Dell Technologies has expanded its AI Data Platform with agentic AI context, Nvidia-accelerated data preparation, multi-tenancy, storage performance tools, managed PowerScale for Microsoft Azure, and professional services—all aimed at helping enterprises move AI deployments more quickly from pilot projects into production.
Dell Technologies on Tuesday expanded its AI Data Platform with new capabilities aimed at helping businesses move artificial intelligence projects from pilots into production by making enterprise data easier to prepare, govern, understand, and access across on-premises and cloud environments.
According to a recent Dell survey of business and IT decision makers, 53 percent ranked establishing and scaling AI capabilities as a top priority for the next 24 months. Yet half lack a clear, actionable roadmap spanning AI, data and security, said Varun Chhabra, senior vice president of Marketing for Dell’s ISG Solutions Group.
“But intent and execution are two very different things,” Chhabra said during a pre-briefing press conference. “Half of those leaders tell us that they don’t yet have a clear, actionable roadmap for AI, data, and security. And when we ask them what’s holding them back from scaling AI, data challenges rank among the top three barriers consistently across the globe when we talk to business decision makers.”
[Related: Dell Technologies World 2026: Biggest Dell AI Factory With Nvidia Innovation]
The problem is no longer simply obtaining compute capacity or models, Chhabra said. Enterprise data remains fragmented across clouds, data centers, applications, and edge locations, while governance requirements complicate access. Building pipelines across those silos can slow deployments and limit returns on AI investments.
“The result is what we internally call the pilot-to-production gap,” he said. “The use-case works in the demo. This happens all the time. Or there’s a small pilot rollout, and users are excited, leadership is excited. But when you start scaling it out, all of a sudden challenges—around data governance, not having the right data, or having dirty data impact AI outcomes—come in, and that’s really where the real enterprise scale is held back.”
The three-layered Dell AI Data Platform, which Dell last expanded in May, combines an orchestration engine for ingesting and enriching data, data engines for processing and search, and storage engines that provide file, block and object capacity. Cyber resilience extends across the platform.
Chhabra said Dell is introducing four new advancements to the Dell AI Data Platform that map directly to those layers.
Agentic AI With Business Context
Dell is adding a unified semantic layer designed to give AI agents the business meaning behind enterprise data, along with an enterprise knowledge graph that connects related entities across an organization. Enterprise knowledge agents, curated for specific missions or areas of expertise, can work with a customer’s model of choice, Chhabra said.
“What all of this drives is fewer steps and lower costs because agents don’t have to keep rebuilding contexts in every request,” he said. “They actually have data that can flow into the agent that reduces compute costs and the number of tokens that are generated. There is increased model choice.”
Running those capabilities in a customer’s environment, including disconnected deployments, can also support data sovereignty while improving the speed and consistency of agent responses, he said.
Accelerating AI Data Preparation
Dell is bringing Nvidia accelerated computing into its data processing engine. Nvidia cuDF accelerates processing, GPU-accelerated Apache Spark prepares structured and unstructured data, and Apache Arrow transfers data efficiently between storage and processing with full security throughout the entire process, Chhabra said.
“There’s up to a 20x faster batch processing of data versus if they were doing it just through a CPU without acceleration,” he said. “There’s 4x faster data processing on average across workloads, and every engine in the platform—processing, search capabilities, or analytics—are now accelerator supported.”
The acceleration spans processing, search and analytics, letting businesses use GPU infrastructure for both model execution and the data work that supports AI results, he said.
While AI data preparation is being accelerated by Dell, the company is also adding multi-tenancy, with the ability to support up to 500 tenants in a single cluster, Chhabra said.
The Storage Layer: Open-Source Storage Platform Tool, PowerScale For Microsoft Azure
Dell’s new open-source Storage Performance Tool for ObjectScale and PowerScale lets organizations test realistic, repeatable AI workloads instead of relying only on vendor benchmarks, Chhabra said. It measures throughput and latency across writes, high-concurrency reads, mixed environments and Iceberg queries.
“They can run these benchmarks on their own infrastructure with the same tools that Dell engineering uses internally for benchmarking our products,” he said. “It measures throughput and latency live and verifies persisted data end-to-end. And every result records versions and provenance, so it’s a repeatable process.”
Dell also is offering PowerScale for Microsoft Azure as a fully managed service, Chhabra said. Dell handles deployment, monitoring, maintenance and upgrades, while Microsoft provides infrastructure and billing. The service is generally available first in the U.S. East region.
“Quite simply, this is about taking the enterprise grade performance, the enterprise-grade resilience, the enterprise-grade capabilities in general that customers love about PowerScale and that they can’t often get in the public cloud, and bringing that to a Microsoft Azure environment so that customers don’t have to choose between one or the other,” he said.
