AI-Native Companies Could Reshape Corporate Structures, Reduce Management Layers

As companies rush to deploy generative AI, a growing number of operators argue the real competitive edge will not come from using AI as a productivity booster, but from rebuilding the firm around it. At a major Seoul conference this week, one founder said the emerging model of the 'AI native company' will reshape everything from decision-making to org charts—potentially making layers of middle management obsolete.

Kim Seung-kwon, CEO of Joshua Company, laid out that thesis on Friday ET (Saturday KST) during Metacon 2026, an AI conference hosted by TV Chosun and co-organized by TokenPost. In a session titled “AI Native Company: How to Work With Your Own AI Employees,” Kim described how organizations are moving from using AI for document summaries or content drafting to treating AI as the 'operating system' of the business.

Kim said most enterprises remain stuck in the early stages of adoption—using models as a “thinking partner” for research and ideation, or at best as a “secretary” that understands limited user context and helps execute tasks. The next stages, he argued, are far more disruptive: AI becoming a full “teammate” without which work cannot proceed, and eventually a “system” where AI sits at the center of operations and humans work on top of it. “What matters is not how well you use AI,” he said, “but how you redesign the organization itself around AI.”

In Kim’s framework, the defining feature of an AI-native enterprise is getting AI to understand a company’s full context—its institutional memory, decision history, workflows, and intent. He pointed to cases where AI agents automate end-to-end operations for online education programs, from building landing pages and managing enrollment to sending messages, running Zoom meetings, transcribing calls, and drafting feedback.

Another pattern he highlighted is consolidating corporate decisions and work logs into a 'single source of truth'—a unified knowledge base that becomes the authoritative reference for both humans and models. In that setup, teams can pair the repository with tools such as Claude Code to align decisions around the same shared “AI memory,” reducing internal friction caused by inconsistent or fragmented information.

Kim also described “in-the-room” deployment: using AI during customer meetings to translate requirements into working product prototypes on the spot, then demonstrating them immediately. In other examples, he said employees are loading business data—business cards, meeting notes, emails, dashboards—into AI-accessible stores and using that foundation to build internal tools tailored to their workflow, blurring the line between end user and developer.

The bigger shift, Kim argued, will be structural. He cited comments from Jack Dorsey, co-founder of Twitter and Block, that AI forces leaders to reconsider core assumptions about what a company is. Traditional hierarchies exist in part to move information down the chain and reporting back up, Kim said. But if AI can ingest and understand “all conversations, records, and context” across the firm, those information bottlenecks—and the layers built to manage them—can erode.

Instead of a pyramid, he predicted a hub-and-spoke model: an organization with AI at the center and employees connected around it in a “circular” structure. In that world, he added, every employee increasingly becomes a 'builder'—someone expected to assemble workflows, prompts, and lightweight applications rather than waiting for centralized engineering teams to deliver solutions.

Kim framed AI literacy as a baseline skill, arguing that failing to use tools like Claude Code or Codex could soon resemble skipping basic computer literacy in earlier eras. He also emphasized the scale effects of agent-driven work: one person can manage hundreds or even thousands of parallel agents, he said, changing not only how teams operate but what “company size” means in practice.

But building an AI-native company is not a one-time deployment, Kim cautioned. He compared it to raising a child: organizations must continuously collect, clean, connect, and feed data back into systems, while refining instructions and feedback loops so the AI improves. The end goal is a 'self-improving' loop—systems that review performance daily, surface mistakes, and propose process improvements without constant human prompting.

As AI becomes more central, he argued, governance becomes the harder problem. Access controls, security, and audit trails must be designed for environments where agents act on behalf of employees and can touch sensitive systems. Kim predicted a new kind of internal control function will emerge—roles focused on managing agent permissions and ensuring AI actions reflect human intent. “In an AI-centered era, what ultimately remains for humans is 'intent',” he said, calling oversight and accountability a future source of corporate advantage.

Metacon 2026 ran July 3–4 local time at Seoul’s COEX Grand Ballroom under the theme “AI Makers Rise,” bringing together builders and companies to share execution playbooks across enterprise transformation, marketing, and investment. Kim closed with a stark message: in the AI era, companies that cling to old operating models will fall behind, while those willing to abandon legacy structures and “ride the wave” of AI-driven change will be better positioned for the next phase of competition.