I’ve been building technology platforms long enough to recognize when an industry is repeating itself. 

I joined Mad Mobile early. Back when Greg, Bruce, and I were spending more time building than talking about building. We were solving real problems for real businesses, retail and hospitality in particular, and we were doing it in an environment that didn’t allow for much abstraction. If something broke, you felt it immediately. If something worked, it was because it actually worked in the field. 

That kind of experience leaves a mark. It teaches you to separate progress from novelty. 

Over the last few years, as AI has moved from research labs into everyday products, I’ve felt that familiar pattern returning. The excitement. The rush. The temptation to rebuild everything as quickly as possible. And the quiet concern that we might be about to do the same thing again. Digital transformation delivered real value. It moved information out of filing cabinets and into systems. It made data accessible. It allowed businesses to operate at a scale that simply wasn’t possible before. But it also left us with something we rarely talk about honestly. Exhaustion. 

To make systems scalable, we broke them apart. To make them resilient, we isolated them. To make them secure, we wrapped them in layers. Over time, applications became the unit of value. Data became fragmented. Workflows became rigid. Entire teams were formed just to keep systems stitched together. If you work in technology today, you feel this. Not as a theory, but as a daily reality. Too many tools. Too many dashboards. Too many integrations just to answer simple questions. Most of us didn’t get into technology to manage complexity. We got into it to solve problems. 

Somewhere along the way, the work shifted. 

Every major technology shift promises efficiency. AI is the first one that changes the direction of effort. For decades, we trained people to think the way systems needed them to think. We digitized processes so computers could understand them. Humans adapted. Software stayed rigid. 

AI reverses that relationship. 

For the first time, systems can understand context. They can interpret intent. They can adapt to how people naturally work instead of forcing people into predefined paths. I sometimes explain this with a simple image. Picture a server in a restaurant writing an order on a pad of paper. For years, digital transformation tried to eliminate that moment. Replace it with screens, taps, structured inputs. Not because it was better for the human, but because it was better for the system. Now imagine that same analog moment, but the system understands it. The intelligence no longer depends on perfect inputs. It doesn’t require every interaction to be forced into a form. The system adapts to the human, not the other way around. 

That is the real shift. 

AI is not exciting because it is faster. It is exciting because it allows us to simplify. Right now, much of the industry is focused on adding AI to existing structures. Wrapping intelligence around apps. Automating workflows that were already overly complex. Rebuilding the same architectures with new tools. That approach will deliver incremental gains. It will also preserve the very things that made technology feel heavy in the first place. We should pause before we lock those decisions in. 

Do we really need dozens of isolated systems representing the same people, the same data, the same work? Do we really need to keep rebuilding complexity just to manage complexity? AI gives us a chance to rethink not just how fast we work, but how much work is actually necessary. 

This moment matters because we are early. The choices being made right now will shape how the next decade of technology feels to use, not just how powerful it looks on paper. We can build systems that respect human time. We can simplify instead of fragment. We can let intelligence absorb complexity instead of pushing it onto people. Or we can move quickly and recreate the same fatigue with better tools. 

Having built platforms through multiple waves, I’ve learned that the most important decisions are often the quiet ones. The ones about structure, ownership, and restraint. 

AI gives us the chance to make better ones. We should take it.