New Professional Services
Expanded professional services cover the AI Data Platform lifecycle, from data strategy and production planning to selecting data engines for specific use cases and optimizing deployments as business requirements change, Chhabra said.
“Technology, as we’ve often said when we talk about the AI factory, is only part of what it takes to get AI into production,” he said. “Know-how, understanding of how to actually deploy things, how to plan a deployment out, what work you have to do before you even start thinking about deployment, these are things that have been a critical part of the success we’ve had with the Dell AI Factory.”
The Channel Side
The Dell AI Data Platform will help channel partners move past the infrastructure stuff, said Vrashank Jain, Dell’s director of products.
“It actually helps them solve the problems customers have with AI fragmented data and stuff like that,” Jain told CRN. “There are a lot of partners still trying to serve the need for the infrastructure that’s obviously booming. That’s the same story that’s playing out with our own sales team, which is that infrastructure is not fully utilized until you’ve solved the broader problems, and that’s how partners can elevate themselves to a higher level with customers. They become more of a trusted advisor because they’re not just a transactional sort of vehicle for compute infrastructure or storage infrastructure.”
This opens up partners to provide more value-added services, especially implementation services, Jain said.
“Data platforms are all great, but no data platform is completely turnkey,” he said. Every single data platform in the world has a learning curve. It takes a while to get it implemented and integrated into your data sources. You have to build a pipeline there. That is perfect ground for partners to start bringing in their bench of data experts, which is exactly what the AI Data Platform is meant for.”
Jain declined to estimate how much of his company’s revenue for the Dell AI Data Platform will come from channel partners.
“Channel partners do play a very, very significant role in our storage business, and I would expect that same mix to continue here as well,” he said.
Several of the new capabilities in the AI Data Platform are part of a broader push into Dell’s roadmap, more of which is slated to be unveiled in a month or so, Jain said.
“That whole push will require some new partner certification, some new learning, because it’s brand new stuff that’s taking data platforms into a new world,” he said. “We will be making those available as we’ve already done to what’s shipping right now.”
Jack Hogan, vice president of advanced growth technologies at SHI International, a Somerset, N.J.-based Dell channel partner that’s ranked No. 12 on CRN’s 2026 Solution Provider 500, told CRN that his company sees the major customer challenge related to working with data become a real opportunity.
“By being able to add a platform that can unify some of the context of that information, make that information more accessible, more governable in one central environment, it’s really an important part of what’s necessary to allow companies to unlock the value from the data they have,” Hogan said.
“Ultimately, data is the fuel for AI, the new oil,” he said. “I think the most interesting and important part of the news coming out of Dell is the ability to have that universal semantic layer, that enterprise knowledge graph, that kind of corporate map to the information that allows you to turn that into value in terms of AI processing.”
The new Dell Storage Performance Tool is also important, Hogan said.
“When you move data around, it’s expensive both in terms of where that data needs to land and how long it takes for that data to ultimately be processed,” he said. “Being able to have a system that can reduce the amount of times data needs to be moved from one place to another, and keeping GPUs consistently fed, is only going to help us allow customers to achieve value from the infrastructure they’re purchasing.”
Rob Kim, chief technology officer at Presidio, a New York-based Dell channel partner ranked No. 26 on CRN’s 2026 Solution Provider 500, told CRN that when he originally looked at the Dell AI Data Platform, he thought the company was trying to tackle too much from a data standpoint and compete directly with data lake house architectures like those of Snowflake and Databricks.
With AI, however, things change quickly, Kim said.
“The fact that they’re focused on building context for the thing that clients are looking for the most help on, which is how to make generative AI agents more accurate and more efficient and hallucinate less, that’s when we bought in,” he said. “Obviously, the fact that they are marrying this up with Dell AI Factory and what they’re doing around Nvidia and RTX Pro, that’s all good stuff too. But for us, it’s the fact that we agree on what the market opportunity is.”
Dell has shown that it has identified AI data issues much earlier than its competitors, said Jonathan Kowall, senior director, specialist solutions engineering at Ahead, a Chicago-based Dell channel partner ranked No. 24 on CRN’s Solution Provider 500.
“Nvidia has an AI Data Platform reference architecture to make its technology broadly available to storage manufacturers, and Dell was aligned to that before Nvidia put pen to paper,” Kowall said. “This proved that Dell had a good approach, and they’ve continued to expand on it.”
AI agents are searching and using data, but if they’re looking at data in different places and using different contexts and getting different answers, that’s not good for an enterprise looking to get a very deterministic answer, Kowall said.
“We need to provide not only good data, useful data, but provide data with common context so you can get to the right answer faster, where you have confidence in it,” he said. “That’s how enterprises can move at the AI speeds that they’re being promised. I think Dell has built a solution to start moving in that direction.”
