At Dell Technologies Forum 2026 in Bengaluru, Rajeev Ranjan, Editor, Digital Terminal, spoke with Mr. Venkat Sitaram, Senior Director & Country Head, Infrastructure Solutions Group, Dell Technologies, about the evolution of enterprise AI, the importance of AI-ready data foundations, infrastructure modernisation, agentic AI, and the role of next-generation storage in supporting enterprise workloads.
Rajeev: Enterprise AI is moving beyond the development of AI models towards real-world deployment. How do you see this conversation evolving, and what should organisations prioritise to make AI an integral part of their business operations?
Venkat: Enterprise AI is evolving rapidly, and AI is increasingly becoming an integral part of business operations. An AI-ready enterprise is one that embeds AI into its core business functions, including sales, marketing, supply chain, manufacturing, logistics, and customer service.
As organisations build AI agents and digital assistants to support these functions, AI begins to deliver more tangible business value through predictive outcomes and improved operational efficiency. The focus is no longer limited to selecting the right AI model; it is also about deploying the right agents and scaling their use across the organisation.
As AI models continue to evolve, organisations must determine which models best meet their requirements and how they can scale their deployments effectively. This is a fundamental shift in the enterprise AI landscape. At Dell Technologies, we aim to be a partner of choice for customers, helping them navigate this transition and scale their AI initiatives.
Rajeev: How is Dell Technologies helping organisations build a strong data foundation for AI adoption?
Venkat: A strong data foundation is the starting point for any AI journey. Many AI experiments have not yet moved into production, and one of the important reasons is the readiness of the underlying data foundation.
Enterprises often have data distributed across multiple silos. Bringing that data together, establishing meaningful connections, and preparing it for AI applications is a complex task. Organisations need a technology partner that understands how to prepare data and build the appropriate data pipelines. Our Dell AI Data Platform is designed to address this challenge. It helps customers create data pipelines, prepare datasets, validate the data, and then apply AI to that validated information.
The quality of the data directly influences the quality of AI outcomes. If an organisation feeds poor-quality data into an AI system, it cannot expect consistently good results. Preparing and validating data is therefore a foundational step in enterprise transformation and successful AI adoption.
Rajeev: Regulated sectors such as banking, financial services, and insurance (BFSI) require high levels of trust and security, and availability. How is Dell Technologies helping these organisations modernise their infrastructure for AI adoption?
Venkat: BFSI is a highly regulated sector that supports millions of citizens through financial transactions. Trust is fundamental to these operations, and the underlying technology infrastructure must be reliable, secure, and consistently available.
Financial institutions are also looking for ways to expand access, improve customer experiences, and use technology to create new business opportunities. For example, a banking customer may increasingly look for additional services, such as portfolio management, through the same financial services ecosystem.
To support these evolving requirements, modernising the technology foundation becomes essential. Dell Technologies works with customers through dedicated modernisation accelerator workshops that focus on areas such as modern storage, cybersecurity, and compute infrastructure.
These engagements help organisations develop a technology framework tailored to their specific requirements and AI deployment plans. By bringing together specialists in data modernisation, infrastructure modernisation, and cybersecurity, we help customers evaluate their options, make informed technology decisions, and identify the outcomes they can work towards achieving.
Our objective is to provide a structured, outcome-oriented approach that helps customers determine their next steps in the modernisation journey.
Rajeev: Agentic AI is changing how enterprises execute workflows and distribute computing workloads. How is this shift influencing infrastructure requirements, and what role does edge computing play in making AI deployments more cost-effective?
Venkat: AI agents are increasingly operating alongside existing business workflows. These workflows could involve query engines, sales management systems, or back-office finance dashboards. As agents become integrated into business operations, AI workloads are becoming more distributed.
This creates a growing need for local compute capabilities rather than relying exclusively on centralised infrastructure. If every workload has to access centralised compute resources, operational costs can become a barrier to scaling AI deployments.
By bringing compute closer to where the workloads operate, organisations can improve the economics of running AI applications. Dell Technologies offers solutions, including GB10- and GB300-based systems and Precision workstations, that support this move towards local AI computing.
Running suitable AI workloads on-premises can offer significant cost advantages compared with running the same workloads entirely in the cloud. The economics become particularly important when organisations are processing billions of tokens over extended periods.
As agentic AI adoption grows, we expect more workloads to move towards the edge. Dell Technologies is addressing this shift with infrastructure solutions designed to support distributed AI deployments and help customers scale their AI initiatives more efficiently.
Rajeev: How does the Dell PowerStore portfolio address the performance, scalability, and data-management requirements of enterprise AI?
Venkat: The new generation of Dell PowerStore is purpose-built to support AI workloads, which have different requirements from traditional applications.
Many modern AI workloads are containerised, while enterprise environments continue to run virtual machines and traditional applications. Organisations need storage infrastructure that can accommodate these different environments and bring file, block, and other data requirements together on a unified platform.
PowerStore provides this unified approach while focusing on performance, scalability, and resiliency. In a compact 3U form factor, the platform can deliver more than one million IOPS, providing substantial performance capacity for demanding enterprise workloads.
Another important capability is its data-reduction ratio of up to 6:1, which can help organisations make more efficient use of their storage resources.
These capabilities enable enterprises to do more with their existing infrastructure while addressing the growing demands of modern applications and AI workloads. As organisations continue to modernise their environments and scale AI adoption, we are seeing increasing interest in PowerStore.
Rajeev: As enterprises move from AI experimentation to large-scale deployment, what is the most important consideration they should keep in mind when building their technology strategy?
Venkat: Organisations need to look at AI transformation holistically. AI adoption is not just about choosing a model or deploying an application. It requires a strong data foundation, modern compute and storage infrastructure, appropriate cybersecurity frameworks, and a clear understanding of the business outcomes the organisation wants to achieve.
This is why a structured approach to modernisation is important. By bringing together the right specialists, understanding the customer's requirements, and evaluating the possible outcomes, we help organisations make informed decisions about their technology investments.
At Dell Technologies, our focus is on helping customers take the next step in their transformation journey and build the capabilities required to scale AI in line with their business objectives.
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