2 posts tagged with "AI-Native Architecture"

While AI technologies, particularly large language models, have seen explosive growth in recent years, their enterprise-scale adoption has fallen far short of expectations. Countless AI projects remain stuck in the demo phase, failing to deliver meaningful business value. The gap between promise and reality remains vast.

Unlike traditional enterprise web applications, production-grade AI applications carry inherent complexity. They're fundamentally about executing tasks, not merely recording transactions. This requires deep integration with each organization's unique business processes, data ecosystems, and knowledge systems. Custom development becomes inevitable, yet the reality is prohibitively expensive and often disappointing. Traditional development paradigms are no longer sufficient—the market urgently needs AI-native engineering practices and development methodologies.

The industry is exploring how to deploy enterprise AI applications in production, but many attempts have taken the wrong path. Inability to make partial adjustments to outputs, deployment isolated from business systems, standalone UIs that can't collaborate with humans, and claims of being universal products—none of these represent what production-grade AI applications should be.