What AI-Native Really Means (And Why Most Companies Get It Wrong)

This is a placeholder article. Replace with your actual content from LinkedIn.

The term “AI-native” gets thrown around a lot these days. Every company wants to claim they’re AI-native, AI-first, AI-powered. But most of them are really just AI-adjacent—they’ve bolted some machine learning onto existing processes and called it a transformation.

True AI-native engineering is fundamentally different. It starts with a different question.

The Wrong Question

Most companies ask: “How can we add AI to what we already do?”

This leads to predictable outcomes: AI chatbots on top of existing documentation, ML models predicting things that could be calculated deterministically, and “AI features” that are really just fancy autocomplete.

The Right Question

AI-native companies ask: “What would we build if AI capabilities were a given from day one?”

This leads to entirely different architectures, workflows, and team structures. It means designing systems where human and AI capabilities complement each other rather than compete.

What This Looks Like in Practice

(More content would go here…)

---

Want to discuss how your engineering organization can become truly AI-native? Get in touch.