TY - GEN

T1 - AI Implementation and Capability Development in Manufacturing: An Action Research Case

AU - Eklöf, Jon

AU - Snis, Ulrika Lundh

AU - Hamelryck, Thomas Wim

AU - Grima, Alexander

AU - Rønning, Ola

PY - 2024

Y1 - 2024

N2 - This action research article presents a case study of a global manufacturing company deploying artificial intelligence (AI) to develop capabilities and enhance decision-making. This study explores considerations and trade-offs involved in introducing AI into daily operations, leading up to the decision to develop AI capabilities in-house or outsource them. The case study offers in-depth technical descriptions of model selection, dataset creation, model adoption, model training and evaluation while addressing organizational obstacles and decision-making processes. The study’s findings highlight the importance of collaboration between technical experts, business leaders, and end-users, as well as the interaction and collaboration between AI systems and human employees in the workplace. The article contributes a practical perspective on AI implementation in manufacturing, emphasizing the need to balance in-house capability development with external acquisition. Although the case study company managed to create an in-house model, factors such as implementation, debugging, data requirements, training time, and performance led to outsourcing the capabilities. However, making this informed decision required capabilities and insights that were acquired through practical work. Consequently, although in-house development can be challenging, it can also enhance organizational capabilities and provide the necessary knowledge to make informed decisions about future development or outsourcing.

AB - This action research article presents a case study of a global manufacturing company deploying artificial intelligence (AI) to develop capabilities and enhance decision-making. This study explores considerations and trade-offs involved in introducing AI into daily operations, leading up to the decision to develop AI capabilities in-house or outsource them. The case study offers in-depth technical descriptions of model selection, dataset creation, model adoption, model training and evaluation while addressing organizational obstacles and decision-making processes. The study’s findings highlight the importance of collaboration between technical experts, business leaders, and end-users, as well as the interaction and collaboration between AI systems and human employees in the workplace. The article contributes a practical perspective on AI implementation in manufacturing, emphasizing the need to balance in-house capability development with external acquisition. Although the case study company managed to create an in-house model, factors such as implementation, debugging, data requirements, training time, and performance led to outsourcing the capabilities. However, making this informed decision required capabilities and insights that were acquired through practical work. Consequently, although in-house development can be challenging, it can also enhance organizational capabilities and provide the necessary knowledge to make informed decisions about future development or outsourcing.

M3 - Konferencebidrag i proceedings

BT - Proceedings of the 57th Hawaii International Conference on System Sciences - HICSS 2024

PB - Hawaii International Conference on System Sciences

T2 - 57th Hawaii International Conference on System Sciences - HICSS-57

Y2 - 3 January 2024 through 6 January 2024

ER -