This month, D'Intelligence Hub is focused on: AI-Native.

In an ever-changing digital world, businesses face a two-fold pressure: keeping pace with emerging technologies while also cutting costs. Being merely "digitalized" is no longer enough in this intense competition; while companies that bolt AI onto legacy systems are hitting the limits of efficiency, becoming an AI-Native company is taking center stage. This approach, which turns AI into the organization's core engine rather than just a tool, reduces operational costs while enabling much smarter, error-free decisions.

Click here to read the full D'Intelligence Hub: AI-Native issue!

Frequently Asked Questions

What is the difference between AI-native and digitalization?

Digitalization supports existing processes with technology; AI-native, on the other hand, redesigns the business model, data architecture, and decision-making culture around AI from the ground up.

What is a digital twin?

It is a real-time trackable, analyzable, and simulatable digital replica of a physical asset, process, or operation.

How much cost savings do AI-native companies achieve?

According to McKinsey data, AI-native leader companies that place AI at the center of their business processes reduce their operational costs by 20 to 30 percent and achieve profit margin growth twice as fast compared to traditional competitors. (Source: McKinsey & Company – The seven operating truths of AI-native companies)

What determines organizational success in AI-native transformation?

According to Microsoft's 2026 Work Trend Index data, the strategic impact gained from AI is no longer determined solely by employees' individual technology skills; corporate culture, strong executive support, innovative talent practices, and how business processes are structured around AI agents now play a more decisive role. (Source: The Official Microsoft Blog – How Frontier Firms are rebuilding the operating model for the age of AI)

Where should AI-native transformation begin?

It begins with a strong data infrastructure, the right skill sets, a clear roadmap, and organizational ownership; by redesigning the software architecture and decision-making mechanisms.