The transition to an AI-native enterprise demands more than just technology adoption; it requires a fundamental re-architecture of the business model. When a company declares itself AI-native, customers are inevitably confronted with products and services built with or enabled by AI.
While AI is clearly a marketing term for high-value creation in industries like AI TVs and AI smartphones, many companies that don’t produce consumer electronics hesitate. They question whether customers will truly perceive added value from the AI integration. For instance, Lotte Mart, a leading Korean retailer, has begun selling peaches selected by an advanced AI-based sorting system to enhance product quality through deep learning algorithms.
While AI-selected fruit is a clear value proposition, the question remains for cosmetics: Is it simply better cosmetics or is AI-personalized cosmetics genuinely possible? Nevertheless, the mission remains for all businesses to boost internal productivity using advanced AI tools.
The business impact of an AI-native declaration varies dramatically depending on the company’s context. Some categories see a massive impact because AI features directly integrate into the product, while others struggle with unclear customer messaging because AI features don’t clearly manifest in the final product. Based on my experience leading AI projects at Samsung Electronics, Target and Emart, I discovered two distinct paths to AI-native transformation. Here are the necessary strategies, with examples from US companies that made major AI-native declarations around 2025.
How we’re making our products better with built-in AI
The most potent business impact occurs when AI becomes a core feature of the product or service, allowing customers to immediately experience its value. These companies leverage AI to deliver clear messages around personalization, performance optimization and intelligent automation.
My time at Samsung Electronics exemplifies this direct product-centric AI-native shift. I led projects integrating personalized automatic speech-recognition technology leveraging On-Device AI technology and chipsets into smart devices and home appliances. The AI integrated into mobile devices enables real-time translation, image super-resolution processing and customized settings based on learned user behavior without relying solely on the cloud. This redefined the product as an ultimately smarter device through AI, delivering the clear customer value of top-tier performance and intelligent automation.
In the software sector, Microsoft spearheaded a monumental AI-native transition around 2025, fundamentally altering knowledge work. By embedding Copilot, an AI assistant, across the entire Microsoft 365 suite, they innovated the very mechanism of work. Copilot moves beyond simple summaries; it automatically structures and drafts professional reports in Word based on user-provided data. In Excel, Copilot allows users to request data analysis and trend visualization in natural language, eliminating the need for complex formulas.
Microsoft’s AI-native strategy provides the specific, clear value of dramatically enhanced knowledge worker productivity. This focus on core business value has been key to their rising subscription rates and company valuation, as detailed in reports like those from the Second Microsoft Report on AI and Productivity Research.
How we drive AI-powered operations to ensure internal efficiency and long-term product value proposition
For companies where AI features are difficult to embed directly into the product or where customers don’t heavily factor AI adoption into their purchasing decisions, maximizing internal operational efficiency becomes the primary path to business impact. These companies must use advanced AI tools to improve their cost structure and service quality, then pass these benefits to the customer as competitive pricing or enhanced speed and accuracy.
My experience leading projects at Target (the #2 retailer in USA) and Emart (the #1 retailer in South Korea) highlights this strategy of indirect value creation. At Target, I led the enhancement of the AI-powered demand forecasting and inventory management system. The AI model analyzed numerous variables to minimize losses from both stockouts and overstocking. This efficiency indirectly translated into customer trust: the products they want are always available at the right price.
In the omnichannel strategy connecting Emart and SSG.COM, AI-optimized logistics and dispatching to ensure the fastest and most accurate delivery. Customers didn’t focus on the AI’s presence; they reacted positively to the resulting value: the convenience of receiving fresh goods on time.
Beyond retail, major financial institutions like JPMorgan Chase focus their AI-native efforts on internal efficiency and risk management. They leverage AI to enhance their fraud detection systems, enabling real-time detection of subtle pattern changes to protect customer assets. Furthermore, AI models analyze vast amounts of financial data to predict regulatory changes and market risks, leading to operational cost savings.
Their customer message focuses on accuracy and trust, asserting that AI keeps customer money safest and invests it most efficiently. Maximizing internal efficiency through AI-native transformation secures long-term competitive advantage and provides the foundation for delivering sustainable value to customers.
