Rather than focusing purely on storage capacity, the company is emphasising built-in automation, AI-driven operations and simplified management to reduce the operational complexity associated with production AI environments.

According to Sitaram, integrated AIOps automates storage provisioning, workload balancing, predictive monitoring and maintenance, allowing IT teams to focus on deploying production workloads instead of managing infrastructure manually.

"Technology teams can focus on getting production use cases running rather than worrying about provisioning storage or ensuring workloads shift automatically in real time," he said.

Dell also highlighted storage consolidation as an important consideration for enterprises scaling AI workloads.

According to Sitaram, organisations can run more workloads using a smaller storage footprint, reducing rack space requirements alongside power and cooling consumption while lowering overall total cost of ownership.

AI infrastructure now extends beyond GPUs

Sitaram also argued that successful enterprise AI deployments require organisations to think beyond GPU servers alone.

He said Dell's AI Factory, developed in partnership with NVIDIA, brings together data platforms, GPU infrastructure, storage, networking, AI software and validated reference architectures into a single deployment framework.

"The first step is getting your data right because AI depends on quality data to deliver good outcomes," he said.

Once data foundations are established, Dell combines GPU-enabled servers, optimised storage, networking and validated AI software stacks to help customers deploy AI workloads with clearly defined business outcomes.

According to the company, this end-to-end approach has become increasingly important as customers transition from isolated AI pilots to enterprise-wide production deployments.

Looking ahead, Dell believes organisations will measure AI success not by experimentation but by how effectively they operationalise AI across the business.

Sitaram said enterprises do not necessarily need to be "born AI-native," but they must build AI-native infrastructure capable of supporting inference-heavy workloads, automation and agentic AI over the coming years.

"You may not have been born AI-native, but you can become AI-native by leveraging inferencing capabilities and advanced technology solutions," he said.