Pilot Application 1: AI Business Models and Role Development<\/h2><\/div>Development of an AI-based business model that, in terms of work organization, is based on revised process flows and AI role concepts.<\/h4><\/div><\/div><\/div><\/div><\/div>Starting situation<\/h3><\/div><\/div><\/div>The use of AI at the interface to the customer is changing existing fields of activity. Auf der technischen Seite ist die Herausforderung, die Daten aus dem Feld mit Hilfe der KI f\u00fcr neue Gesch\u00e4ftsmodelle nutzbar zu machen. Dabei entstehen sowohl interne als auch externe Schnittstellen zwischen der KI und den klassischen Rollen des Kunden und der Servicemitarbeitenden, aber auch zwischen den KI-Entwickelnden und den Servicemitarbeitenden. In bestimmten Prozessen m\u00f6gen sogar die KI-Entwickelnden direkt mit den Kunden in Kontakt treten um spezifische Features der KI nach Kundenwunsch zu implementieren.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/humaine.info\/wp-content\/uploads\/2025\/04\/IMG_20250212_131309-scaled.jpg\" "\\"IMG_20250212_131309\\"")
<\/span><\/div><\/div><\/div>Procedure<\/h3><\/div><\/div><\/div><\/i>Part 1:
\nDevelopment of
\nAlgorithms<\/p><\/h4><\/div>
As part of the HUMAINE project, a multi-stage process model was developed and piloted for this purpose. It focuses on the targeted and cost-effective elicitation of domain expertise from the machine manufacturer\u2019s technical experts as well as the customer\u2019s process knowledge. It is also worth noting the continuous enrichment and refinement of the knowledge generated using AI and rule-based evaluation logic throughout the process model. Equally important is the continuous verification and adaptation of the algorithms, which requires close interaction between data scientists and domain experts that extends beyond the development phase.<\/p>\n<\/div><\/div><\/div>
<\/i>Part 2:
\nPiloting Digital Business Models<\/p><\/h4><\/div>
In the age of AI, traditional mechanical engineering companies in Germany face the challenge of adopting new business models and transforming themselves into digital service providers. Instead of merely selling machines, companies can now also offer data-driven services that enable predictive maintenance and optimized production processes. These services not only provide customers with added value but also create new revenue streams for the companies. However, developing and selecting a suitable business model poses a practical challenge for mechanical engineering companies. To address this issue, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. Following the principle of practical hypothesis testing, the focus is on the early piloting of various potential business model approaches.<\/p>\n<\/div><\/div><\/div>
<\/i>Part 3:
\nDigital Role Development in Customer Service<\/p><\/h4><\/div>
To achieve an employee-centered approach to role development, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. The focus is on the in-depth involvement of all affected employees in the detailed role development process. This enables employees to engage with their own roles at an early stage and, later on, to identify more strongly with them. Another key element is the identification and implementation of necessary actions by the employees themselves. The pilot was conducted at SEEPEX in the Customer Service department.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nLangholf, V., & Wilkens, U. (2024). Pathway to Work with AI: Testing the clAIr Role Development Method in an Industrial Work Environment. Zeitschrift f\u00fcr Arbeitswissenschaft. https:\/\/doi.org\/10.1007\/s41449-024-00435-4
\n <\/a><\/p>\nBarton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J.: Ideating Ethical AI Business Models<\/p>\n
Barton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J. (2026): Entwicklung ethischer KI-Gesch\u00e4ftsmodelle. In: I4S 1\/2026: Angewandte KI-Ethik am Arbeitsplatz. Eine gemeinsame Verantwortung \u2013 von der Radiologie und Sprachtherapie bis zur Montage <\/i>. Industry 4.0 Science.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Partners<\/h1><\/div><\/div><\/div>Practice Partner<\/h3><\/div><\/div><\/div>Research Partners<\/h3><\/div><\/div><\/div>
Development of an AI-based business model that, in terms of work organization, is based on revised process flows and AI role concepts.<\/h4><\/div><\/div><\/div><\/div><\/div>Starting situation<\/h3><\/div><\/div><\/div>The use of AI at the interface to the customer is changing existing fields of activity. Auf der technischen Seite ist die Herausforderung, die Daten aus dem Feld mit Hilfe der KI f\u00fcr neue Gesch\u00e4ftsmodelle nutzbar zu machen. Dabei entstehen sowohl interne als auch externe Schnittstellen zwischen der KI und den klassischen Rollen des Kunden und der Servicemitarbeitenden, aber auch zwischen den KI-Entwickelnden und den Servicemitarbeitenden. In bestimmten Prozessen m\u00f6gen sogar die KI-Entwickelnden direkt mit den Kunden in Kontakt treten um spezifische Features der KI nach Kundenwunsch zu implementieren.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Procedure<\/h3><\/div><\/div><\/div><\/i>Part 1:
