3 posts tagged with "AI Native"

In enterprise digitalization and intelligent transformation, the biggest anxiety isn't the lack of tools—it's the fear of choosing the wrong path. Systems built with massive resource investments become obsolete and need complete overhauls within two years because they can't scale. Applications that required extensive employee training become bottlenecks as the business grows. AI technologies that look impressive in demos prove disconnected from business systems and unusable for employees. What enterprises truly need are systems that deliver fast, customize rapidly, and evolve with business needs—investments that generate long-term value. However, lightweight apps built on database-UI paradigms and AI applications deployed in isolation from business contexts are products of short-termism, failing to gain traction in large enterprises.

AI-native application architecture must address not only how AI modules are designed and integrated, but also how traditional technical modules are perceived, driven, and orchestrated by AI. Attempting to integrate AI capabilities into legacy event-driven architectures is like putting an internal combustion engine on a horse cart—foolish and inefficient. Traditional enterprise applications like ERP, CRM, and OA systems will inevitably be reshaped by new AI-native architectures and development paradigms.

In today’s world, where everyone is embracing AI in software development, how much of your code do you write yourself? How much is generated by AI? In this evolving landscape, every software engineer will soon have to become an AI native developer.

But do we need to manually debug and adapt every AI-generated line of code—whether in embedded AI scenarios or vibe coding? Is there a development approach that can truly streamline this process? Perhaps an AI native development platform gives the answer.