Deploying AI Applications to Multiple Environments: Coping with Environmental, Data, and Predictive Variety Short Paper
Type
conference paper
Date Issued
2022
Author(s)
Abstract
Deploying Artificial Intelligence (AI) proves to be challenging and resource-intensive in practice. To increase the economic value of AI deployments, organizations seek to deploy and reuse AI applications in multiple environments (e.g., different firm branches). This process involves generalizing an existing AI application to a new environment, which is typically not seamlessly possible. Despite its practical relevance, research lacks a thorough understanding of how organizations approach the deployment of AI applications to multiple environments. Therefore, we conduct an explorative multiplecase study with four computer vision projects as part of an ongoing research effort. Our preliminary findings suggest that new environments introduce variety, which is mirrored in the data produced in these environments and the required predictive capabilities. Organizations are found to cope with variety during AI deployment by 1) controlling variety in the environment, 2) capturing variety via data collection, and 3) adapting to variety by adjusting AI models.
Language
English (United States)
Keywords
Artificial intelligence
machine learning
development
deployment
HSG Classification
contribution to scientific community
Refereed
Yes
Event Title
International Conference on Information Systems
Event Location
Copenhagen, Denmark
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
Weber et al._2022 ICIS.pdf
Size
338.29 KB
Format
Adobe PDF
Checksum (MD5)
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