After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

After the presentation and an introduction to the learning environment, the participants and the:der trainer will immerse themselves in scenarios that show the challenges involved in operationalizing AI projects.

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• AI in real use: examples from various industries
• Influence of AI on innovation and competitiveness
• Correcting misconceptions about AI
• Key elements of successful AI projects
• Case study analysis: What makes an AI project successful?
• Best practices: learning from the successes of others
• AI as part of the corporate vision
• Integration of AI into existing business strategies

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

• Competencies and roles in productive AI teams
• Team dynamics and collaboration in AI projects
• Executives and managers in AI projects
• Step by step to a customized AI strategy
• Flexibility and adaptability of the AI strategy
• Guidelines and standards for the use of AI
• The COSIMA principle - a guide for your AI strategy
• Measuring progress and monitoring KPIs
• Continuous improvement with Build-Measure-Learn
• Risk management and crisis management in AI projects

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

After reflecting on the content from the first self-learning phases, the expert goes through the entire process of AI strategy development together with the learning group and provides an outlook on the challenges of implementation.

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

• Introduction to AI technologies and tools
• AI platforms and frameworks in comparison
• The guide for AI technology decisions
• Overview of leading LLMs and their providers
• Application examples and areas of use for LLMs
• Integration of LLMs into existing AI platforms
• The most important foundation: effective data management

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In this self-study unit, trainer Björn Rosenthal describes in detail his practical experience, including his current projects for the DFL (German Football League) and other projects for large companies. 

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.

In the final live webinar, final questions are first clarified and the content from the self-study phases is reflected on together. The learning group then goes through the entire process of executing an AI strategy under the guidance of the:die trainer and is presented with special challenges.