Artificial Intelligence Software Adoption in Manufacturing Companies

This study investigates the adoption of artificial intelligence (AI) software in manufacturing companies in Slovenia, Slovakia and Croatia, and across six production areas. This research ad-dresses a gap in the literature regarding AI software implementation in relation to company size, technology intensity and supply chain role, and examines whether Industry 4.0 (I4.0) readiness influences AI adoption. Data from the European Manufacturing Survey 2022 were analyzed, and showed that the use of AI is still relatively low. On average only 18.4% of companies use AI software in at least one production area. Logistic regression analysis revealed that neither company size nor role in the supply chain or technology intensity are statistically significantly related to AI usage. However, a significant positive relationship was found between I4.0 readiness and AI adoption, suggesting that companies with advanced digital infrastructures and integrated cyber-physical systems are more likely to adopt AI. This finding underlines the importance of digital transformation for the integration of AI software. The study concludes that while company characteristics such as size and the role of the company in the supply chain are not statistically significantly related to the use of AI, the level of digital readiness is crucial.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2024-08-08
DOI
https://doi.org/10.3390/app14166959
Citations
15
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
5.23

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence Software Adoption in Manufacturing Companies

Klemen Kovič, Iztok Palčić, Jasna Prester, Polona Tominc
15 citations
Applied Sciences
Digital Transformation in Industry
5.23
article

Artificial Intelligence Software Adoption in Manufacturing Companies

Klemen Kovič, Iztok Palčić, Jasna Prester, Polona Tominc
article en
15 citations

Abstract

This study investigates the adoption of artificial intelligence (AI) software in manufacturing companies in Slovenia, Slovakia and Croatia, and across six production areas. This research ad-dresses a gap in the literature regarding AI software implementation in relation to company size, technology intensity and supply chain role, and examines whether Industry 4.0 (I4.0) readiness influences AI adoption. Data from the European Manufacturing Survey 2022 were analyzed, and showed that the use of AI is still relatively low. On average only 18.4% of companies use AI software in at least one production area. Logistic regression analysis revealed that neither company size nor role in the supply chain or technology intensity are statistically significantly related to AI usage. However, a significant positive relationship was found between I4.0 readiness and AI adoption, suggesting that companies with advanced digital infrastructures and integrated cyber-physical systems are more likely to adopt AI. This finding underlines the importance of digital transformation for the integration of AI software. The study concludes that while company characteristics such as size and the role of the company in the supply chain are not statistically significantly related to the use of AI, the level of digital readiness is crucial.

Applied SciencesVol. 14(16)
University of Zagreb (HR), University of Maribor (SI)
Javna Agencija za Raziskovalno Dejavnost RS
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Digital Transformation in Industry
5.23
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.