Summary of Key Points
This article illustrates through the author's own failed AI startup attempt (a CEO digital avatar project) and the practical implementation of AI in a telemarketing company that the essence of an "AI-native team" is not about using AI tools, but rather about "organizational restructuring." Many companies mistakenly believe that using AI tools makes them AI-native, when in fact they need to address underlying management issues such as chaotic business processes, inconsistent data standards, unclear permissions, and difficulties in implementing changes. The article proposes a hierarchical model for AI-native teams (tool assistance, process integration, rule-based operations) and emphasizes that the management's commitment and willingness to transform are crucial for the successful adoption of AI—no matter how advanced the technology is, it cannot bypass the fundamental aspects of management.
Detailed Explanation
#### 1. The Misconception of AI-Native Teams: It’s About Organizational Restructuring
Many people think that an AI-native team means employees use AI tools and are highly efficient, but the author points out this is a major misunderstanding. The core of being AI-native lies not in the technology itself, but in redesigning the entire way the team works around AI—how business processes are conducted, how information flows, who is responsible for what tasks, and how outcomes are evaluated. For example, the author's CEO digital avatar project aimed to use AI to manage corporate information flow, automate repetitive tasks, and provide decision-making advice. However, it failed because no one could clearly explain things like where leads came from, who was responsible for assigning them, or how they should be converted into actionable opportunities—the business processes were too chaotic for AI to effectively intervene. It took a telemarketing company three months to clarify their core business processes and standardize terminology before the AI system could be successfully implemented. This shows that building a solid organizational foundation is essential before integrating AI.
#### 2. The Four Major Barriers to Establishing an AI-Native Team: All Are Management Challenges
The author highlights common pitfalls in implementing AI-native systems, which are primarily management-related issues:
- Lack of Clear Business Processes: Telemarketing companies relied on manual processes and coordination through WeChat groups; no one could articulate a complete workflow. AI requires clear processes to replace repetitive tasks, so extensive communication with various departments is necessary to transform vague practices into well-defined procedures.
- Inconsistent Data Standards: Different departments used different terms for the same concepts (e.g., "leads" were referred to as "business opportunities" by sales and "accounts to be collected" by finance). AI cannot work effectively with such inconsistencies; a unified terminology was only established after multiple meetings involving all departments.
- Unclear Permissions: Who can access what data and make changes? These issues are typically resolved manually, but with AI, they can lead to errors if not handled properly. The telemarketing company spent a week creating a detailed permission guide to clarify issues like salespeople not being able to see payment information.
- Difficulties in Implementation: Employees often ignore instructions; clear "standard procedures" must be established (e.g., when and how data should be entered) for the AI system to function effectively.
These problems are unrelated to technology but are essential management tasks that must be addressed before AI can be successfully integrated.
#### 3. The Three Levels of AI-Native Teams
The author divides AI-native teams into three levels, with a significant difference in efficiency:
- Level 1 (Tool Assistance): Employees sporadically use AI tools (e.g., using ChatGPT for writing copy), but there is no overall organizational improvement.
- Level 2 (Process Integration): AI is integrated into departmental processes (e.g., using AI to assign leads in telemarketing), but final decisions are still made by humans (e.g., managers can interfere with lead assignments).
- Level 3 (Rule-Based Operations): AI directly affects rewards, punishments, and promotions (e.g., AI evaluates employee performance without arbitrary manager intervention). This is the true definition of an AI-native team, but few companies achieve this level.
A more detailed criteria for evaluation includes whether AI has penetrated into individual tasks, job-specific actions, departmental processes, core business operations, and strategic decision-making. Most companies remain at the first two levels.
#### 4. The Role of Management in AI Adoption
The author’s example with the lead allocation system highlights the importance of management's attitude: Although the AI system reduced the number of people needed to handle leads from five to one and improved efficiency, it was discontinued because the manager was dissatisfied with a particular sales leader and preferred to make decisions based on traditional criteria. This shows that the adoption of AI ultimately depends on whether managers are willing to let AI play a decisive role in decision-making. Many managers claim to support AI fully but still prefer control; they may resist changes because they perceive fairness as threatening their authority.
#### 5. Advice for Companies and Individuals
The author advises against trying to use AI as a shortcut to avoid management tasks. The prerequisite for building an AI-native team is to clarify organizational foundations—business processes, data standards, and permission rules. For companies, this means taking the time to organize their business, standardize terminology, and establish clear roles. For individuals, it’s important to understand which tasks AI cannot perform and focus on those that require creativity and interpersonal skills.
In conclusion, the author emphasizes that behind AI-native initiatives lies complex management work, but this is what truly creates value by eliminating repetitive tasks and enabling people to engage in more meaningful work. The article dispels the myth of AI being advanced and mysterious, highlighting that the essence of AI-native teams is using it to drive organizational improvement, which requires addressing long-standing management issues. Without a solid management foundation, even the most advanced AI technology will be ineffective.