Course Overview
After completing this course, you will be able to use advanced AI technologies strategically in your company and realize sustainable benefits. You will learn to understand and effectively use complex AI models, master data management and quality assurance professionally and recognize and eliminate bias in models.
You will also be well prepared to scale AI projects, develop governance guidelines and address ethical challenges. You will develop a sound strategy for the long-term use of AI and learn how to successfully integrate AI into the corporate culture.
This course lays the foundation for making your company future-proof and competitive.
Who should attend
- Managers and decision-makers who want to use AI strategically in their company to secure long-term competitive advantages.
- Project managers and innovation managers who want to plan, implement and scale AI projects to advance their organization.
- IT and data experts who want to understand advanced technologies such as reinforcement learning and generative AI and integrate them into existing systems.
- Change managers and HR managers who want to support the introduction of AI into the corporate culture and actively involve employees.
- Entrepreneurs and strategy managers who want to understand the ethical, technical and organizational aspects of AI and make informed decisions.
- The course is ideal for anyone who wants to deepen their knowledge of AI, experience practical applications and develop sustainable strategies for the use of AI.
Prerequisites
The Artificial Intelligence (AI) Foundation (KIF) is a prerequisite for participation in the training.
Course Objectives
After the advanced course Strategic AI application and optimization
you will be able to
- Apply advanced AI technologies: Understand concepts such as reinforcement learning and generative AI and be able to apply them specifically to business challenges.
- Manage data efficiently: You are proficient in methods for data collection, cleansing and annotation as well as quality assurance of AI models.
- Recognize and eliminate bias in AI models: You know how to develop and implement fair and transparent AI solutions.
- Plan and scale strategic AI projects: You can develop a long-term AI strategy, successfully expand pilot projects and use suitable platforms and tools.
- Address ethical and governance aspects: Create governance policies, resolve ethical challenges and promote responsible use of AI in your organization.
- Integrate AI into the corporate culture: You have mastered change management methods and know how to train and involve employees and communicate the use of AI in your organization.
- Solve practical problems with AI: Through interactive case studies and group work, you will have the ability to tackle real-world challenges using AI-powered approaches.
- This course enables you to use AI strategically and sustainably in your company to drive innovation and secure competitive advantages.
Course Content
Welcome and objectives
- Presentation of the agenda and course objectives
- Clarification of participants' expectations and specific questions
In-depth study: Machine learning and deep learning in practice
- Advanced concepts: reinforcement learning and transfer learning
- Case studies: Optimization of supply chains, predictive maintenance
- Demo: Training pipeline of a machine learning model
- Discussion: Possible applications and challenges
Data management and AI quality assurance
- Data preparation: data collection, cleansing and annotation
- Dealing with bias in AI models
- Quality assurance: model validation and continuous learning
- Exercise: Identification and correction of bias in training data
Generative AI and its applications
- Advanced use of generative AI: content creation, prototyping
- Case study: AI-supported product innovation
- Exercise: Creating a content prototype with generative AI
AI strategies for companies
- Development of a long-term AI strategy
- Exercise: Creation of a strategic AI plan for an example company
- Discussion: Success factors and risks
Scaling of AI projects
- From pilot projects to large-scale implementation
- Platforms and tools for scaling (e.g. cloud services)
- Exercise: Creating a scaling plan for an AI project
AI ethics and governance
- Development of governance guidelines for AI
- Ethical challenges: Transparency, fairness, responsibility
- Exercise: Analyzing an ethical dilemma in the use of AI
Integration of AI into the corporate culture
- Change management: training and involving employees
- Communication strategies for AI projects
- Exercise: Creating a communication plan for an AI change project
Case studies and interactive group work
- Analysis of real AI projects
- Group work: Solving an AI-specific problem for a company
Conclusion, reflection and farewell
- Summary of the course
- Feedback round and open questions
- Handover of the certificates