People ask me a lot what’s top-of-mind in storage today, and there are a few themes I keep coming back to because they’re showing up in customer conversations, competitive moves and even how we run our own teams.
All the press is about how storage companies are becoming AI companies, and you see this across the board. And yes, that’s a challenge, but the bigger issue is clarity: what is our role in AI storage?
Here’s the nuance that gets lost in the headlines. A lot of the giant neo clouds and hyperscalers lean heavily on object storage for many AI pipelines. That’s not where traditional enterprise data always lives. In the enterprise, as customers start doing inference and even small language models, a lot of their data is still on block—primary storage they already trust and operate every day.
The message to customers isn’t “ignore object and file.” It’s that you don’t automatically need to go buy the shiny new thing just because someone talks a good AI game. You’ve already got storage in place. We can prove performance across real use cases and then complement it with the right mix of block, object and file from the broader Dell portfolio. Modernize where you are, instead of assuming AI means a forklift upgrade.
AI has to help us go faster without getting reckless
The other thing I’m thinking about is how I use AI within my teams to help us go faster, improve velocity and quality, while still doing it responsibly. Honestly, the dumber the idea, the more interesting it is if it works (and sometimes it doesn’t).
For example, we asked ourselves, “What if we could create one management interface that works across PowerMax, PowerFlex and PowerScale?” The first response was a long list of reasons it couldn’t work: different terminology, different mental models, different everything. Fine. Let’s go see. About a week later, the team had a working version. It was cool because you could say, “I’m a PowerMax person,” use that vocabulary, and still manage the other platforms without having to re-learn the whole world.
That kind of unification creates benefits: less effort to keep things secure, fewer one-off tools and cool new workflows—like seeing everything in one place and managing it the same way, which the industry has historically struggled to do. For me, that’s what “bringing AI into everything” should mean: practical wins, not a science project.
Stay in a constant state of modernization so you never need a “big bang”
This is a pattern I’ve learned to trust. If you’re in a constant state of transformation—iterating, improving, tightening feedback loops—then you don’t end up needing a big bang transformation. Nobody has to come in and do the harsh A-to-Z reset. The organizations that get in trouble are the ones that postpone change until it becomes unavoidable.
Yes, AI is at the center of it. But not in a buy-brand-new-everything way. We see it as helping customers modernize where they are, keep their options open and keep moving. Internally, it’s about using AI to remove friction and speed outcomes without creating new risk. How do I stay out of the big bang trap? How do I keep our organization out of that? That’s what keeps me up.
Dell reported this
Source: www.dell.com
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