AI-powered decision making enables companies to make more informed and faster decisions using data analytics and machine learning. In this workshop, participants learn how AI tools such as predictive analytics and data mining can be used to identify trends and patterns in large amounts of data. In this way, business decisions can be optimized and risks minimized. The application of AI in decision-making increases efficiency and accuracy and supports a strategic orientation of the company.

AI-powered decision making uses data-based analyses and algorithms to make well-founded, objective decisions. These technologies help companies identify patterns, minimize risks, and make more accurate strategic decisions.

Benefits for you as a company:

• Increasing decision accuracy through data-based analyses
• Improving the speed of response to market changes and trends
• Reducing errors and bias in decision making
• Optimizing resource allocation and business strategies
• Fostering a future-oriented, innovative corporate culture

1. Introduction to AI-powered decision making

• What does AI-based decision-making mean and how does it differ from traditional methods?
• Benefits of AI in Decision-Making Processes: Speed, Accuracy, and Data Analysis
• How companies are using AI-powered decision making to gain competitive advantage
• Overview of the different types of AI that support decision-making processes: machine learning, deep learning, natural language processing

2. Data as a basis for decision-making

• The role of data in AI-powered decision making
• How to collect, clean, and prepare the right data for decision making
• Structured vs. unstructured data: Where and how AI is used
• How AI quickly and precisely analyses large amounts of data to make informed decisions
• Practical examples: How companies use data to make strategic decisions

3. Machine learning in decision making

• What is machine learning (ML) and how is it used in decision making?
• Supervised vs. unsupervised learning: use cases in decision making
• How ML algorithms recognize patterns and trends that are important for strategic decisions
• Examples of machine learning in financial planning, marketing, human resource management, and production
• Practical exercises: Implementing machine learning techniques to analyze business data

4. Artificial intelligence in financial and risk management decisions

• How AI is being used in finance to assess risks and make strategic decisions
• AI tools to predict market movements, identify risks and opportunities
• Automated credit scoring and fraud detection using AI
• Example: Using AI in portfolio management to optimize investment strategies
• Practical application: Development of an AI model to predict financial risks

5. AI in personnel and resource planning

• How AI helps with personnel planning and decisions: Recruiting, employee retention and training needs
• Using AI to predict employee turnover and identify talent
• Using AI to optimize resource allocation and production planning
• How AI can personalize personnel management and improve the decision for individual development measures
• Practical examples: AI in use when creating shift plans or allocating resources in large projects

6. Ethical Considerations and Transparency in AI-Supported Decision-Making

• How do companies ensure that AI algorithms are fair, transparent and ethical?
• Challenges in Decision-Making with AI: Bias, Fairness, and Responsibility
• How bias in training data can distort decisions and how to address this problem
• The need for transparent AI decision-making and how to deal with potential AI mistakes
• Practical exercises to analyze and improve the ethical aspects of AI models

7. AI-powered real-time decision making

• How AI processes real-time data to make instant decisions
• Example: Real-Time Optimization in Retail, Production Processes, and Customer Experience
• How AI tools automatically make decisions and make quick, precise adjustments in real time
• Use cases for AI in logistics, customer service and advertising optimization
• Practical exercises for implementing real-time decision-making processes through AI

8. Decision-making with AI-powered business intelligence (BI) tools

• How BI tools powered by AI are taking data analysis and decision making to a new level
• Integrating AI with BI software such as Power BI, Tableau, Qlik: Automated dashboards and reports
• How business intelligence and AI work together to identify trends and patterns that are important for strategic decisions
• Example: Automated reporting and decision making based on real-time data
• Practical application: Using AI in BI tools to analyze business data and develop recommendations

9. AI-based forecasts for strategic decisions

• How AI helps companies predict future developments and make decisions based on them
• Using AI in demand forecasting, market analysis, and risk management
• How AI analyses historical data to make accurate predictions about future events
• Examples: AI in production planning, market trends and for personalized marketing strategies
• Practical exercises: Development of an AI model to forecast business trends and market developments

10. Integration and implementation of AI-based decision-making processes in companies

• How to integrate AI-based decision-making into existing business processes
• Steps to adopt AI technologies: choosing the right AI tools, team building, and resource planning
• Overcoming challenges in implementing AI: training, change management, data protection
• How companies can develop and implement a sustainable AI strategy
• Practical examples: Implementation of AI decision models in various areas of the company

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