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Investigating Market and Regulatory Forces Shaping Artificial Intelligence Adoptions
Author(s) -
Chrissann R. Ruehle
Publication year - 2020
Publication title -
muma business review
Language(s) - English
Resource type - Journals
ISSN - 2640-6373
DOI - 10.28945/4644
Subject(s) - order (exchange) , misinformation , identification (biology) , analytics , marketing , business , market power , industrial organization , knowledge management , economics , data science , political science , computer science , market economy , botany , finance , law , biology , monopoly
Copyright © 2020, Chrissann Ruehle. This article is published under a Creative Commons BY-NC license. Permission is granted to copy and distribute this article for non-commercial purposes, in both printed and electronic formats The Artificial Intelligence (AI) industry has experienced tremendous growth in recent years. Consequently, there has been considerable interest in the media regarding this emergent technology. Practitioners and academics are interested in learning how this market functions to make evidence-based decisions regarding its adoption. The purpose of this manuscript is to perform a systematic examination of the current market dynamics as well as identify future growth opportunities for the benefit of incumbents in addition to firms seeking to enter the AI market. The primary research question is: how do market and governmental forces reportedly shape AI adoptions? Drawing on predominantly practitioner focused literature, along with several seminal academic sources, the article examines and maps stakeholders in the market using the AI Industry Stakeholder Power/Interest Matrix. This approach allows for the identification and analysis of key stakeholders as well as power and influence within the industry. Semiconductor and cloud computing firms play a substantive role in the industry as they wield substantial power, as revealed by this analysis. Utilizing the stakeholder analysis framework, this article elucidates the market structure, competitive and regulatory forces influencing AI adoption decisions within firms, and subsequent profitability.

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