Firms Must Rebuild Around AI, Not Just Deploy Tools, KAIST Professor Says

Companies preparing for an ‘AI-native’ era must go beyond adding AI features to existing workflows and instead redesign their businesses and organizations around AI as a foundational assumption, according to a leading Korean academic. The argument reframes the current wave of corporate AI adoption as a structural transformation—one that will increasingly separate firms that merely deploy tools from those that rebuild operating models for AI-driven decision-making.

Speaking Thursday UTC at the MetaCon 2026 conference in Seoul, Maeng Seong-hyeon, emeritus professor at KAIST, delivered a keynote titled “Beyond AX to X+AI Convergence: Leadership for the AI-Native Era.” Maeng said competitive advantage in the AI era will hinge less on how many AI systems a company has introduced and more on how thoroughly it has re-architected its business, culture, and governance for a world where AI is embedded everywhere.

Maeng framed the industry’s trajectory using two connected concepts: artificial general intelligence, or ‘AGI’, and the ‘AI-native’ era. He characterized AGI as a stage where AI can perform nearly all tasks humans can do—at or above human level—and argued that the market is already moving past today’s conversational and reasoning models toward ‘agentic AI’ that can plan and act in multi-step workflows. He added that roadmaps increasingly point to AI systems capable of conducting novel research and even supporting organizational management functions.

In parallel, Maeng stressed that the AI-native era will be defined not simply by more powerful models, but by ubiquitous deployment. As AI spreads from cloud services into devices, robots, and ambient computing environments, continuous interaction between humans and AI will become the default. Over time, he argued, users will stop consciously “using AI” in the way they think about applications today—making the strategic question not whether to use AI, but ‘how deeply’ and ‘in what way’ it should be embedded into the enterprise.

That distinction matters for corporate transformation programs increasingly branded as ‘AX’—AI transformation. Maeng said AX must extend beyond technology rollout to structural changes in how organizations operate, make decisions, and monetize products. If digital transformation (‘DX’) focused on digitizing data and processes, he argued, AX introduces AI as a participant in judgment and execution, effectively shifting parts of the organization’s “agency” to machines.

To clarify what meaningful integration looks like, Maeng separated AI convergence into two paths: ‘AI+X’ and ‘X+AI’. The first refers to adding AI functions to existing businesses—such as chatbots, search assistants, or document summarization—to improve efficiency. The second, he said, is the deeper and more disruptive model: rebuilding domain workflows with AI as the organizing principle. Examples include reengineering manufacturing processes, insurance underwriting, and other operational pipelines so that the AI system sits at the center of the flow rather than at the edge as a bolt-on tool. “X+AI is not placing AI on top of an existing business,” he said, “it is changing the business structure itself.”

Maeng also laid out a three-stage approach to AX: first, automation and productivity gains; second, building organizational capabilities such as data assets, knowledge bases, and ‘AI literacy’; and third, creating new products and business models premised on AI. However, he cautioned that treating these as a linear ladder often prevents companies from ever reaching the strategic stage. Instead, he recommended a top-down approach that starts with business-model and strategy design, then works backward into execution and tooling.

According to Maeng, firms that succeed in AI transformation tend to build multiple layers at once: ‘AI governance’ frameworks, systematic data asset development, retrieval-augmented generation (RAG) knowledge systems, official enterprise AI platforms, and company-wide AI literacy programs. The cultural dimension is critical, he argued, because AI becomes durable inside an organization only when employees broadly understand AI’s capabilities and limitations and incorporate it into day-to-day decision-making.

He closed with a leadership message: the defining question is not what can be automated, but how the business should be redesigned for AI. Tools should follow strategy, he said, and AI literacy must be developed as an enterprise-wide culture. “AI is not merely a tool,” he added, describing it as an ‘augmenting’ agent for individuals and organizations—and arguing that true AX is ultimately completed through ‘X+AI’ convergence, where technology and business design are rebuilt together.

MetaCon 2026 runs from Thursday to Friday UTC at COEX in Seoul under the theme “AI Makers Rise,” bringing together companies, builders, and investors to share implementation strategies and lessons learned across enterprise innovation, marketing, and investment as industries adapt to AI-driven changes in work and value creation.