Summary of Key Points
This article reflects the author's two years of experience in AI projects, sharing their understanding of "AI-native" concepts and "AI-native organizations." The author distinguishes between two types of AI applications: "direct offspring" (business models directly created by AI, such as AI-generated code or AI customer service) and "supporting offspring" (technical enhancements that complement AI, such as making systems more stable). The key terms used are "replacement" and "100% stable replacement." Through real cases from development teams and e-commerce companies, the author illustrates that an AI-native organization is not merely a technical issue; it requires a combination of employee AI capabilities, appropriate mechanisms and processes, evaluation systems, and even specialized AI operating systems. The article also discusses the stages of developing an AI-native organization (from sporadic use to having AI at its core) and provides practical steps for implementation.
What Exactly is AI-Native?
The author categorizes AI applications into two types:
- Direct Offspring: Business models directly created by AI, such as AI-generated code, AI-created comics, or AI customer service. The goal of these applications is to replace human tasks more efficiently (for example, AI writing code to save programmers time), with the primary focus on generating revenue.
- Supporting Offspring: Technologies designed to address AI's limitations. For instance, systems that help AI remember information or ensure its output stability, as well as datasets for training AI models. The aim of these tools is to enable 100% stable replacement of human tasks, with the focus on supporting the primary business functions.
In simple terms, AI-nativity means using AI to replace humans, but this requires ensuring that AI can work reliably first. The "direct offspring" applications are responsible for replacing tasks, while the "supporting offspring" technologies help make this replacement process more stable.
Why Doesn't AI Improve Organizational Efficiency Despite Its Benefits for Individuals?
The author points out a paradox: While AI significantly enhances individual productivity (for example, small teams can increase efficiency by tenfold using AI), overall organizational efficiency may not improve, and issues can arise:
- Employee Misbehavior: High-performing employees might use AI to free up time but then slack off instead of working more.
- Mismanagement by Companies: Companies may initially provide resources (e.g., AI tokens worth $1000) only to realize limited improvements and subsequently revoke them or merge departments (for example, stopping recruitment for a specific position to shift tasks to employees who know how to use AI).
- Root Management Problems: Organizational inefficiencies often stem from information distortion (requirements becoming distorted as they are passed down) and ineffective evaluation systems (no consequences for poor decision-making at higher levels).
Solutions include using methodologies like the "SDD" (Single Source Development), which standardizes requirement documentation with clear goals, scope, and acceptance criteria. If upstream requirements do not meet these standards, downstream teams (including AI systems) can refuse to proceed. This approach addresses issues at the source, allowing both humans and AI to work more efficiently. Even without AI, such processes can improve efficiency; AI merely accelerates this process.
An AI-Native Organization Is Not Just About Technology
The author outlines the evolution of an AI-native organization from version 1.0 to version 3.0:
- Version 1.0: Matching employee AI capabilities with appropriate processes and systems. For example, using SDD to integrate AI into workflows.
- Version 2.0: Adding organizational evaluation mechanisms. For instance, e-commerce companies may implement intelligent task allocation systems, but these may be discontinued if management targets specific employees.
- Version 3.0: Incorporating an AI operating system that automates process management and evaluation. The author mentions a tool they developed, the "CEO Digital Double," which manages information flows, identifies inefficient processes, and provides recommendations, thereby reducing management costs.
The core idea is that an AI-native organization requires more than just purchasing AI tools; it needs to ensure that employees know how to use them, processes are adapted accordingly, evaluations are recognized, and all these elements are integrated into a systematic framework.
Challenges in Implementing AI-Native Organizations
Many companies struggle to implement AI-nativity due to poor foundations:
- Disorganized Business Processes: No one has a clear understanding of the overall business operations (e.g., unclear sources or distribution of tasks).
- Unstructured Data: Handling large amounts of data manually, such as entering customer information into Excel, often leads to errors.
- Chaotic Management: New management changes may disrupt existing systems, or internal politics may prevent the use of effective AI tools.
The author notes that over 50% of Chinese companies are in this state but still wish to benefit from AI. The first step towards implementing AI-nativity is to address these fundamental issues, such as automating manual tasks and using tools to organize unstructured data (e.g., converting messy customer conversations into clear requirements).
Stages of Developing an AI-Native Organization
The author outlines three stages in the development of an AI-native organization:
1. Sporadic Use: Employees occasionally use AI tools without a coherent plan.
2. Copilot Stage: Humans still play a central role, and while individual productivity improves, overall organizational efficiency remains low (e.g., programmers use AI to write code, but the team's overall progress is slow).
3. Native Stage: Workflows are designed around AI, significantly enhancing organizational efficiency.
Practical steps include automating tasks, using tools to process unstructured data, regularly monitoring performance indicators, and implementing feedback loops to continuously improve AI capabilities (e.g., the more data AI processes, the more accurate its recommendations become).
In Conclusion
An AI-native organization is not about showing off technical prowess; it's about using AI to solve practical problems that are time-consuming and tedious. To truly leverage AI’s potential, companies need to establish a solid foundation, align processes and evaluations with AI capabilities, and integrate these elements into systematic frameworks. What matters to managers is the impact that AI has on business outcomes, not just the technology itself.