\nDevelopment of
\nAlgorithms<\/p><\/h4><\/div>
As part of the HUMAINE project, a multi-stage process model was developed and piloted for this purpose. It focuses on the targeted and cost-effective elicitation of domain expertise from the machine manufacturer\u2019s technical experts as well as the customer\u2019s process knowledge. It is also worth noting the continuous enrichment and refinement of the knowledge generated using AI and rule-based evaluation logic throughout the process model. Equally important is the continuous verification and adaptation of the algorithms, which requires close interaction between data scientists and domain experts that extends beyond the development phase.<\/p>\n<\/div><\/div><\/div>
<\/i>Part 2:
\nPiloting Digital Business Models<\/p><\/h4><\/div>
In the age of AI, traditional mechanical engineering companies in Germany face the challenge of adopting new business models and transforming themselves into digital service providers. Instead of merely selling machines, companies can now also offer data-driven services that enable predictive maintenance and optimized production processes. These services not only provide customers with added value but also create new revenue streams for the companies. However, developing and selecting a suitable business model poses a practical challenge for mechanical engineering companies. To address this issue, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. Following the principle of practical hypothesis testing, the focus is on the early piloting of various potential business model approaches.<\/p>\n<\/div><\/div><\/div>
<\/i>Part 3:
\nDigital Role Development in Customer Service<\/p><\/h4><\/div>
To achieve an employee-centered approach to role development, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. The focus is on the in-depth involvement of all affected employees in the detailed role development process. This enables employees to engage with their own roles at an early stage and, later on, to identify more strongly with them. Another key element is the identification and implementation of necessary actions by the employees themselves. The pilot was conducted at SEEPEX in the Customer Service department.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nLangholf, V., & Wilkens, U. (2024). Pathway to Work with AI: Testing the clAIr Role Development Method in an Industrial Work Environment. Zeitschrift f\u00fcr Arbeitswissenschaft. https:\/\/doi.org\/10.1007\/s41449-024-00435-4
\n <\/a><\/p>\nBarton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J.: Ideating Ethical AI Business Models<\/p>\n
Barton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J. (2026): Entwicklung ethischer KI-Gesch\u00e4ftsmodelle. In: I4S 1\/2026: Angewandte KI-Ethik am Arbeitsplatz. Eine gemeinsame Verantwortung \u2013 von der Radiologie und Sprachtherapie bis zur Montage <\/i>. Industry 4.0 Science.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Partners<\/h1><\/div><\/div><\/div>Practice Partner<\/h3><\/div><\/div><\/div>Research Partners<\/h3><\/div><\/div><\/div>
Starting situation<\/h3><\/div><\/div><\/div>The use of AI at the interface to the customer is changing existing fields of activity. Auf der technischen Seite ist die Herausforderung, die Daten aus dem Feld mit Hilfe der KI f\u00fcr neue Gesch\u00e4ftsmodelle nutzbar zu machen. Dabei entstehen sowohl interne als auch externe Schnittstellen zwischen der KI und den klassischen Rollen des Kunden und der Servicemitarbeitenden, aber auch zwischen den KI-Entwickelnden und den Servicemitarbeitenden. In bestimmten Prozessen m\u00f6gen sogar die KI-Entwickelnden direkt mit den Kunden in Kontakt treten um spezifische Features der KI nach Kundenwunsch zu implementieren.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Procedure<\/h3><\/div><\/div><\/div><\/i>Part 1:
\nDevelopment of
\nAlgorithms<\/p><\/h4><\/div>
As part of the HUMAINE project, a multi-stage process model was developed and piloted for this purpose. It focuses on the targeted and cost-effective elicitation of domain expertise from the machine manufacturer\u2019s technical experts as well as the customer\u2019s process knowledge. It is also worth noting the continuous enrichment and refinement of the knowledge generated using AI and rule-based evaluation logic throughout the process model. Equally important is the continuous verification and adaptation of the algorithms, which requires close interaction between data scientists and domain experts that extends beyond the development phase.<\/p>\n<\/div><\/div><\/div>
<\/i>Part 2:
\nPiloting Digital Business Models<\/p><\/h4><\/div>
