What the Last Three Years of AI Have Taught Me

When I look back at the last three years, especially professionally, one thing stands out more than anything else: you just haven’t got a clue what the next thing is going to be or where it’s going to come from.

That realization has shaped how I work, how I talk to customers and how I think about the future of our industry.

A few years ago, if you’d asked anyone whether AI would suddenly become mainstream, used daily by teachers, frontline employees and entire enterprises, nobody would’ve said yes. Yet here we are. My wife, who’s a schoolteacher, now uses AI every day to help her plan lessons. That level of adoption would’ve been unthinkable not long ago.

These past three years have taught me three important lessons around AI, and I’ll share them with you here.

1. 1. Accept What You Don’t Know

AI forced me to recognize that there will always be things that come out of nowhere and completely change things. The speed at which AI emerged wasn’t something most of us predicted. And that has professional implications.

In the past, technology conversations were often about long‑term planning—three‑ to five‑year roadmaps, structured growth expectations and detailed architectural projections. Today, that approach simply doesn’t work the way it used to.

Be ready to go with it. You don’t have to know what’s coming next, but you do have to be prepared to adapt quickly.

For a good look at how rapidly AI is transforming enterprise foundations, Dell’s recent blog From Big Bang to Light Speed: the AI Revolution Continues captures this accelerating pace.

2. Scale Means Something Different Now

The second major lesson is about scale and how much that conversation has changed.

Before COVID, scaling discussions felt relatively predictable. Customers would outline their growth expectations, and we’d build architectures to match. Now, as I often tell customers, very few people really know what the next three to five years looks like. You might see no growth at all or you might quadruple in size. You might need far more performance, or you might simply need flexibility.

The real goal today is to create an architecture that can take you wherever the future leads. Whether the next three years bring stability, disruption or expansion, your environment needs to be ready.

Dell’s ongoing AI Factory work lays out how enterprises can design for that kind of uncertainty, emphasizing infrastructure that can scale up, scale out or pivot entirely as workloads shift.

1. 3. Be Ready

The final lesson is almost a combination of the first two: we just don’t know what the future’s going to hold, but we need to be ready for it, whatever it is. That mindset shift has become fundamental.

AI fundamentally changed the way we think about performance, workloads, data, productivity, and even collaboration. It compressed innovation cycles. It introduced a constant, ambient pressure to adapt.

And while that could feel overwhelming, it’s actually an opportunity. Organizations that embrace adaptability architecturally, culturally and operationally see the benefits. Those that cling to rigid planning models struggle.

To explore how companies are scaling AI securely and operationally at enterprise levels, Dell’s AI hub is an excellent resource.

Looking ahead

If the last three years have taught me anything, it’s that unpredictability isn’t a threat—it’s a reality. And the organizations that recognize that, and design for it, are going to be the ones that thrive.

We may not know what’s coming next. But we can build systems, strategies and mindsets that are ready for whatever arrives.