Clear strategy can make AI-native vision a success
The success of an AI-native transition hinges on the company’s strategic choices. Businesses must analyze their core competencies and customer touchpoints to clearly decide whether AI should be the engine of their product or the foundation of their operations.
Returning to the cosmetics industry, where customer value perception is uncertain, the AI-native shift found a breakthrough in personalization. Beauty companies like L’Oréal use AI skin diagnostic technology to analyze a customer’s skin condition, lifestyle and even micro-environmental factors. This data enables AI to formulate a customized serum perfectly optimized for that individual from potentially hundreds of thousands of combinations.
Here, AI isn’t making a better product; it’s providing a unique value previously unattainable. The AI-native declaration, therefore, redefines customer value by offering an experience that is made possible only by AI.
Ultimately, a successful AI-native transition requires clear answers to two core questions for the customer:
- How does AI fundamentally innovate the product/service itself? (e.g., Samsung Electronics’ AI chips, Microsoft’s Copilot)
- How are the benefits of AI-driven internal efficiency passed on to the customer, directly or indirectly? (e.g., Target’s competitive pricing, JPMorgan Chase’s asset security)
AI-native is no longer optional; it is the strategic imperative for survival. Companies must select the strategy that most effectively integrates AI into their business model and translates that value into measurable business impact.
Transforming business with AI: Innovating products and maximizing efficiency
AI-native transformation signifies a fundamental redefinition of corporate value, driven by two main pillars: innovation at the customer touchpoint and optimization of internal operations.
Direct product value through AI-native integration
The strategy of using AI as the core product engine delivers direct innovative value to the customer. As seen with Samsung Electronics’ On-Device AI, AI optimizes product performance and functionality in a user-customized way, maximizing differentiation from conventional products.
This provides the customer with a new dimension of experience and forms the basis for high-value creation. When AI fundamentally innovates the product, the AI-native declaration becomes a powerful market signal.
Indirect value through internal AI-native integration
In contrast, the strategy of deploying AI for internal operations, such as demand forecasting, inventory management and logistics optimization (as exemplified by Target and Emart), aims at maximizing efficiency. AI reduces operating costs and improves the accuracy and speed of services. The result is returned to the customer as an indirect benefit, such as more reasonable pricing or faster delivery. In these cases, the customer message should focus not on the presence of AI, but on the superior service quality made possible by AI.
CTO/CIO’s action items for AI-native transformation
As CTO, leading the AI-native transformation comes to me as another level of pressure on leadership that spans technology roadmaps, organizational culture and data strategy. Here are concrete action items for successfully turning the enterprise into an AI-native company.
- Mandate the clarity of the business impact path: Before initiating any AI project, the CIO must strategically distinguish whether the AI will enhance product competitiveness or operational efficiency, setting clear key performance indicators for each. Not every AI investment can share the same goals.
- Establish a unified data fabric: AI model performance relies on data quality and accessibility. The CIO’s immediate priority must be to unify and standardize siloed data, building a single, accessible data fabric that AI can utilize company-wide. Without this foundation, training advanced AI models is impossible.
- Ensure organization-wide AI tooling and education: As demonstrated by Microsoft’s Copilot, AI-native is not limited to data scientists. The IT department must widely deploy generative AI and collaboration tools and provide structured training so all employees can easily leverage AI tools to boost productivity. This accelerates internal innovation.
- Prioritize responsible AI governance: AI models that make biased or opaque decisions can severely damage corporate trust. The CIO must proactively establish and enforce a company-wide AI governance framework covering fairness, transparency, security and data privacy standards. This is critical, especially in sensitive sectors like finance.
- Strategically decouple from legacy systems: AI-native applications often clash with the inflexible legacy IT infrastructure. The CIO must plan a roadmap for gradually separating core AI-based services from legacy systems, transitioning to a cloud-based modern architecture that allows for the agile adoption and testing of new AI technologies.
The journey to becoming AI-native is a comprehensive transformation of technology, organization, processes and the entire business model.
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