In the age of AI, traditional mechanical engineering companies in Germany face the challenge of adopting new business models and transforming themselves into digital service providers. Instead of merely selling machines, companies can now also offer data-driven services that enable predictive maintenance and optimized production processes. These services not only provide customers with added value but also create new revenue streams for the companies. However, developing and selecting a suitable business model poses a practical challenge for mechanical engineering companies. To address this issue, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. Following the principle of practical hypothesis testing, the focus is on the early piloting of various potential business model approaches.<\/p>\n<\/div><\/div><\/div>
<\/i>Part 3:
\nDigital Role Development in Customer Service<\/p><\/h4><\/div>
To achieve an employee-centered approach to role development, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. The focus is on the in-depth involvement of all affected employees in the detailed role development process. This enables employees to engage with their own roles at an early stage and, later on, to identify more strongly with them. Another key element is the identification and implementation of necessary actions by the employees themselves. The pilot was conducted at SEEPEX in the Customer Service department.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nLangholf, V., & Wilkens, U. (2024). Pathway to Work with AI: Testing the clAIr Role Development Method in an Industrial Work Environment. Zeitschrift f\u00fcr Arbeitswissenschaft. https:\/\/doi.org\/10.1007\/s41449-024-00435-4
\n <\/a><\/p>\nBarton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J.: Ideating Ethical AI Business Models<\/p>\n
Barton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J. (2026): Entwicklung ethischer KI-Gesch\u00e4ftsmodelle. In: I4S 1\/2026: Angewandte KI-Ethik am Arbeitsplatz. Eine gemeinsame Verantwortung \u2013 von der Radiologie und Sprachtherapie bis zur Montage <\/i>. Industry 4.0 Science.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Partners<\/h1><\/div><\/div><\/div>Practice Partner<\/h3><\/div><\/div><\/div>Research Partners<\/h3><\/div><\/div><\/div>
The use of AI at the interface to the customer is changing existing fields of activity. Auf der technischen Seite ist die Herausforderung, die Daten aus dem Feld mit Hilfe der KI f\u00fcr neue Gesch\u00e4ftsmodelle nutzbar zu machen. Dabei entstehen sowohl interne als auch externe Schnittstellen zwischen der KI und den klassischen Rollen des Kunden und der Servicemitarbeitenden, aber auch zwischen den KI-Entwickelnden und den Servicemitarbeitenden. In bestimmten Prozessen m\u00f6gen sogar die KI-Entwickelnden direkt mit den Kunden in Kontakt treten um spezifische Features der KI nach Kundenwunsch zu implementieren.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Procedure<\/h3><\/div><\/div><\/div><\/i>Part 1:
\nDevelopment of
\nAlgorithms<\/p><\/h4><\/div>
As part of the HUMAINE project, a multi-stage process model was developed and piloted for this purpose. It focuses on the targeted and cost-effective elicitation of domain expertise from the machine manufacturer\u2019s technical experts as well as the customer\u2019s process knowledge. It is also worth noting the continuous enrichment and refinement of the knowledge generated using AI and rule-based evaluation logic throughout the process model. Equally important is the continuous verification and adaptation of the algorithms, which requires close interaction between data scientists and domain experts that extends beyond the development phase.<\/p>\n<\/div><\/div><\/div>
<\/i>Part 2:
\nPiloting Digital Business Models<\/p><\/h4><\/div>
In the age of AI, traditional mechanical engineering companies in Germany face the challenge of adopting new business models and transforming themselves into digital service providers. Instead of merely selling machines, companies can now also offer data-driven services that enable predictive maintenance and optimized production processes. These services not only provide customers with added value but also create new revenue streams for the companies. However, developing and selecting a suitable business model poses a practical challenge for mechanical engineering companies. To address this issue, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. Following the principle of practical hypothesis testing, the focus is on the early piloting of various potential business model approaches.<\/p>\n<\/div><\/div><\/div>
<\/i>Part 3:
\nDigital Role Development in Customer Service<\/p><\/h4><\/div>
To achieve an employee-centered approach to role development, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. The focus is on the in-depth involvement of all affected employees in the detailed role development process. This enables employees to engage with their own roles at an early stage and, later on, to identify more strongly with them. Another key element is the identification and implementation of necessary actions by the employees themselves. The pilot was conducted at SEEPEX in the Customer Service department.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nLangholf, V., & Wilkens, U. (2024). Pathway to Work with AI: Testing the clAIr Role Development Method in an Industrial Work Environment. Zeitschrift f\u00fcr Arbeitswissenschaft. https:\/\/doi.org\/10.1007\/s41449-024-00435-4
