AI's effect on innovation capacity in the context of industry 5.0: a scoping review

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Bécue, A (Author) Other; João Gama (Author) FEP; Brito, PQ (Author) Other

Journal

Title: Artificial Intelligence Review

Vol. 57

ISSN: 0269-2821

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-011-5BY

DOI: 10.1007/s10462-024-10864-6

Abstract (EN): The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from how innovation fosters AI to how AI fosters innovation. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI's effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 29