In the current business landscape, declaring an artificial intelligence strategy that doesn’t clearly improve the product or lower its cost is a surefire way to be seen as merely AI-branded rather than truly AI-native. The transition to becoming an AI-native enterprise requires far more than adopting new technologies; it demands a fundamental re-architecture of the entire business model, a shift that places AI at the very core of value creation. When a company announces its AI-native status, customers rightly expect products and services that are tangibly enhanced or enabled by artificial intelligence. While this value proposition is clear in sectors like consumer electronics, where “AI TVs” and “AI smartphones” offer obvious feature upgrades, many other industries face a significant challenge. They must grapple with whether customers will genuinely perceive added value from AI integration. For instance, Lotte Mart, a major Korean retailer, successfully markets peaches selected by an advanced AI-based sorting system, a clear quality enhancement. However, for a cosmetics company, the question becomes more complex: is it offering genuinely better cosmetics, or is the concept of “AI-personalized cosmetics” a tangible reality? Regardless of the industry, the underlying mission for all businesses remains to leverage advanced AI tools to boost internal productivity and deliver superior value.
1. Enhancing Products with Integrated AI
The most significant business impact from an AI-native transition occurs when artificial intelligence becomes a core, inseparable feature of the product or service itself, allowing customers to immediately experience its benefits. Companies pursuing this path leverage AI to deliver unambiguous messages centered on personalization, performance optimization, and intelligent automation, thereby creating a distinct competitive advantage. A prime example of this direct, product-centric AI-native shift is seen at Samsung Electronics, which has led projects integrating personalized automatic speech recognition leveraging On-Device AI technology and advanced chipsets into its smart devices and home appliances. By integrating AI directly into mobile devices, functionalities like real-time translation, image super-resolution processing, and customized settings based on learned user behavior can be executed without constant reliance on the cloud. This strategic move redefines the product as an inherently smarter device, delivering the clear customer value of top-tier performance and sophisticated automation that adapts to individual needs. This approach not only enhances the user experience but also solidifies the brand’s position as an innovator at the forefront of technology.
In the software industry, Microsoft has spearheaded a monumental AI-native transition that is fundamentally reshaping the landscape of knowledge work. By embedding Copilot, a sophisticated AI assistant, across its entire Microsoft 365 suite, the company has innovated the very mechanisms through which professional tasks are accomplished. This integration goes far beyond simple summarization tools; Copilot can automatically structure and draft professional reports in Word based on user-provided data and conceptual inputs. Within Excel, users can now request complex data analysis and trend visualization using natural language, effectively eliminating the need to master intricate formulas and functions. Microsoft’s AI-native strategy provides the specific, demonstrable value of dramatically enhanced productivity for knowledge workers, a benefit that resonates directly with its enterprise and individual customers. This sharp focus on delivering core business value has been a key driver of the company’s rising subscription rates and overall corporate valuation, as detailed in extensive industry analyses like those from the Second Microsoft Report on AI and Productivity Research.
2. Driving AI-Powered Operations for Efficiency
For companies where embedding AI features directly into the final product is difficult or where customers do not heavily factor AI adoption into their purchasing decisions, maximizing internal operational efficiency becomes the primary avenue for achieving business impact. These organizations must strategically deploy advanced AI tools to refine their cost structures and elevate service quality, subsequently passing these advantages on to the customer in the form of competitive pricing or enhanced speed and accuracy. This indirect value creation strategy is effectively highlighted by projects at major retailers like Target and Emart. At Target, the enhancement of an AI-powered demand forecasting and inventory management system proved crucial. The advanced AI model analyzed a multitude of variables—from historical sales data to weather patterns and local events—to minimize financial losses from both stockouts and overstocking. This operational efficiency indirectly translated into profound customer trust; shoppers learned that the products they wanted were consistently available at the right price, reinforcing loyalty and brand perception without ever explicitly mentioning the underlying AI technology.
Continuing with the strategy of indirect value creation, the omnichannel integration between Emart and SSG.COM demonstrates how AI-optimized logistics and dispatching can ensure the fastest and most accurate delivery services. Customers in this scenario did not focus on the presence of AI in the supply chain; instead, they responded positively to the tangible value it delivered: the unparalleled convenience of receiving fresh goods precisely when expected. Beyond the retail sector, major financial institutions such as JPMorgan Chase have concentrated their AI-native efforts on bolstering internal efficiency and risk management. These institutions leverage sophisticated AI algorithms to enhance their fraud detection systems, enabling real-time identification of subtle, anomalous patterns to proactively protect customer assets. Furthermore, AI models meticulously analyze vast quantities of financial data to predict regulatory changes and market risks, which in turn leads to significant operational cost savings and a more resilient business model. Their message to customers, therefore, centers on accuracy and trust, asserting that AI is what keeps their money safest and ensures it is invested most efficiently.
