Dell Simplifies Storage for the AI Era

Key takeaways: 

• Storage for the AI era is now a strategic CIO decision, shaping how data fuels AI factories, risk and cost.
• Classic HCI alone is not enough; CIOs need architectures that scale compute and storage independently across multi‑hypervisor environments.
• Dell’s Automation Platform with PowerStore, PowerScale, ObjectScale and the Dell AI Data Platform provides an AI‑ready storage backbone that plugs into the Dell AI Factory with NVIDIA.

CIOs are under pressure to move from AI experiments to durable platforms that deliver real value. That shift exposes a hard truth: storage for the AI era is no longer an afterthought. In a recent episode of theCUBE’s AI Factory series from the New York Stock Exchange, Arthur Lewis, President of Dell’s Infrastructure Solutions Group, made the case that AI factories succeed or fail based on the data and storage decisions leaders make now, not just on GPU choices later.

Why storage for the AI era is a board‑level decision

Most enterprise data is still created and stored close to home in existing data centers and at the edge, not in a single cloud. Much of that data is “dark” or stranded in backup and archive systems, even as business leaders ask for AI‑driven transformation.

Arthur described the modern data center as “one big computer”. Compute, acceleration, networking and storage all work together to turn raw data into high‑grade fuel for AI. For CIOs, this has three clear implications:

• More data must be observable, protected and in motion, not locked away.
• Sensitive data has to stay in the right place for sovereignty and governance while still feeding AI at scale.
• Storage design becomes a strategic decision, not background plumbing.

This is already visible at Oregon State University. Its Dell AI Factory foundation manages petabytes of marine research data over decades. Teams can apply AI to that data without constantly rebuilding the underlying storage for each new project.

Moving beyond HCI to flexible AI storage

Hyperconverged infrastructure (HCI) and VxRail brought valuable “cloud‑like” simplicity on‑prem. However, as Arthur noted, AI and cloud‑native workloads now demand more flexibility.

Today, many IT estates:

• Run VMs, containers and bare metal across several hypervisors.
• Mix GPU‑dense AI clusters with traditional applications.
• Need compute and storage to scale independently, rather than in fixed blocks.

HCI remains useful, yet models that bind compute and storage too tightly can create cost and utilization problems as AI scales. For many core and private‑cloud workloads, Dell PowerStore offers a more adaptable base. It consolidates databases, VMs and files on unified all‑flash storage and lets teams grow capacity without dragging extra compute along for the ride.

To keep operations simple while regaining flexibility, Dell introduced the Dell Automation Platform. It automates compute, networking and storage as a full stack and supports multi‑hypervisor, multi‑workload environments. In day‑to‑day terms, it lets teams manage servers and storage such as PowerStore, PowerScale and ObjectScale as shared pools that can serve many different AI and non‑AI workloads.

Building an AI‑ready storage foundation with Dell

Arthur outlined how Dell has streamlined its storage strategy for the AI era around three pillars:

1. Private cloud storage
   Traditional workloads will remain important. In fact, AI often creates more of them in the form of reports, transactions and analytics. Platforms like PowerStore provide unified block and file storage for these applications, so they can share a foundation with newer AI services.
2. An AI Data Platform for unstructured and AI data
   Dell has centered this layer on PowerScale and ObjectScale and added offerings such as Project Lightning and the Dell AI Data Platform. These storage engines feed GPUs at line rate and manage AI data across its lifecycle, placing, processing and protecting data across core, cloud and edge.
3. Cyber resilience for AI data
   AI models depend on trusted data. The storage layer has to protect training sets, models and outputs, and support rapid recovery when something goes wrong.

This storage foundation underpins the Dell AI Factory with NVIDIA. That full‑stack approach spans infrastructure, storage engines, data services and operations. Studios like Kennedy Miller Mitchell use this combination to speed AI‑assisted film production and Oregon State University relies on it to protect decades of scientific data while advancing AI‑driven research.

What CIOs should do now

Arthur’s comments translate into a short, practical agenda for CIOs who want storage ready for the AI era:

• Make storage the first AI design decision, not the last. Decide where crown‑jewel data will live, how it will be governed and protected and how it will be served to GPUs across core, cloud and edge before you finalize cluster designs.
• Move beyond HCI‑only thinking. Keep the operational simplicity HCI brought you, but adopt architectures that let you scale compute and storage independently and support multi‑hypervisor environments.
• Standardize on an AI‑ready storage backbone. Use PowerStore for core workloads and PowerScale/ObjectScale with the AI Data Platform for AI and unstructured data, so each new AI use case builds on the same foundation instead of creating another silo.
• Connect storage to a complete AI stack. Make sure your storage engines and automation layer can plug cleanly into platforms like the Dell AI Factory with NVIDIA, rather than assembling your own AI stack from unrelated point products.

To hear more from Arthur on AI factories and data gravity and on how Dell is evolving infrastructure for the AI era, watch his full theCUBE interview from the New York Stock Exchange: “Building the Future: Harnessing AI Factories and Data Gravity in Modern Enterprises”.