After the presentation and an introduction to the course and master class process, the participants collect initial scenarios for the use of AI from their own contexts. These are categorized together into the different variants of artificial intelligence and can be used as examples throughout the course. 

After the presentation and an introduction to the course and master class process, the participants collect initial scenarios for the use of AI from their own contexts. These are categorized together into the different variants of artificial intelligence and can be used as examples throughout the course. 

After the presentation and an introduction to the course and master class process, the participants collect initial scenarios for the use of AI from their own contexts. These are categorized together into the different variants of artificial intelligence and can be used as examples throughout the course. 

After the presentation and an introduction to the course and master class process, the participants collect initial scenarios for the use of AI from their own contexts. These are categorized together into the different variants of artificial intelligence and can be used as examples throughout the course. 

After the presentation and an introduction to the course and master class process, the participants collect initial scenarios for the use of AI from their own contexts. These are categorized together into the different variants of artificial intelligence and can be used as examples throughout the course. 

• What is artificial intelligence?
• AI as the most important innovation for digital change
• Relevance: What AI means for companies
• Perspectives: What AI will achieve
• Prerequisite: data and trust
• Use data sources outside the organization
• Effects: How AI is changing the rules
• The future: what's next for AI

• What is artificial intelligence?
• AI as the most important innovation for digital change
• Relevance: What AI means for companies
• Perspectives: What AI will achieve
• Prerequisite: data and trust
• Use data sources outside the organization
• Effects: How AI is changing the rules
• The future: what's next for AI

• What is artificial intelligence?
• AI as the most important innovation for digital change
• Relevance: What AI means for companies
• Perspectives: What AI will achieve
• Prerequisite: data and trust
• Use data sources outside the organization
• Effects: How AI is changing the rules
• The future: what's next for AI

• What is artificial intelligence?
• AI as the most important innovation for digital change
• Relevance: What AI means for companies
• Perspectives: What AI will achieve
• Prerequisite: data and trust
• Use data sources outside the organization
• Effects: How AI is changing the rules
• The future: what's next for AI

• What is artificial intelligence?
• AI as the most important innovation for digital change
• Relevance: What AI means for companies
• Perspectives: What AI will achieve
• Prerequisite: data and trust
• Use data sources outside the organization
• Effects: How AI is changing the rules
• The future: what's next for AI

• From the beginnings to today's LLMs
• Principles of human learning
• Learning methods in machine learning
• Artificial neural networks (KNN)
• Deep learning explained in a nutshell
• Residual Networks (ResNet)
• Convolutional Neural Networks (CNN)
• Generative Adversarial Networks (GAN)
• Recurrent Neural Networks (RNN)

• From the beginnings to today's LLMs
• Principles of human learning
• Learning methods in machine learning
• Artificial neural networks (KNN)
• Deep learning explained in a nutshell
• Residual Networks (ResNet)
• Convolutional Neural Networks (CNN)
• Generative Adversarial Networks (GAN)
• Recurrent Neural Networks (RNN)

• From the beginnings to today's LLMs
• Principles of human learning
• Learning methods in machine learning
• Artificial neural networks (KNN)
• Deep learning explained in a nutshell
• Residual Networks (ResNet)
• Convolutional Neural Networks (CNN)
• Generative Adversarial Networks (GAN)
• Recurrent Neural Networks (RNN)

• From the beginnings to today's LLMs
• Principles of human learning
• Learning methods in machine learning
• Artificial neural networks (KNN)
• Deep learning explained in a nutshell
• Residual Networks (ResNet)
• Convolutional Neural Networks (CNN)
• Generative Adversarial Networks (GAN)
• Recurrent Neural Networks (RNN)

• From the beginnings to today's LLMs
• Principles of human learning
• Learning methods in machine learning
• Artificial neural networks (KNN)
• Deep learning explained in a nutshell
• Residual Networks (ResNet)
• Convolutional Neural Networks (CNN)
• Generative Adversarial Networks (GAN)
• Recurrent Neural Networks (RNN)

• Emotional and cognitive value contributions through AI
• The relationship between humans and AI
• The AI periodic system for companies
• Group 1: "Assess"
• Group 2: "Infer"
• Group 3: "Respond"

• Emotional and cognitive value contributions through AI
• The relationship between humans and AI
• The AI periodic system for companies
• Group 1: "Assess"
• Group 2: "Infer"
• Group 3: "Respond"

• Emotional and cognitive value contributions through AI
• The relationship between humans and AI
• The AI periodic system for companies
• Group 1: "Assess"
• Group 2: "Infer"
• Group 3: "Respond"

• Emotional and cognitive value contributions through AI
• The relationship between humans and AI
• The AI periodic system for companies
• Group 1: "Assess"
• Group 2: "Infer"
• Group 3: "Respond"

• Emotional and cognitive value contributions through AI
• The relationship between humans and AI
• The AI periodic system for companies
• Group 1: "Assess"
• Group 2: "Infer"
• Group 3: "Respond"

After reflecting on the content from the first self-study phases, the focus is on the concrete application of AI in a business context. Using the AI Project Canvas, participants get to know a tool that they can use to describe their AI project in a structured and methodical way. The AI Project Canvas is a proven methodology for starting a new AI project, e.g. to get involved in stakeholders.

After reflecting on the content from the first self-study phases, the focus is on the concrete application of AI in a business context. Using the AI Project Canvas, participants get to know a tool that they can use to describe their AI project in a structured and methodical way. The AI Project Canvas is a proven methodology for starting a new AI project, e.g. to get involved in stakeholders.

