Artificial intelligence (AI) - Application for evaluating the enterprise's digital transformation capacity
Keywords:
Machine learning, Digital transformation capabilities, EnterprisesAbstract
We are living in a period of digital transformation, in the 4.0 industrial revolution, the age of information is developing rapidly, so corporations and businesses can no longer exploit sustainably the competitive advantages if they only rely on the tangible assets of technology and the exploitation and mobilization of the intangible assets of technology are becoming increasingly more decisive factors. Digital transformation is an inevitable trend, contributing to promoting economic growth, improving labor productivity, competitiveness, production, and business efficiency, and lowering product costs, reducing administrative procedures, time, and costs. To evaluate the digital transformation capacity of businesses, traditional measures that are based on financial indicators are no longer strong enough and are not suitable to control and accurately control the performance of businesses in the new circumstances. Businesses need a new tool that can use artificial intelligence (AI) to provide a balanced view of all qualitative influencing factors and identify decisive capacity parameters in a more relevant and smarter manner. This article provides an overview of digital transformation perspectives and enterprise digital transformation capabilities, factors affecting digital transformation capabilities, and the application of language-based machine learning algorithms: ontology and fuzzy logic to evaluate the digital transformation capacity of enterprises.
Code: 24020101
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Bộ Kế hoạch và Đầu tư (2020). Sách trắng Doanh nghiệp Việt Nam 2020. <https://www.gso.gov.vn/wp-content/uploads/2020/04/Ruot-sach-trang-2020.pdf>.
Bộ Thông tin và Truyền thông (2023). Bộ chỉ số đánh giá mức độ chuyển đổi số của doanh nghiệp. <https://dbi.gov.vn/?AspxAutoDetectCookieSupport=1>
Nguyễn Thị Kim Ánh (2022). “Các nhân tố ảnh hưởng đến chuyển đổi số doanh nghiệp: Mô hình nghiên cứu và thang đo”. Tạp chí Tài chính doanh nghiệp, số 10/2022.
Ethem Alpaydın (2014). Introduction to Machine Learning, 3rd ed., The MIT Press, Cambridge, Massachusetts, London, England.
L.A Phuong, T.D.Khang, N.V.Trung (2015). “New Approach to Mining Fuzzy Association Rule with Linguistic Threshold Based on Hedge Algebras”. Proceedings of the 2nd International Workshop on Semantic Technologies.
Irene Solaiman, Zeerak Talat, et all (2023). “Evaluating the Social Impact of Generative AI Systems in Systems and Society”, in arXiv:2306.05949v1 [cs.CY] 9 Jun 2023. .
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