Digitalization and the Innovation of Artificial Intelligence: A Systematic Review

Crişan Georgiana-Alina (), Suciu Andrei-Alexandru (), Domenteanu Adrian () and Popescu Mădălina Ecaterina ()
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Crişan Georgiana-Alina: Bucharest University of Economic Studies, Bucharest, Romania
Suciu Andrei-Alexandru: Bucharest University of Economic Studies, Bucharest, Romania
Domenteanu Adrian: Bucharest University of Economic Studies, Bucharest, Romania
Popescu Mădălina Ecaterina: Bucharest University of Economic Studies, Bucharest, Romania The Romanian National Scientific Research Institute for Labour and Social Protection

Proceedings of the International Conference on Business Excellence, 2025, vol. 19, issue 1, 2740-2754

Abstract: The implementation of new technologies comes with the use of digitalization and Artificial Intelligence (AI) as core drivers. AI, including machine learning and deep learning, thrives in digitally mature environments with robust infrastructures and vast data availability With respect to AI innovations, digitalization is equally important because it provides the necessary computation power, data frameworks, and even the network systems needed to spearhead innovations. However, global disparities exist, with the United States and China excelling in AI commercialization, while Europe leads in academic research but struggles with industry adoption due to fragmented policies and inconsistent investment in AI-driven enterprises. This study conducts a systematic bibliometric analysis to examine the relationship between digitalization and AI research productivity. Using data from the Web of Science Core Collection, bibliometric techniques such as keyword co-occurrence analysis, citation network mapping, and thematic clustering were applied to assess AI research trends and their connection to digitalization. Responding to the outlining issue: How has digitalization influenced AI research evolution, and what are the key bibliometric trends? Findings confirm that digitally advanced regions lead in AI research output, while Europe faces commercialization challenges due to regulatory constraints and weak industry-academia collaboration. This study offers a novel contribution by integrating bibliometric analysis with policy insights to examine AI commercialization challenges, a dimension that has received limited attention in prior systematic reviews. This analysis points to insufficient academic activity and industrial adoption and calls for collaboration, dedicated policies for AI, and infrastructure investments. This helps policymakers focused on AI innovation as well as those in charge of AI governance and business leaders aiming to improve industry competition and commercialization.

Keywords: Artificial Intelligence; Digitalization; Bibliometric Analysis; Bibliometrix; Digital Transformation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:19:y:2025:i:1:p:2740-2754:n:1021

DOI: 10.2478/picbe-2025-0211

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