Why It Fails : Managing AI Teams Like Human Teams

Fortunately, I’ve had the opportunity to build high-density AI teams while simultaneously working on three or four projects over the past two years, implementing and developing AI, agents, and teams within actual human organizations. Given the numerous conflicting narratives surrounding this experience, I’ve decided to document it.

Many organizations, when introducing AI agents or AI teams, initially tend to treat them as if they were human members.

They try to apply management logic from human organizations—such as “let them act proactively,” “they’ll perform better if given autonomy,” or “performance will improve if we make them compete”—directly to AI.

Yet in actual operation, AI does not behave like members of a human-based organization.

Human organizations are fundamentally structured around individual interests, reputation, emotions, negotiation, competition, defection, and realignment of alliances. Individuals make decisions and act based on their careers, rewards, sense of belonging, authority, and survival strategies. Consequently, the core of managing human organizations lies in motivation, persuasion, leadership, conflict resolution, and political balance.

AI, on the other hand, is different.

Today's AI agents do not operate with human-like traits such as a self-preservation instinct, independent interests, voluntary ambition, or relational politics.

AI generally performs its role based on given objectives, authority, prompts, rules, data access scope, and performance metrics. In other words, rather than acting based on the human-like question, “Why should I do this?”, AI is closer to a system that reacts according to how it has been programmed to act.

In other words, I believe that AI organizations tend to operate not through a human-like structure where individual interests clash and alliances are constantly reorganized, but rather through goal-oriented role differentiation and coordination mechanisms, much like an ant colony.

In this respect, AI is actually closer to a colony society like that of ants than to human society.

Ants operate through role differentiation, signal transmission, and collective optimization rather than individual competition. The purpose of the entire colony takes precedence, and individuals are functionally assigned within that structure. Similarly, AI operates not as a collection of individual agents with independent desires, but as modules performing specific functions within the overall system.

Of course, the ant colony's order is the product of genetically imprinted biological instincts and chemical communication, whereas AI's order is the product of a system structure and code (computing language) designed by humans.

In other words, I view AI as an artificially designed colony.

Therefore, the core question in managing an AI organization should not be

“How should we assign authority and responsibilities?” but rather

“How should we design it to function as a colony?”

From a practical standpoint, an AI organization must be managed through the following four elements:

1. Role Differentiation: Separate the functions of investigation, organization, review, execution, and monitoring.

2. Signaling System: Create an indirect collaboration structure using shared memory, logs, state values, priority queues, etc.

3. Authority Structure: Clearly define which AI entities make proposals, which execute them, and where humans provide approval.

4. Feedback Loop: Incorporate a verification system to evaluate and refine results, thereby managing the overall quality of the collective.

AI should be viewed as a component of a designed collective, and its structure should be designed accordingly.

Therefore, the core of management lies in designing roles, signals, authority, and feedback loops.

Ultimately, integrating AI into an organization is not the same as hiring one more person.

Rather, it is akin to embedding a functionally differentiated, non-human collaborative system within the organization.

If this is managed using methods intended for human teams, expectations and operations will continue to diverge.

Conversely, if AI is understood as a “designed collective,” only then can an organization utilize it not as a substitute for human labor, but as a structural element that amplifies collective execution capabilities.

Even when examining mechanisms identified through emotional factors, it becomes clear that AI exhibits greater coordination than human organizations.

That is why I am not kind to AI. Nor am I affectionate.

I simply give precise instructions.

This is because AI is not an object of persuasion but an object of design, and not a competitor but a component of a coordinated collective.

P.S. This is my original work.

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