Artificial Intelligence Governance & Innovation Management in Organizations Tbilisi 02.Nov.2026 (103600529_72653)

Course Overview:

With the rapid expansion of artificial intelligence adoption across governments and enterprises, the need for robust AI governance frameworks has become increasingly critical. Organizations are now integrating AI technologies into decision-making, data analytics, operational optimization, and digital transformation initiatives. However, without clear governance structures, the deployment of AI systems can introduce significant risks related to transparency, accountability, privacy, and regulatory compliance.

This training course provides a comprehensive understanding of Artificial Intelligence Governance (AI Governance) and its practical application within modern organizations. It explores how institutions can manage AI systems responsibly while simultaneously fostering innovation and digital transformation. The program integrates strategic perspectives inspired by the concept presented in “Zero to One: Mastering Innovation and Success in Startups,” emphasizing that breakthrough innovation emerges from creating new technological value rather than merely competing within existing markets.

Participants will learn how to design AI policies, governance models, and risk management frameworks that support responsible AI adoption. The course also highlights the critical role of data governance, ethical AI principles, and organizational innovation strategies in enabling sustainable and responsible AI implementation. By the end of the program, participants will gain a strategic perspective on how to balance technological innovation with institutional governance and regulatory compliance.

Target Audience:

• Executive leaders and senior decision makers
• Strategy and corporate planning managers
• Digital transformation and innovation leaders
• IT and technology managers
• Governance, risk, and compliance professionals
• Data analytics and artificial intelligence managers
• Digital policy advisors and technology consultants

Target Departments:

• Strategy and corporate planning departments
• Digital transformation departments
• Innovation and research departments
• Information technology departments
• Data governance and AI management units
• Risk management and compliance departments
• Policy development and regulatory affairs departments

Target Industries:

• Government and public sector organizations
• Banking and financial institutions
• Technology and telecommunications companies
• Energy and industrial sectors
• Healthcare organizations
• Education and digital learning institutions
• Regulatory authorities and public agencies

Course Objectives:

By the end of this course, participants will be able to:

• Understand the concept and importance of Artificial Intelligence Governance in modern organizations
• Analyze the relationship between AI governance and organizational innovation
• Design effective AI governance frameworks within institutions
• Develop comprehensive AI policies to ensure responsible AI adoption
• Apply ethical AI principles in institutional decision-making systems
• Implement data governance and AI governance models within organizations
• Integrate digital innovation management with AI strategies
• Identify and manage risks associated with AI technologies

Training Methodology:

This training program follows an interactive and practice-oriented methodology designed to ensure that participants can translate governance concepts into real institutional practices. The course combines conceptual learning with practical application to enable participants to understand AI governance principles and apply them within their organizations.

Participants will engage in interactive lectures that explain the foundations of AI governance frameworks and the governance of intelligent technologies. These sessions will be complemented by real-world case studies demonstrating how global organizations design and implement AI governance policies and ethical AI frameworks.

The training will also include group discussions and collaborative exercises where participants evaluate different models of AI governance and explore governance strategies for managing AI systems in organizations. Participants will examine real examples of data governance models and AI governance practices used by leading global institutions.

Additionally, the methodology includes practical workshops, scenario-based exercises, and guided discussions on managing AI innovation while mitigating technological risks. These activities help participants convert theoretical knowledge into practical frameworks that can be implemented within their own institutions.

Course Tools:

• Specialized reading materials on Artificial Intelligence Governance
• Case studies on global AI governance implementation
• Practical models for AI policy analysis and development
• Risk assessment worksheets for intelligent technologies
• Data governance and AI governance framework examples
• Innovation management models for digital transformation initiatives

Course Content:

Day 1: Foundations of Artificial Intelligence and Institutional Governance

• Topic 1: Evolution of artificial intelligence and its impact on organizational innovation
• Topic 2: The concept of Artificial Intelligence Governance in modern institutions
• Topic 3: The relationship between AI Governance and digital innovation management
• Topic 4: Lifecycle of artificial intelligence systems within organizations
• Topic 5: Governance of intelligent technologies and associated risk management
• Topic 6: Principles of responsible AI and ethical artificial intelligence
• Review: Reviewing key concepts and institutional applications

Day 2: Global Frameworks for Artificial Intelligence Governance

• Topic 1: Global principles for regulating artificial intelligence
• Topic 2: Data governance and AI governance models in organizations
• Topic 3: AI policies implemented by governments and corporations
• Topic 4: Regulatory standards for intelligent technologies
• Topic 5: The role of governance in supporting organizational innovation
• Topic 6: Legal and regulatory challenges of artificial intelligence
• Review: Analysis of international AI governance frameworks

Day 3: Risk Management and Ethics in Artificial Intelligence

• Topic 1: Types of risks associated with AI applications
• Topic 2: Algorithmic bias and its impact on institutional fairness
• Topic 3: Ethical AI and transparency in intelligent systems
• Topic 4: Data protection and privacy in AI-driven technologies
• Topic 5: The role of data governance and AI governance in risk mitigation
• Topic 6: Evaluating the impact of AI on institutional decision-making
• Review: Practical case discussions on AI risk management

Day 4: Designing an Artificial Intelligence Governance Framework

• Topic 1: Developing institutional AI policies
• Topic 2: Roles and responsibilities in managing AI systems within organizations
• Topic 3: Oversight and auditing mechanisms for intelligent systems
• Topic 4: Implementing data governance and AI governance frameworks
• Topic 5: Monitoring the performance of AI models
• Topic 6: Regulatory compliance for artificial intelligence applications
• Review: Building an institutional AI governance model

Day 5: Strategic Innovation Using Artificial Intelligence

• Topic 1: Artificial intelligence strategy for modern organizations
• Topic 2: Managing digital innovation using intelligent technologies
• Topic 3: Integrating artificial intelligence into organizational business models
• Topic 4: AI-driven digital transformation strategies
• Topic 5: Building a culture of innovation within organizations
• Topic 6: Developing a roadmap for AI governance and innovation
• Review: Presentation of institutional implementation plans

FAQ:

What prerequisites are required to attend this course?

This course does not require advanced technical knowledge in programming or data science. It focuses primarily on the strategic, governance, and policy aspects of artificial intelligence, making it suitable for managers, decision-makers, and professionals involved in digital transformation and innovation initiatives.

What is the duration of the daily sessions and the total training hours?

Each training day typically includes 4–5 hours of learning sessions, including discussions and practical exercises. The full program spans five training days, totaling approximately 20–25 training hours.

Does the course focus on technical AI development or governance and policy?

The course focuses primarily on AI governance, regulatory frameworks, ethical AI, and institutional AI policy development rather than programming or algorithm development. The goal is to enable leaders and professionals to manage AI adoption strategically and responsibly.

What Makes This Course Different:

This program distinguishes itself by combining Artificial Intelligence Governance with innovation management within a strategic institutional framework. Rather than focusing solely on technical aspects of AI, the course emphasizes how organizations can transform AI into a strategic capability that drives sustainable innovation and responsible technological growth.

The course integrates modern AI governance principles with digital innovation management practices, providing participants with practical insights on designing AI policies and governance models for intelligent technologies within organizations. It also highlights the relationship between data governance, AI governance, and strategic decision-making, enabling organizations to balance technological advancement with effective risk management.

Furthermore, the course incorporates real global experiences in AI governance regulation and ethical AI implementation, supported by case studies and practical frameworks. This approach ensures that participants leave the program with actionable strategies for implementing responsible AI governance while sustaining innovation in their organizations.