3. The Importance of a Clear Strategic Choice
The ultimate success of an AI-native transition is fundamentally contingent on the strategic choices a company makes at the outset. It is imperative for businesses to conduct a thorough analysis of their core competencies and critical customer touchpoints to clearly decide whether AI should be the primary engine of their product innovation or the foundational bedrock of their operational excellence. A return to the cosmetics industry, where the perception of customer value from AI was initially uncertain, provides a compelling case study. The AI-native breakthrough in this sector was found in hyper-personalization. Beauty companies like L’Oréal now utilize AI-powered skin diagnostic technology to analyze a customer’s unique skin condition, lifestyle habits, and even micro-environmental factors. This rich dataset enables an AI algorithm to formulate a customized serum, perfectly optimized for that individual from a pool of potentially hundreds of thousands of ingredient combinations. In this instance, AI is not merely making a better version of an existing product; it is providing a unique, previously unattainable value proposition. The AI-native declaration, therefore, serves to redefine customer value by offering an experience that is made possible only through the power of artificial intelligence.
Ultimately, a successful AI-native transformation requires clear, compelling answers to two fundamental questions from the customer’s perspective. First, how does AI fundamentally innovate the product or service itself, creating new possibilities and experiences? This is exemplified by Samsung Electronics’ advanced AI chips that enable on-device intelligence and Microsoft’s Copilot, which redefines productivity. The second question is, how are the benefits of AI-driven internal efficiency passed on to the customer, whether directly or indirectly? This is demonstrated by Target’s ability to maintain competitive pricing through optimized inventory and JPMorgan Chase’s commitment to enhanced asset security through superior fraud detection. Choosing to be AI-native is no longer an optional endeavor; it has become a strategic imperative for survival and growth in a competitive market. Companies must meticulously select the strategy that most effectively integrates AI into their unique business model and then rigorously work to translate that integration into a measurable and sustainable business impact that resonates with their target audience.
4. Action Plan for a CTOs AI Native Transition
Leading an AI-native transformation presents a new level of pressure on technology leadership, demanding a vision that spans technology roadmaps, organizational culture, and data strategy. To successfully guide an enterprise into becoming truly AI-native, a Chief Technology Officer (CTO) or Chief Information Officer (CIO) must champion several concrete action items. The first and most critical step is to mandate clarity on the business impact path. Before initiating any AI project, the CIO must strategically distinguish whether its primary goal is to enhance product competitiveness or to drive operational efficiency. This distinction is crucial because not every AI investment can or should share the same objectives; clear key performance indicators must be established for each path to measure success accurately. The second priority is to establish a unified data fabric. The performance of any AI model is intrinsically dependent on the quality and accessibility of the data it is trained on. Therefore, the CIO’s immediate focus must be on unifying and standardizing siloed data from across the organization, building a single, accessible data fabric that AI can utilize company-wide. Without this foundational layer, the development and training of advanced, effective AI models remain an impossibility.
Continuing the action plan, IT leadership must ensure organization-wide AI tooling and education are available. As powerfully demonstrated by the rollout of Microsoft’s Copilot, the benefits of an AI-native approach are not limited to data scientists and engineers. The IT department has a responsibility to widely deploy generative AI and collaboration tools and to provide structured, ongoing training so that all employees can easily leverage these tools to boost their individual and collective productivity. This democratization of AI accelerates internal innovation and cultivates a culture of efficiency. Another non-negotiable priority is responsible AI governance. AI models that produce biased or opaque decisions can inflict severe damage on corporate trust and brand reputation. The CIO must proactively establish and enforce a company-wide AI governance framework that covers fairness, transparency, security, and data privacy standards. This is especially critical in sensitive sectors like finance and healthcare. Finally, a strategic plan must be developed to decouple from legacy systems. AI-native applications often clash with inflexible, outdated IT infrastructure. The CIO must therefore create a detailed roadmap for gradually separating core AI-based services from these legacy systems, transitioning toward a cloud-based modern architecture that allows for the agile adoption, testing, and scaling of new AI technologies.
From Vision to Value
The journey toward becoming an AI-native organization represented a comprehensive transformation of technology, processes, and the entire business model. The success of this transition hinged on the strategic clarity with which companies approached their transformation, understanding that AI-native was not a monolithic concept but a tailored approach driven by two main pillars: direct innovation at the customer touchpoint and the profound optimization of internal operations. Companies that chose to make AI the core engine of their product delivered direct, innovative value that customers could see and feel. This path maximized differentiation from conventional products and provided customers with a new dimension of experience, forming the basis for high-value creation. In contrast, the strategy of deploying AI for internal operations, such as demand forecasting and logistics, aimed at maximizing efficiency. These efforts resulted in indirect benefits for the customer, such as more competitive pricing or superior service quality, which solidified long-term trust and loyalty. The successful execution of these strategies has since redefined market leadership and reshaped customer expectations across industries.
