About this Training
The training AI Business Manager provides participants with a proven methodological approach from idea generation to evaluation, technical and business-related feasibility, competence & team settings, data testing, and agile implementation methods. Using self-selected and company-individualized use cases, participants learn how to develop new data-driven business models in their own company to select the most promising use cases for implementing AI.
Why this Training is right for you
The AI Business Manager training is designed for professionals and executives who want to approach the topic of artificial intelligence from a business perspective, learn in-depth, field-tested approaches for implementing AI solutions, and gain hands-on experience with key agile AI implementation methodologies. The number of participants is limited to 20 persons so that an efficient knowledge transfer in the dialogue between the participants and lecturers is guaranteed.
- Delivering practice-relevant and applicable knowledge in technology management as well as for the systematic development of business model innovations.
- In-depth knowledge of methods and tools for the sustainable implementation of processes in your company.
- Reference to current problems of companies from practice.
- Projection of what you have learned to your own professional field or your own company.
- Gaining insights from industry best practice examples & fostering interdisciplinary knowledge exchange within the network.
Training Goals & Training Plan
After this training, you will be able to apply the field-tested methods of the AI Navigator and data-driven business model canvas in order to identify and evaluate economically logical AI use cases. In addition, you will receive an introduction to agile development methods, the importance of user experience in AI solutions, and the impact of AI on digital business models.
Within five days, the focus is on case studies and exercises in which the participants concretize and apply the theoretical & methodological basics of the AI Navigator. With the help of the field-tested AI Navigator, a structured approach is possible to identify AI-enabled projects and to guide them from idea generation to evaluation and data testing to successful implementation. In addition to lectures and practice-oriented exercises, there are numerous opportunities for professional and personal exchange as well as networking during joint evening events.
Day 1
- Introduction into Artificial Intelligence
- Data-Driven Business Model Canvas
- Ideation Phase (Theory & Practical Exercise)
- Jobs-to-be-done (Theory & Practical Exercise)
Day 2
- AI Teams: Competences & Roles
- Maturity Assessment (Theory & Practical Exercise)
- Data as Enabler for new Business
- Developing Data-Driven Business Models (Theory & Practical Exercise)
- Production related Specifics
Day 3
- Overview: Business & Technical Feasibility
- Understanding Machine Learning - Limits & Possibilities (Theory & Pracitcal Exercise)
- Data Testing
- Importance of Clearly Defined Processes
- Networking Event
Day 4
- Data Testing (Pracitcal Exercise)
- Technical Feasibility (Theory & Pracitcal Exercise)
- Technology Scouting - Approach for quick Solution Identification
- Standards & Data Governance
- Agile Project Management
Day 5
- Future Trends: Responsible & Explainable AI
- Best Practice: Data Management & Digital Transformation
- Business Model Stress-Test
- Exam
- Wrap-Up & Feedback
Customer Voices
"The training was an educational and intensive experience that provided valuable knowledge about the strategic use of AI in various industries. The content was extremely informative and helped to develop a deeper understanding of data-driven business models. The cross-industry exchange with other participants was particularly positive, offering new perspectives and inspiration."
Robert Kohlhaupt - Project Manager & BIM-Manager @ KVL Group
"The intensive training week with a lot of input gave a deep insight into the development of data-driven business models. I look forward to using the experience gained to promote data-driven decisions and strategies."
Frank Peterlein - Head of IT & AI @ KVL Group
Requesting Inhouse Training?
The course, or parts of it, would also be interesting for your entire team/your company? We would be happy to advise you on the possibility of an inhouse training. Give us a call or write to us - we will tailor your further training to suit your needs!
Learn more about
- AI Navigator
- Ideation – Use case browsing
- AI Use Case Assessment
- Agile Development
- Data-driven Business Models
- Technical feasibility
- Cross-functional Teams
- Best Practices
- AI Organization
Our Partners
Speakers
Anne Loos is a dedicated business leader in the manufacturing industry, passionately driving growth and innovation. With extensive expertise in advanced AI and digital transformation, coupled with over 14 years of hands-on experience in pioneering technologies, she implements strategies to sustainably strengthen companies' competitiveness. Through successfully diversifying international business, creating new business models, and crafting customer-centric strategies, she has established herself as a trusted partner. Her global impact extends to significant data-driven projects on a global scale.
Anne Loos
Toni Drescher is an experienced engineer and entrepreneur who has been involved in the implementation of technology-based innovations for the past 20 years. He gained practical experience in various leading management and consulting positions. He set up and commercialized new companies for several cutting-edge products and services and stands for the successful implementation of innovations. As the owner and CEO of the INC Innovation Center Group, he focuses on implementing and scaling innovations from idea to market launch to secure growth opportunities and competitiveness of large enterprises.
Toni Drescher
Tim Schroeder studied Electrical Engineering & Information Technology as well as Business Administration and General Management at RWTH Aachen University. As the Head of Artificial Intelligence at the INC Innovation Center, he led multiple bilateral and multilateral projects in the areas of Industry 4.0, Logistics 4.0, and Artificial Intelligence. Through technology scouting, data analysis, and AI assessments, he assists companies in successfully implementing new innovations.
Tim Schroeder
Dr. Olaf Enge-Rosenblatt studied automation engineering at Chemnitz University until 1986. He worked at the Institute for Mechatronics in Chemnitz, specializing in modeling mechatronic systems. Since 2005, he has been with Fraunhofer Institute in Dresden, initially as a research assistant. In 2007, he became a group leader, and in 2015, he took over the Data Analysis Systems group. Dr. Olaf Enge-Rosenblatt manages projects in condition monitoring, predictive maintenance, energy efficiency, big data, and AI. He also conducts training courses on predictive maintenance and AI implementation in production and maintenance since 2017.
Dr. Olaf Enge-Rosenblatt
Antonela Germek brings over 5 years of experience in leading and managing innovation projects across diverse industries. As the Head of Innovation Excellence at the INC Innovation Center, her primary responsibilities include developing, coordinating, and overseeing initiatives centered on methodical innovation management, new business development, and strategic alignment with current and emerging technologies. Supporting companies in identifying and systematically implementing the most suitable innovation approaches is just one aspect of her diverse focus.
