AI Era: Moving Beyond 'Using' AI

The era of AI utilization has ended; we must prepare for the 'AI Native' era, where AI is taken for granted and everything is designed based upon it. This signifies a new transition where AI becomes a fundamental premise for our lives and work.

On July 3rd, the 'MetaCon 2026' event was held at COEX in Seoul. Companies and experts from various fields, including Hyundai Motor, K-Bank, and Anthropic, attended and agreed that AI has become a premise for corporate operations.

Meng Sung-hyun, KAIST Professor Emeritus, emphasized that AI Transformation (AX) should move beyond merely 'AI+X,' which adds AI functions to existing tasks. Instead, it should progress to 'X+AI,' which reorganizes the business structure itself around AI. He explained that AI should not be viewed as a tool but as a colleague, requiring a redesign of task allocation and responsibility structures. AX, or AI Transformation, does not simply involve attaching functions externally but means AI deeply participates in the judgment and execution processes themselves. Professor Meng distinguished between 'AI+X,' which adds AI functions to existing businesses, and 'X+AI,' which reorganizes the business structure around AI. He stressed that the real change lies in the latter. Adding chatbots or summarizing documents falls under the former approach, while fundamentally changing manufacturing processes, insurance underwriting, customer service, and the flow of financial services belongs to the latter.

Consequently, companies need to prepare for the 'AI Native' era, which requires fundamentally redesigning organizational culture and work methods beyond the dimension of purchasing AI tools. This signifies AI-driven business innovation and a comprehensive transformation in corporate operating methods.

Jang Dong-jin, Architect at Anthropic, stated that software is evolving from a tool to a colleague. As a colleague, AI prompts a redefinition of task allocation, meeting formats, and responsibility structures. AI takes on execution, while humans focus on setting direction and evaluating outcomes; human roles are not disappearing but changing. Jude Umeh, Director at Salesforce, highlighted the importance of the question, 'What more can humans do thanks to AI?' While decisions and responsibilities remain human domains, individuals are no longer solely executors of every task but are moving into roles that design the structure of work alongside AI.

Many companies view AI as merely a matter of purchasing a tool. Jessica Kim, Partner at Deloitte, revealed that only 5% of companies have achieved tangible financial performance and ROI from AI adoption. The cause identified was not a lack of technology but treating AI as just another IT project, akin to RPA. The failure stemmed from purchasing tools without defining what problems to solve, without refining data, and without involving frontline workers. Jeong Su-ji, Advisor at SAP, diagnosed that failure begins the moment AI becomes the objective. AI adoption must be a means to solve specific challenges on the ground. Companies that use AI to solve problems succeed, while those that search for problems to justify AI adoption fail.

The AI discourse is shifting from performance competition to cost issues. The questions from businesses are no longer 'Will we adopt AI?' but 'At what cost and how stably can we execute it?' Lee Jin-hyung, Head of Division at KT, explained that AI token budgets have become performance variables beyond simple cost factors. The decline in token prices leads to Jevons' paradox, where falling prices spur explosive growth in usage. Jin Yo-han, Center Director at LG CNS, expressed concern that this trend could lead to levels unsustainable for businesses.

The competition is now in operational aspects. Decisions on which tasks require large models versus small models, and which stages will be automated versus human-verified, become competitive advantages. AI is not a technology that ends with purchase; operation is a more challenging task than implementation, according to Jin's analysis.

K-Bank's proposed hyper-personalized finance also falls within this trend. Kim Hong-jong, Team Leader, explained that banks are moving beyond branches, ATMs, and mobile banking toward hyper-personalized finance and agentic banking. When AI is a premise, hyper-personalization, which reads and responds to customer situations, behaviors, and preferences in real-time, becomes a system that transcends mere marketing slogans. This transformation can occur not only in banking but also in various industries such as media, retail, education, and healthcare.

Ultimately, all presentations on this day converged on one point: 'culture.' Deloitte, SAP, Salesforce, K-Bank, and others defined AI not as an IT project but as business transformation. No matter how good the technology, no change occurs if it's not used by frontline workers. If frontline workers don't define the problems, AI will provide irrelevant answers. Kim Hong-jong stressed that a culture where frontline teams and AI organizations jointly define problems, experiment, fail, and improve is essential for sustained AI transformation. Without changes in organizational culture, true innovation cannot be achieved through AI tool adoption alone.