What Digital Native & AI Native Companies Must Prepare – From an AI and Data Perspective

Introduction: The Shift from DX to AX

Over the past decade, Digital Transformation (DX) has reshaped the way organizations operate—digitizing workflows, migrating systems to the cloud, and improving customer connectivity. DX was about efficiency, modernization, and digital survival. But a new wave has arrived, and it’s far more transformative: AI Transformation (AX).

Beyond DX to AX: The Next Big Shift in Business Transformation

Unlike DX, which focused on digitizing existing processes, AX is about re-architecting business models around artificial intelligence (AI). It’s not merely a technology upgrade—it’s a strategic evolution redefining how value is created, delivered and sustained. 

In industries ranging from finance to healthcare, manufacturing to retail, companies that once digitized to survive must now intelligently automate, predict and personalize to remain competitive. The underlying enablers? Data and AI. 

Why AI Transformation Matters More Than Ever

AI Transformation isn’t just about adopting new tools—it’s about rethinking the business itself. Every function, from product design to marketing, is being reshaped by intelligent automation and predictive analytics.

Here’s how AI Transformation is redefining success across industries:

• Customer Experience → Hyper-personalized, real-time, AI-driven interactions that anticipate customer needs.
• Product Innovation → Smart, adaptive, and self-learning products that continuously evolve through data feedback.
• Operational Efficiency → Predictive automation that reduces waste, lowers costs, and enhances agility.

However, the path to AI Transformation (AX) varies widely depending on a company’s maturity. Let’s explore how three types of organizations—Traditional Enterprises, Digital Natives, and AI Natives—can each evolve to succeed in the AI era.

Evolution from DX to AX

1. Traditional Enterprises: Preparing for AI Transformation

Traditional organisations—such as many banks, large manufacturers, retailers—often have decades of operational history, large legacy processes and a wealth of data. But the challenge isn’t a lack of data; it’s data readiness.

To prepare for AX, these organizations must focus on three key priorities:

• Establish AI alignment – Leadership must first define how AI fits into major company goals. Is the focus on improving customer experience, automating workflows, or driving innovation? 

• Unify and clean data – Many legacy firms suffer from siloed systems, inconsistent formats, missing metadata, and unclear governance. These must be addressed to build reliable AI pipelines. 

• Build scalable AI infrastructure – Implementing MLOps and AIOps enables automation of model deployment, updating and monitoring, which is essential for continuous improvement. 

• Focus on value first – For traditional enterprises, the journey doesn’t demand completing DX before AX; what matters is aligning strategy and data discipline with AI capabilities.

If you work within or with a traditional organisation, ask: What business outcomes do we want from AI? How mature is our data ecosystem? This isn’t just “can we deploy AI?” but “are we ready to integrate AI into our business model?”

2. Digital Native Companies: Accelerating Toward AX

Digital-native companies—those born in the digital era, operating on cloud-native, data-driven platforms—already have advantages in personalization, agility and scaling. 
However, the key is: being digital is no longer enough.

Next-level focus areas

• Real-time data platforms – Transition from being merely data-rich to truly intelligent in real time. For example, using streaming analytics to inform decisions as events unfold. 

• Continuous AI experimentation – Embrace live A/B testing, model retraining, feedback loops that iterate rapidly rather than infrequent big-bang launches. 

• Responsible AI practices – As data privacy, transparency and bias concerns grow, digital natives must embed ethics and explainability into their AI operations. 

If you are part of a digital-native company—or advising one—ask: Is AI just the next project or is it the operating model? Are we moving from “digital first” to “AI first”? Speed is increasingly becoming the competitive moat.

3. AI-Native Companies: Scaling in the AX Era

For AI-native firms, AI is the product. These organisations—such as those building generative-AI platforms, self-driving vehicles or large-scale enterprise AI systems—derive their core value directly from the performance, trustworthiness and scalability of their AI. 

Core Growth Drivers

1. Commercializing AI models – Delivering new customer experiences through generative or predictive AI at scale. 

2. Embedding AI into products – Rather than just “adding AI”, embed AI deeply into the product/business offering. 

3. Expanding ecosystems – Building APIs, partnerships, platforms to allow AI capabilities to scale across organisations and use-cases. 

4. Securing large-scale, high-quality datasets – The foundation of accurate and reliable AI models.

5. Automating the entire model lifecycle – From training to deployment, monitoring, and updating models continuously.

6. Embedding ethics, safety and explainability – As AI becomes pervasive, these become non-optional for trust and compliance. 

For startups or business units aspiring to be AI-native: it’s not just about building cool models. It’s about trust, scalability and business impact. Ask: How will our AI capability generate sustainable revenue or strategic value?

The Future of Business: When AI Becomes the Core

The transition from Digital Transformation (DX) to AI Transformation (AX) marks a fundamental shift in how organizations operate and compete. DX helped businesses go digital. AX helps them become intelligent.

Each type of company faces unique challenges, but they share one undeniable truth: AI and data are no longer support functions—they are the essence of business itself.

In summary:

• Traditional enterprises must unify data and build scalable AI operations.
• Digital Natives need to evolve toward real-time, responsible AI ecosystems.
• AI Natives must focus on trust, scalability, and monetization of their AI capabilities.

The future of business will belong to organizations that master this transformation. The question is no longer whether to adopt AI—but how quickly and intelligently your organization can evolve to lead in an AI-driven economy.