Summary of Key Points
Many companies regard AI as a “high-level tool” to optimize existing processes (such as reducing labor costs), but this is merely patching up old organizational structures; they are not truly AI-native enterprises. True AI-native organizations need to completely restructure their logical frameworks: shifting from traditional “division of labor + hierarchical” assembly lines to flexible, end-to-end closed-loop systems; moving from a model where humans use tools to one where humans, AI digital employees, and robots collaborate in symbiosis; and changing from old management rules based on error prevention to governance principles that allow for freedom within defined boundaries. The transformation should not be rushed or limited to minor adjustments; instead, it should involve a dual-track approach with targeted restructurings, led by top executives.
1. Stop patching the old system with AI; embrace end-to-end closed-loops
Traditional companies operate like assembly line factories: a single business process must go through multiple departments (for example, in insurance claims handling, from customer service to dispatching, investigation, damage assessment, compliance, to finance). Customers wait 15 days, but only 6 hours of actual work are done; the rest of the time is spent on internal departmental barriers. This was due to limited human capabilities in the past, which necessitated division of labor. However, the cost of coordinating these divisions (such as cross-departmental meetings and approvals) often outweighed the benefits.
AI makes it possible for a single team to handle the entire business process. For instance, in AI-based claims processing, once a customer reports an issue, the system automatically processes photos for damage assessment, verifies compliance, and generates a proposal; only in cases of suspected fraud does human intervention occur, and customers receive their compensation within 2 hours. In this new model, organizations are no longer composed of fixed departments but of task-driven temporary teams that assemble and disperse as needed, resembling an intelligent network that can adjust on the fly, with departmental barriers disappearing altogether.
2. Your new colleagues: AI is no longer just a tool; it’s a digital employee capable of working independently
In the past, AI was an auxiliary tool that responded to queries. Now, it has evolved into three types of “new colleagues”:
- AI Agents (digital employees): They can autonomously break down tasks and allocate resources, such as automating complex processes (like coding or generating reports).
- Intelligent robots: They handle physical tasks (such as lifting and sorting).
- Human-AI hybrids: Humans and AI work together seamlessly. For example, when a regional manager makes inventory distribution decisions, AI provides data and performs calculations in real-time; the final outcome is a blend of her experience and AI’s computational power, to the point where it’s hard to distinguish which part was conceived by humans.
The division of labor has also changed: Humans are responsible for setting direction, innovating, and ensuring ethical standards (such as handling extreme exceptions), while AI handles standardized and repetitive tasks, and robots handle physical operations. The core capability of an organization becomes the ability to coordinate the collaboration of these three elements.
3. Manage AI using different principles; unleash its potential with “meta-rules”
Many companies try to manage AI using traditional human management methods, which often leads to problems. For example, requiring AI systems to obtain signatures from workshop supervisors (slowing down responses that could be instant) or setting KPIs for customer service AI to increase call volumes, leading to superficial responses.
AI requires governance based on “meta-rules”: the underlying logic should shift from a default prohibition and permission-by-rule basis to allowing freedom within defined rules and prohibiting actions outside of them. Companies don’t need to create hundreds of pages of SOPs; instead, they should establish a few key principles (such as compliance, security, budget limits), and let AI find the best solutions on its own as long as it adheres to these guidelines.
Financial HR also needs to adapt: Instead of measuring “human efficiency” by the number of employees, they should measure “AI efficiency”—similar to how electricity costs are managed, by tracking the usage of computing resources and model invocation rates to determine whether AI investments are worthwhile.
4. Don’t rush the transformation; proceed with a dual-track approach and targeted restructurings
The transformation process can fall into two common pitfalls: either making minor adjustments (combining old processes with AI, which is outperformed by true AI-native organizations) or being too aggressive (going fully automated, leading to employee resistance and system instability). The correct approach is:
1. Targeted restructurings: Select high-value areas (such as customer service or software development) for thorough transformation—humans set the direction and optimize during the day, while AI automates coding and testing at night, gradually transforming the entire business process.
2. Reframe talent development: New employees used to gain experience through basic tasks (like writing summaries and following procedures); now, these tasks are taken over by AI, so they need to develop skills in human-AI collaboration (such as directing AI and interpreting its results).
3. Top leadership involvement: This kind of change cannot be achieved by simply buying software or adjusting performance metrics; executives must plan for future profit streams, reassign roles between humans and AI, and shift the corporate culture from one that fears mistakes to one that tolerates experimentation and encourages innovation.
Conclusion
The future competitiveness of companies will not lie in having the “strongest tools” (large models are public resources), but in possessing “proprietary intelligence” (transforming internal knowledge and experience into AI capabilities) and “intelligent operational efficiency” (the ability to coordinate human-AI collaboration more effectively than competitors). True AI-native organizations are not created through assembly; they evolve naturally, much like living organisms.