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  • 學位論文

企業環境下人工智慧應用之評估與策略

Developing Strategies for the Successful Application of Artificial Intelligence in Corporate Environments

指導教授 : 黃明蕙
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摘要


Artificial intelligence as a research topic is prevalent, and interest from the corporate world is increasing, with organizations looking into how they can incorporate AI technology. However, applying AI within companies doesn’t go with much ease. Many AI projects tend to fail, either because they are terminated early or because they didn’t meet the expectations of the decision-makers. For that reason, researchers and organizations are asking themselves whether there are ways to prevent failure or improve the odds of AI projects. Research explores important antecedents, enablers, inhibitors, and AI capabilities. There is also much research on organizational learning, project, and organizational success. With all that, one question seems to remain, what if an AI project isn’t going well and an organization is facing adversity in its undertaking? With that question, this research steers in the direction of organizational resilience and, more precisely, adaptation, anticipation, and coping capabilities. The research focused on conceptualizing this research subject and the essential related variables to answer the main research question. A theoretical framework and a measurement scale were developed to hypothesize how adaptation, specific AI-anticipation and coping capabilities can help an organization face adversity during its AI projects. At the end of this research, the measurement tool is still unvalidated. But the implication of both the framework and measurement scale is that a new research field suddenly reveals itself, providing a better understanding of potential future research. Furthermore, it led to several suggestions and advice for organizations wanting to or deploying AI.

關鍵字

人工智慧 項目失敗 組織成功 適應 預期 應對

並列摘要


Artificial intelligence as a research topic is prevalent, and interest from the corporate world is increasing, with organizations looking into how they can incorporate AI technology. However, applying AI within companies doesn’t go with much ease. Many AI projects tend to fail, either because they are terminated early or because they didn’t meet the expectations of the decision-makers. For that reason, researchers and organizations are asking themselves whether there are ways to prevent failure or improve the odds of AI projects. Research explores important antecedents, enablers, inhibitors, and AI capabilities. There is also much research on organizational learning, project, and organizational success. With all that, one question seems to remain, what if an AI project isn’t going well and an organization is facing adversity in its undertaking? With that question, this research steers in the direction of organizational resilience and, more precisely, adaptation, anticipation, and coping capabilities. The research focused on conceptualizing this research subject and the essential related variables to answer the main research question. A theoretical framework and a measurement scale were developed to hypothesize how adaptation, specific AI-anticipation and coping capabilities can help an organization face adversity during its AI projects. At the end of this research, the measurement tool is still unvalidated. But the implication of both the framework and measurement scale is that a new research field suddenly reveals itself, providing a better understanding of potential future research. Furthermore, it led to several suggestions and advice for organizations wanting to or deploying AI.

參考文獻


Afiouni-Monla, R. (2019). Organizational Learning in the Rise of Machine Learning. ICIS 2019 Proceedings, 2. https://aisel.aisnet.org/icis2019/business_models/business_models/2
Al-Ahmad, W., Al-Fagih, K., Khanfar, K., Alsamara, K., Abuleil, S., Abu-Salem, H. (2009). A Taxonomy of an IT Project Failure: Root Causes. International Management Review, 5. https://www.researchgate.net/publication/282135018_A_Taxonomy_of_an_IT_Project_Failure_Root_Causes
Ammanath, B., Mittal, N., Saif, I., Anderson, S. (2021). Becoming an AI-fueled organization (Deloitte's State of AI in the Enterprise, Issue. https://www2.deloitte.com/content/dam/insights/articles/US144384_CIR-State-of-AI-4th-edition/DI_CIR-State-of-AI-4th-edition.pdf
Anyoha, R. (2017, 11 January). The History of Artificial Intelligence. https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/
Argote, L., Miron-Spektor, E. (2011). Organizational Learning: From Experience to Knowledge. Organization Science, 22(5), 1123-1137. https://doi.org/10.1287/orsc.1100.0621

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