What Does "AI Native" Actually Mean?
Most software was built 5, 10, 15 years ago. Adding AI to it now is like retrofitting a Tesla engine into a 1995 Corolla.
The Problem
Retrofitting AI doesn't work.
Here's the non-tech explanation: most software you use today was built 5, 10, 15 years ago. The code was written in a different era, designed for how technology worked then. Adding AI to it now is like trying to retrofit a Tesla engine into a 1995 Corolla. You can do it, but everything fights against you. AI native means the software was built from day one with AI as part of its foundation.
The Disconnect
You can't wait for the perfect system.
Why does this matter? Because you're in the business of getting shit done. Mining, construction, equipment hire — you need data that helps you do the work, not IT projects that take two years to deliver. By the time a traditional IT project is finished, the data you spent all that time structuring is already stale. You can't wait for the perfect system. You need something working now.
The Solution
Start with structured data at source.
If you're even thinking about a big data project — stop. Start capturing operational data in a structured way first. It's always harder to fix things after they're already built. AI native systems let you start now. Capture operational data properly. Structure it as it's created. Then feed it to whatever other systems need it — today's systems or tomorrow's.
The Insight
Systems that work today and adapt for tomorrow.
The companies that win in operational data will be the ones that help businesses get shit done — not the ones that promise a perfect data warehouse in 18 months. You need data flowing now. You need it structured now. That's what AI native architecture enables: systems that work today and adapt for tomorrow, without needing to rebuild from scratch every time technology changes.