After reflecting on the content from the first self-study phases, the focus is on the concrete application of AI in a business context. Using the AI Project Canvas, participants get to know a tool that they can use to describe their AI project in a structured and methodical way. The AI Project Canvas is a proven methodology for starting a new AI project, e.g. to get involved in stakeholders.

After reflecting on the content from the first self-study phases, the focus is on the concrete application of AI in a business context. Using the AI Project Canvas, participants get to know a tool that they can use to describe their AI project in a structured and methodical way. The AI Project Canvas is a proven methodology for starting a new AI project, e.g. to get involved in stakeholders.

After reflecting on the content from the first self-study phases, the focus is on the concrete application of AI in a business context. Using the AI Project Canvas, participants get to know a tool that they can use to describe their AI project in a structured and methodical way. The AI Project Canvas is a proven methodology for starting a new AI project, e.g. to get involved in stakeholders.

• Use of AI in various areas of the company 
• Four fields of action for management 
• Developing fields of application with the AI Project Canvas 
• Understanding fields of application by example: 20 AI hacks 
• creative services with AI 
• Simplification and assistance through AI 
• Using AI for analyses 
• AI helps to avoid errors 
• Using AI for forecasts 
• Productive fields of application for AI 
• How do employees benefit from AI?

• Use of AI in various areas of the company 
• Four fields of action for management 
• Developing fields of application with the AI Project Canvas 
• Understanding fields of application by example: 20 AI hacks 
• creative services with AI 
• Simplification and assistance through AI 
• Using AI for analyses 
• AI helps to avoid errors 
• Using AI for forecasts 
• Productive fields of application for AI 
• How do employees benefit from AI?

• Use of AI in various areas of the company 
• Four fields of action for management 
• Developing fields of application with the AI Project Canvas 
• Understanding fields of application by example: 20 AI hacks 
• creative services with AI 
• Simplification and assistance through AI 
• Using AI for analyses 
• AI helps to avoid errors 
• Using AI for forecasts 
• Productive fields of application for AI 
• How do employees benefit from AI?

• Use of AI in various areas of the company 
• Four fields of action for management 
• Developing fields of application with the AI Project Canvas 
• Understanding fields of application by example: 20 AI hacks 
• creative services with AI 
• Simplification and assistance through AI 
• Using AI for analyses 
• AI helps to avoid errors 
• Using AI for forecasts 
• Productive fields of application for AI 
• How do employees benefit from AI?

• Use of AI in various areas of the company 
• Four fields of action for management 
• Developing fields of application with the AI Project Canvas 
• Understanding fields of application by example: 20 AI hacks 
• creative services with AI 
• Simplification and assistance through AI 
• Using AI for analyses 
• AI helps to avoid errors 
• Using AI for forecasts 
• Productive fields of application for AI 
• How do employees benefit from AI?

• Challenges in AI projects 
• Why AI projects fail 
• Special features of AI projects 
• The AI trend radar: application and how it works 
• The AI trend radar for financial service providers 
• The AI trend radar for insurance companies 
• The AI trend radar for healthcare companies 
• Create the AI trend radar for your own industry 
• Key learnings for the AI maturity path

• Challenges in AI projects 
• Why AI projects fail 
• Special features of AI projects 
• The AI trend radar: application and how it works 
• The AI trend radar for financial service providers 
• The AI trend radar for insurance companies 
• The AI trend radar for healthcare companies 
• Create the AI trend radar for your own industry 
• Key learnings for the AI maturity path

• Challenges in AI projects 
• Why AI projects fail 
• Special features of AI projects 
• The AI trend radar: application and how it works 
• The AI trend radar for financial service providers 
• The AI trend radar for insurance companies 
• The AI trend radar for healthcare companies 
• Create the AI trend radar for your own industry 
• Key learnings for the AI maturity path

• Challenges in AI projects 
• Why AI projects fail 
• Special features of AI projects 
• The AI trend radar: application and how it works 
• The AI trend radar for financial service providers 
• The AI trend radar for insurance companies 
• The AI trend radar for healthcare companies 
• Create the AI trend radar for your own industry 
• Key learnings for the AI maturity path

• Challenges in AI projects 
• Why AI projects fail 
• Special features of AI projects 
• The AI trend radar: application and how it works 
• The AI trend radar for financial service providers 
• The AI trend radar for insurance companies 
• The AI trend radar for healthcare companies 
• Create the AI trend radar for your own industry 
• Key learnings for the AI maturity path

In the final live webinar - after reflecting on the self-study unit and answering open questions - the prepared AI Project Canvas is presented and discussed. The feedback from the participants and the:der trainer prepares the participants intensively for a possible implementation of their individual AI project.

In the final live webinar - after reflecting on the self-study unit and answering open questions - the prepared AI Project Canvas is presented and discussed. The feedback from the participants and the:der trainer prepares the participants intensively for a possible implementation of their individual AI project.

In the final live webinar - after reflecting on the self-study unit and answering open questions - the prepared AI Project Canvas is presented and discussed. The feedback from the participants and the:der trainer prepares the participants intensively for a possible implementation of their individual AI project.

In the final live webinar - after reflecting on the self-study unit and answering open questions - the prepared AI Project Canvas is presented and discussed. The feedback from the participants and the:der trainer prepares the participants intensively for a possible implementation of their individual AI project.

In the final live webinar - after reflecting on the self-study unit and answering open questions - the prepared AI Project Canvas is presented and discussed. The feedback from the participants and the:der trainer prepares the participants intensively for a possible implementation of their individual AI project.