{"title":"在商业企业中实施人工智能的框架:准备模型","authors":"M. Nortje, S. Grobbelaar","doi":"10.1109/ICE/ITMC49519.2020.9198436","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence (AI) will have transformational impact on businesses. However, the largest opportunities are still to be capitalized. Businesses that are aiming towards implementing AI into their business structure or providing AI services are facing a range of challenges to implement artificial intelligence into their enterprises. As businesses are changing and transforming their business models and processes to capitalize on the advantages of AI, the congestion/bottleneck is in business imagination, management and most importantly, the implementation of AI. This paper aims to identify the range of dimensions and elements for a readiness model framework to assist in the implementation of AI into a business' structures. The development the model framework rests on the completion of two systematic literature reviews. Through this process readiness elements could be mapped into readiness dimensions. This article presents the development of the initial readiness elements and their respective variables which can be categorized as 1) Employee and culture 2) Technology management 3) Organizational governance and leadership 4) Strategy 5) Infrastructure 6) Knowledge and information 7) Security. Through the identification of these readiness dimensions and related elements, an AI-readiness index can in future be developed.","PeriodicalId":269465,"journal":{"name":"2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)","volume":"359-360 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A Framework for the Implementation of Artificial Intelligence in Business Enterprises: A Readiness Model\",\"authors\":\"M. Nortje, S. Grobbelaar\",\"doi\":\"10.1109/ICE/ITMC49519.2020.9198436\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial Intelligence (AI) will have transformational impact on businesses. However, the largest opportunities are still to be capitalized. Businesses that are aiming towards implementing AI into their business structure or providing AI services are facing a range of challenges to implement artificial intelligence into their enterprises. As businesses are changing and transforming their business models and processes to capitalize on the advantages of AI, the congestion/bottleneck is in business imagination, management and most importantly, the implementation of AI. This paper aims to identify the range of dimensions and elements for a readiness model framework to assist in the implementation of AI into a business' structures. The development the model framework rests on the completion of two systematic literature reviews. Through this process readiness elements could be mapped into readiness dimensions. This article presents the development of the initial readiness elements and their respective variables which can be categorized as 1) Employee and culture 2) Technology management 3) Organizational governance and leadership 4) Strategy 5) Infrastructure 6) Knowledge and information 7) Security. Through the identification of these readiness dimensions and related elements, an AI-readiness index can in future be developed.\",\"PeriodicalId\":269465,\"journal\":{\"name\":\"2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)\",\"volume\":\"359-360 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICE/ITMC49519.2020.9198436\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICE/ITMC49519.2020.9198436","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Framework for the Implementation of Artificial Intelligence in Business Enterprises: A Readiness Model
Artificial Intelligence (AI) will have transformational impact on businesses. However, the largest opportunities are still to be capitalized. Businesses that are aiming towards implementing AI into their business structure or providing AI services are facing a range of challenges to implement artificial intelligence into their enterprises. As businesses are changing and transforming their business models and processes to capitalize on the advantages of AI, the congestion/bottleneck is in business imagination, management and most importantly, the implementation of AI. This paper aims to identify the range of dimensions and elements for a readiness model framework to assist in the implementation of AI into a business' structures. The development the model framework rests on the completion of two systematic literature reviews. Through this process readiness elements could be mapped into readiness dimensions. This article presents the development of the initial readiness elements and their respective variables which can be categorized as 1) Employee and culture 2) Technology management 3) Organizational governance and leadership 4) Strategy 5) Infrastructure 6) Knowledge and information 7) Security. Through the identification of these readiness dimensions and related elements, an AI-readiness index can in future be developed.