Summary of Key Points
This article dispels the misconception that "AI-native companies represent a standard of excellence," proposing instead that AI-native enterprises can be categorized into four types based on the intersection of organizational paradigms (division of labor/end-to-end) and AI roles (tools/substances). These types include: Enabling, Form-First, Spirit-First, and Integrated. It also clarifies three common misconceptions—namely, that the amount of AI usage determines a company's level of advancement, that higher AI integration equates to a more advanced company, and that all AI-native companies are merely software companies. The article emphasizes the need for organizational governance to shift from a "people-managed" approach to one based on "meta-rules" (set of fundamental principles). Furthermore, it highlights the importance of "intelligent entities" (new entities resulting from the integration of human and machine cognition) in balancing efficiency and value creation, ultimately helping companies identify their own type and development direction.
I. AI-native Companies Are Not Binary; They Exist in Four Real Forms
To determine which category an AI-native company belongs to, ask two questions: How does the organization operate? What role does AI play within it? The intersection of these two aspects leads to the four types:
1. Enabling Type: AI serves as a tool, and the organization continues to function according to its traditional division of labor.
- Examples: Quality inspectors use AI for preliminary visual checks, lawyers use AI to review contracts, doctors use AI to analyze medical images. Roles and departments remain unchanged, but individual efficiency significantly improves.
- Current Status: 90% of companies are at this stage, which may represent the long-term optimal solution for highly standardized industries (e.g., manufacturing quality inspection).
2. Form-First Type: AI is a tool, but the organization has reorganized around a "value closed loop."
- Examples: Insurance claims processing is handled by a single team with AI assistance, completing in 2 hours instead of the usual 15 days. Although the organizational structure has changed, judgment and decision-making still rely on humans.
3. Spirit-First Type: AI takes the lead, but the organization continues to operate according to its traditional division of labor.
- Examples: AI handles thousands of refund requests independently in a day; unmanned warehouses manage inventory autonomously, but the organizational structure remains unchanged.
4. Integrated Type: AI is the primary driver, and the organization evolves around a "value closed loop."
- Characteristics: Tasks are generated dynamically based on the value closed loop; intelligent entities (a blend of human and machine cognition) make decisions, and capabilities are called upon as needed. The organization evolves autonomously.
Key Concept: There is no inherent superiority or inferiority among these types; they represent different stages in the development of AI integration within an organization, similar to the transformation from a tadpole to a frog.
II. Three Common Misconceptions to Avoid
1. Misconception 1: More AI Usage Means a More Advanced Company?
- Example: Two chain restaurants use similar amounts of AI (for writing menus, processing invoices, and generating inspection reports), but one's organizational processes are the same as they were five years ago (new product approval takes 4 months), while the other operates in a closed-loop system (new products are launched in 21 days). Usage does not define the type; the organization's logic does.
2. Misconception 2: Higher AI Integration Means a More Advanced Type?
- Example: A regional bank's loan approval process includes 12 AI-driven steps, but the overall process remains the same (12 steps in total). This is merely an example of fully implementing an enabling approach without changing the underlying organizational paradigm.
3. Misconception 3: All AI-native Companies Are AI Software Companies?
- Reality: Software companies are merely pioneers; their products are digital and can be easily transformed by AI. However, the AI-native paradigm will spread to all industries—chain restaurants, chemical plants, hospitals, etc.—without requiring them to first become software companies.
III. Organizational Paradigms: Division of Labor vs. End-to-End
Modern organizations can be categorized into two logical types, as illustrated by the insurance claims processing example:
- Division of Labor Paradigm: Customer reports → Customer Service → Inspection → Damage Assessment → Claim Approval → Finance (6 departments and 7 steps). Customers wait 15 days (only 6 hours of actual work are done, with the rest being spent on internal processes).
- Logic: Complex tasks are broken down into standardized parts and managed hierarchically. Disadvantages: Information distortion, low efficiency, and conflicting departmental KPIs.
- End-to-End Paradigm: Customer reports → AI automatically assesses damage and verifies compliance → Automated payment (only in exceptional cases is human intervention required). Customers receive payments within 2 hours.
- Logic: The entire process is centered around the customer's claim, with humans and AI working together. Core change: The basic unit shifts from "departments" to "tasks," and the focus shifts from departmental goals to achieving a complete value closed loop.
In Summary: The division of labor approach involves breaking things down first and then reassembling them, while the end-to-end approach aims for a direct and seamless closed loop.
IV. Don’t Manage AI Using Traditional Human Management Methods
Many companies that introduce AI still use traditional human management methods (job responsibilities, approval processes, KPIs), leading to three misalignments:
1. Misalignment of Motivation: Online education companies use AI for customer support under a "traffic volume" KPI, resulting in poor quality due to lack of motivation (AI doesn’t need promotions or salary cuts; it should be driven by value creation).
2. Misalignment of Capabilities: Retail procurement AI is limited to processing data from its own department, preventing it from leveraging sales, inventory, and logistics information to address shortages (AI could manage these resources globally but is restricted by job descriptions).
3. Misalignment of Behavior: Manufacturing equipment alerts are processed instantly by AI, but the system requires a supervisor’s approval before action, leading to delays (AI should act autonomously based on meta-rules rather than manual approvals).
Solution: Replace traditional management with "meta-rules" that ensure compliance, ethics, and safety. Allow AI full freedom within these rules while strictly prohibiting any actions outside of them.
V. Why Are Intelligent Entities Needed? Isn’t Human + AI Enough?
Some believe that combining human decision-making with AI is sufficient, but this leads to dilemmas:
- If humans make all decisions, their cognitive capacity becomes a bottleneck, limiting system efficiency.
- If AI makes decisions, humans may lose their role as the value creator, potentially leading to ethical and brand-related issues.
Intelligent Entities are the solution: They represent a new entity that integrates human and machine cognition. For example, a consumer goods company’s manager and the system jointly develop distribution plans, making it difficult to distinguish between human input and system calculations. The result is both speed and adherence to the brand’s values.
VI. How to Quickly Identify Your Company’s Type
Use a simple criterion: Examine your core business systems:
- If the software is the main component with AI added (AI is embedded within it, but the system can still function without AI), your company follows a division-of-labor paradigm (Enabling or Spirit-First type).
- If AI is the main component, and the software serves as a tool for its functions, your company follows an end-to-end paradigm (Form-First or Integrated type).
The ultimate goal of AI-native companies is to evolve from machines that assemble AI modules into self-evolving organizational entities. This is the true meaning of being "native" to AI.
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(Note: All company examples in this article are illustrative and do not refer to specific real companies.)