Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
In this paper, we examine Proof-of-Concept activities in the presence of Artificial Intelligence (AI). To this end, we conducted an exploratory, revelatory case study at a leading automotive OEM that constantly explores new technologies to improve its manufacturing processes. We highlight how AI properties affect specifics in project execution and how they are addressed within the focal company. We carved out four key areas affecting underlying activities, i.e., data assessment, process alignment, value orientation, and AI empowerment. With our findings, we provide practical insights into AI-related challenges and corresponding pathways for action. Drawn upon, we develop novel, timely, and actionable recommendations for AI project leaders planning to implement this novel technology in manufacturing. This shall provide empirically grounded and conceptually sound guidance for researchers and practitioners alike, and ultimately foster the success of AI in manufacturing.
Becoming Certain About the Uncertain: How AI Changes Proof-of-Concept Activities in Manufacturing – Insights from a Global Automotive Leader
Online
In this paper, we examine Proof-of-Concept activities in the presence of Artificial Intelligence (AI). To this end, we conducted an exploratory, revelatory case study at a leading automotive OEM that constantly explores new technologies to improve its manufacturing processes. We highlight how AI properties affect specifics in project execution and how they are addressed within the focal company. We carved out four key areas affecting underlying activities, i.e., data assessment, process alignment, value orientation, and AI empowerment. With our findings, we provide practical insights into AI-related challenges and corresponding pathways for action. Drawn upon, we develop novel, timely, and actionable recommendations for AI project leaders planning to implement this novel technology in manufacturing. This shall provide empirically grounded and conceptually sound guidance for researchers and practitioners alike, and ultimately foster the success of AI in manufacturing.
https://aisel.aisnet.org/hicss-55/os/practice-based_research/3