The British Academy for Training and Development offers this training program titled “Artificial Intelligence (AI) for Leaders and Managers”, designed to keep pace with the rapid transformations in modern work environments, where artificial intelligence has become a central driver of institutional strategy and decision-making.

This program focuses on providing leaders and managers with a clear and comprehensive understanding of how AI technologies can be integrated into operational processes and managerial decision-making. Participants will explore the potential of AI in improving efficiency, supporting innovation, analyzing data, and understanding organizational behavior.

The program also supports the development of a digitally driven leadership culture capable of addressing the challenges of intelligent transformation and prioritizing the safe, effective, and responsible use of artificial intelligence, thereby enhancing institutional performance and competitiveness.

Who Should Attend?

• Chief Executive Officers and General Managers.

• Department and Division Heads in large organizations.

• Digital Transformation and Organizational Development Leaders.

• Decision-makers in public and private sector organizations.

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• Understand the fundamental concepts of artificial intelligence technologies and their institutional applications.

• Analyze the strategic impact of artificial intelligence on leadership and decision-making processes.

• Assess opportunities and risks associated with the use of artificial intelligence in workplace environments.

• Develop a roadmap for intelligent, data-driven institutional transformation.

• Build a leadership culture that supports innovation and adopts smart solutions with awareness and strategic planning.

• Introduction to Artificial Intelligence and Its Historical Development

  • General concept of artificial intelligence and key milestones.

  • Differences between artificial intelligence and traditional systems.

  • Types of artificial intelligence and their applications.

• The Technical Environment of Artificial Intelligence

  • Big data and its role in machine learning.

  • The relationship between artificial intelligence and cloud computing.

  • Structure of intelligent models and training cycles.

• Data Analysis for Intelligent Decision-Making

  • Role of algorithms in prediction and decision-making.

  • Use of classification and predictive analytics techniques.

  • Transforming data into actionable insights.

• AI-Based Decision Support Systems

  • Characteristics of automated decision support systems.

  • Integrating artificial intelligence into enterprise resource systems.

  • Examples of AI-supported strategic decisions.

• Digital Leadership Skills

  • Characteristics of effective leadership in intelligent environments.

  • Managing technical teams and understanding the language of artificial intelligence.

  • Balancing technology and human capital in resource management.

• Cultural Transformation in Intelligent Organizations

  • Managing resistance to change and transformation challenges.

  • Building an innovation-driven organizational environment.

  • Enhancing analytical thinking within work teams.

• Applications of Artificial Intelligence Across Sectors

  • AI applications in finance, marketing, and operations.

  • Service automation and its role in improving efficiency.

  • Sentiment analysis and customer service applications.

• AI-Driven Project Management

  • Artificial intelligence tools for project planning and execution.

  • Performance optimization and quality control through algorithms.

  • Evaluating return on investment in intelligent solutions.

• Roadmap for Adopting Artificial Intelligence Within the Organization

  • Steps to building an institutional AI strategy.

  • Defining priorities and developing human capabilities.

  • Integrating AI with existing systems without disruption.

• Ethical and Regulatory Considerations

  • AI governance and accountability frameworks.

  • Addressing privacy concerns and algorithmic bias.

  • Developing internal policies for safe and effective use of AI technologies.