Abstract

This paper proposes AI-Native Management, a management framework designed for organizations in which artificial intelligence systems are no longer tools, but operational executors.

Classical management theories implicitly assume that humans are the sole entities capable of execution and responsibility. As AI systems increasingly perform tasks, generate artefacts, and influence organizational outcomes, this assumption breaks down.

AI-Native Management introduces the concept of semantic subjecthood and redefines management as the governance of executable semantic chains rather than the supervision of human labor.

1. Introduction

Artificial intelligence has crossed a structural boundary.

It no longer functions solely as a decision-support system or productivity tool. In many organizations, AI systems now execute tasks, generate outputs, and influence financial, legal, and strategic outcomes.

However, organizational management frameworks have not evolved accordingly.

Most management theory — including scientific management, bureaucratic administration, and modern managerial practice — presupposes that only humans can act as execution units within an organization.

This paper argues that such assumptions are no longer valid and proposes a new management paradigm: AI-Native Management.

2. Limitations of Classical Management Theory

2.1 Human-centric execution assumption

Classical management frameworks treat humans as the exclusive carriers of:

  • execution,
  • responsibility,
  • accountability.

AI is therefore positioned as a tool, infrastructure, or assistant.

In practice, AI systems increasingly:

  • perform end-to-end task execution,
  • generate regulated artefacts,
  • affect operational outcomes directly.

Responsibility remains legally human, but execution has become non-human.

The gap between execution and responsibility is structurally unmanaged.

2.2 Static organizational representations

Organizational charts presume:

  • fixed roles,
  • stable reporting lines,
  • long-lived organizational units.

AI-driven execution is dynamic, composable, and reconfigurable at task-level resolution.

Static structures fail to describe how work is actually performed.

2.3 Retrospective governance models

In most organizations, governance artefacts are:

  • created after execution,
  • assembled under compliance pressure,
  • disconnected from operational logs.

When AI operates continuously and autonomously, retrospective governance becomes unsustainable.

3. AI-Native Management: Core Concepts

3.1 Semantic Subjecthood

AI-Native Management defines the basic management unit as a semantic subject.

A semantic subject may be:

  • a human,
  • an AI agent,
  • or a composite human–AI role.

Each subject is defined by:

  • an identity (e.g. DID or equivalent identifier),
  • a role specification,
  • task-level execution records,
  • traceable responsibility boundaries.

Management shifts from supervising people to governing interactions between semantic subjects.

3.2 Dynamic Semantic Chains

Organizational reality is modeled as a chain of linked semantic layers:

  • Strategic intent (e.g. Startup Matrix)
  • Task definitions and execution
  • Subject assignment (human / AI)
  • Legal entity binding
  • Governance artefact generation

Org charts are derived views. The primary object is the execution chain.

3.3 Real-time Governance

In AI-Native Management, every executed task produces:

  • a run log,
  • a responsibility trace,
  • a governance-relevant record.

Operational execution and governance documentation are not separate processes.

They are different projections of the same execution reality.

4. Architectural Implications

AI-Native Management naturally maps to a multi-layer system architecture:

  • Startup Matrix
    Strategic intent and resource overview.

  • Task Database
    Task scheduling, delegation, and tracking.

  • Staff Database
    Human and AI subject definitions.

  • Entity Database
    Legal entities and jurisdictional responsibility.

  • Governance Database
    Contracts, policies, data rooms, audit trails.

Together, these layers form a closed semantic loop that aligns execution, accountability, and governance.

5. Illustrative Scenario: A Day in an AI-Native Startup

  • 09:00
    A CFO Agent generates an updated runway report. A human executive reviews and validates it.

  • 11:00
    A CTO assigns engineering tasks via an Engineering Manager Agent.

  • 14:00
    A PR Agent collaborates with a human CEO to produce an investor update.

  • 16:00
    A policy agent prepares documents for a Japanese subsidiary. A human signs as the legal ответствен party.

All actions automatically update governance records, producing an IPO-ready operational trace.

6. Organizational Impact

6.1 Human role transformation

Humans shift from execution-focused roles toward:

  • decision authority,
  • legal accountability,
  • narrative and system design.

6.2 AI role transformation

AI systems evolve from tools to:

  • organizational subjects,
  • departments,
  • replaceable execution units.

They can be upgraded, audited, or decommissioned within defined responsibility boundaries.

6.3 Governance evolution

Day-to-day operations and IPO preparation are no longer separate activities.

Governance becomes continuous and execution-aligned.

7. Future Directions

AI-Native Management raises broader implications:

  • accounting standards must address AI-generated value,
  • labor law must confront non-human execution,
  • governance must become execution-aware.

The endpoint is an organization that operates as a semantic, executable legal entity.

8. Conclusion

AI-Native Management is not a software feature and not a management tool.

It is a structural redefinition of how organizations are managed when AI becomes an executor.

Organizations that fail to update their management paradigm will accumulate governance debt.

AI-Native organizations treat execution, compliance, and strategy as a unified system.

Status and Scope

This document is a working paper.

Technical implementations, architectures, and applied systems are documented separately.

The purpose of this paper is to establish a management theory foundation for AI-native organizations.