OECUMENE
Governance Architecture для Complex Business Systems
AI Management
Managing Artificial Intelligence as Part of the Management System
In the OECUMENE model, AI Management is a discipline at the level:
WHAT? · What?
WHAT asks:
What emerges as a result of realizing WHY through the architecture, methods, and practices of HOW?
AI Management is the result of developing the system’s ability to manage artificial intelligence as part of organizational operations, decision making, capabilities, and management practices.
The Place of AI Management in the OECUMENE Model
The central logic is:
WHY → HOW → WHAT
This is a causal relationship:
WHY
Why does this matter?
Why does the system exist?
Why has this path been chosen?
↓
HOW
How is this realized?
Architecture
Methods
Practices
Frameworks
Processes
Tools
Technologies
↓
WHAT
What emerges as a result of realizing WHY through the architecture, methods, and practices of HOW?
Disciplines
Capabilities
Practices
WHY AI Management
The development of artificial intelligence changes how organizations operate, make decisions, develop capabilities, and create value.
This creates new questions:
- how AI should become part of management;
- how responsibilities should be distributed between people and AI;
- how AI capabilities should be developed and managed;
- how management practices should adapt to an AI-enabled environment;
- how organizations should decide where and how AI should be applied.
AI Management asks:
Why does an organization need to develop the ability to manage AI as part of its management system?
HOW AI Management
AI Management is realized through an architecture of management practices that connects AI capabilities with organizational purpose, structures, processes, people, and decisions.
This includes:
- management principles;
- roles and responsibilities;
- decision-making mechanisms;
- AI capability development;
- processes and workflows;
- performance management;
- resource allocation;
- integration of AI into organizational practices.
AI Management as a Discipline
AI Management is a field of knowledge and practice that helps a system develop capabilities for:
- managing AI capabilities and applications;
- integrating AI into organizational management;
- adapting management practices to an AI-enabled environment;
- coordinating human and AI capabilities;
- making decisions about AI adoption and development;
- managing organizational change resulting from AI.
Core Elements of AI Management
AI Strategy
Defines:
- how AI relates to organizational purpose;
- where AI capabilities may create value;
- which AI opportunities deserve attention;
- how AI initiatives should be prioritized.
Roles and Responsibilities
Defines:
- who is responsible for AI-related decisions;
- how responsibilities are distributed;
- how human and AI roles interact;
- who owns AI capabilities and outcomes.
AI Capability Management
Enables the organization to:
- identify required AI capabilities;
- develop internal capabilities;
- evaluate existing capabilities;
- manage capability gaps;
- continuously adapt to changes in AI.
Decision Making
Defines:
- which decisions may involve AI;
- how AI supports human decisions;
- where human judgment remains essential;
- how decisions involving AI are evaluated.
Organizational Integration
Connects AI with:
- organizational structures;
- processes;
- workflows;
- products;
- projects;
- management practices.
The objective is to move AI from isolated experimentation toward meaningful organizational use.
Change and Adaptation
Enables organizations to:
- adapt management practices;
- redesign roles and workflows;
- learn from AI-enabled work;
- respond to changing AI capabilities;
- manage organizational transformation.
AI Management and AI Governance
AI Management and AI Governance address different but interconnected capabilities.
The relationship is:
AI Management
↓
managing AI as part of organizational operations and management systems
↓
AI Governance
↓
governing AI through principles, responsibilities, controls, and risk management.
AI Governance provides governance mechanisms within the broader context of AI Management.
AI Management and AI Literacy
AI Literacy provides an important foundation for AI Management.
The relationship is:
AI Literacy
↓
understanding and working effectively with AI
↓
AI Management
↓
managing AI as part of organizational operations and management systems.
An organization cannot effectively manage AI without developing the ability of its people to understand and work with AI.
AI Management and AI Navigation
AI Navigation and AI Management address different stages of organizational capability.
The relationship is:
AI Navigation
↓
finding direction in an AI-enabled environment
↓
AI Management
↓
managing AI as part of organizational operations and management systems.
Navigation can help determine where AI should be explored, while management addresses how AI becomes part of the organization.
AI Management as an Intellectual Asset
In OECUMENE, AI Management is an evolving intellectual asset.
It may include:
- management models;
- AI management frameworks;
- organizational practices;
- capability models;
- decision-making approaches;
- implementation practices;
- relationships with other disciplines.
Recursiveness
Like every element of OECUMENE, AI Management can be considered through:
WHY → HOW → WHAT
For example:
WHY:
Why should AI become part of the management system?
↓
HOW:
How can management architecture, roles, processes, capabilities, and practices be adapted?
↓
WHAT:
What AI Management capabilities emerge?
Working Position of OECUMENE
AI Management is not simply the management of AI projects or AI technologies.
It is the capability of an organization to manage artificial intelligence as part of its management system and to adapt organizational structures, practices, decisions, and capabilities to an AI-enabled environment.
The central question is:
How should management change when artificial intelligence becomes part of the management system itself?
Connections with Other OECUMENE Disciplines
AI Management is connected with:
