Training Course in Artificial Intelligence (AI) for Leaders and Managers

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.