\n <\/a><\/p>\nBarton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J.: Ideating Ethical AI Business Models<\/p>\n
Barton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J. (2026): Entwicklung ethischer KI-Gesch\u00e4ftsmodelle. In: I4S 1\/2026: Angewandte KI-Ethik am Arbeitsplatz. Eine gemeinsame Verantwortung \u2013 von der Radiologie und Sprachtherapie bis zur Montage <\/i>. Industry 4.0 Science.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Partners<\/h1><\/div><\/div><\/div>Practice Partner<\/h3><\/div><\/div><\/div>Research Partners<\/h3><\/div><\/div><\/div>
Part 1:
\nDevelopment of
\nAlgorithms<\/p><\/h4><\/div>
As part of the HUMAINE project, a multi-stage process model was developed and piloted for this purpose. It focuses on the targeted and cost-effective elicitation of domain expertise from the machine manufacturer\u2019s technical experts as well as the customer\u2019s process knowledge. It is also worth noting the continuous enrichment and refinement of the knowledge generated using AI and rule-based evaluation logic throughout the process model. Equally important is the continuous verification and adaptation of the algorithms, which requires close interaction between data scientists and domain experts that extends beyond the development phase.<\/p>\n<\/div><\/div><\/div>
Part 2:
\nPiloting Digital Business Models<\/p><\/h4><\/div>
In the age of AI, traditional mechanical engineering companies in Germany face the challenge of adopting new business models and transforming themselves into digital service providers. Instead of merely selling machines, companies can now also offer data-driven services that enable predictive maintenance and optimized production processes. These services not only provide customers with added value but also create new revenue streams for the companies. However, developing and selecting a suitable business model poses a practical challenge for mechanical engineering companies. To address this issue, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. Following the principle of practical hypothesis testing, the focus is on the early piloting of various potential business model approaches.<\/p>\n<\/div><\/div><\/div>
Part 3:
\nDigital Role Development in Customer Service<\/p><\/h4><\/div>
To achieve an employee-centered approach to role development, a roadmap was developed and piloted as a blueprint as part of the HUMAINE project. The focus is on the in-depth involvement of all affected employees in the detailed role development process. This enables employees to engage with their own roles at an early stage and, later on, to identify more strongly with them. Another key element is the identification and implementation of necessary actions by the employees themselves. The pilot was conducted at SEEPEX in the Customer Service department.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nLangholf, V., & Wilkens, U. (2024). Pathway to Work with AI: Testing the clAIr Role Development Method in an Industrial Work Environment. Zeitschrift f\u00fcr Arbeitswissenschaft. https:\/\/doi.org\/10.1007\/s41449-024-00435-4
\n <\/a><\/p>\nBarton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J.: Ideating Ethical AI Business Models<\/p>\n
Barton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J. (2026): Entwicklung ethischer KI-Gesch\u00e4ftsmodelle. In: I4S 1\/2026: Angewandte KI-Ethik am Arbeitsplatz. Eine gemeinsame Verantwortung \u2013 von der Radiologie und Sprachtherapie bis zur Montage <\/i>. Industry 4.0 Science.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Partners<\/h1><\/div><\/div><\/div>Practice Partner<\/h3><\/div><\/div><\/div>Research Partners<\/h3><\/div><\/div><\/div>
Langholf, V., & Wilkens, U. (2024). Pathway to Work with AI: Testing the clAIr Role Development Method in an Industrial Work Environment. Zeitschrift f\u00fcr Arbeitswissenschaft. https:\/\/doi.org\/10.1007\/s41449-024-00435-4 Barton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J.: Ideating Ethical AI Business Models<\/p>\n Barton, M.-C.; Skrzyppek, L.; Nauth, K.; Mazarov, J.; Poeppelbuss, J. (2026): Entwicklung ethischer KI-Gesch\u00e4ftsmodelle. In: I4S 1\/2026: Angewandte KI-Ethik am Arbeitsplatz. Eine gemeinsame Verantwortung \u2013 von der Radiologie und Sprachtherapie bis zur Montage <\/i>. Industry 4.0 Science.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
\n <\/a><\/p>\nPartners<\/h1><\/div><\/div><\/div>
Practice Partner<\/h3><\/div><\/div><\/div>
Research Partners<\/h3><\/div><\/div><\/div>
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