• The effect of AI on innovation capacity is strongly mediated by organizational age, AI maturity, and manufacturing strategy, based on quantitative modeling of 174 industrial AI use cases • The choice of manufacturing strategy significantly influences the outcome of potentially coexisting synergic, competitive, and parasitic innovation capacity types • Bayesian Network modeling provides a robust, predictive framework for diagnosing and guiding the transition to Industry 5.0, achieving 95% accuracy in predicting innovation outcomes The classic literature conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. However, the advent of Artificial Intelligence (AI) 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 conducted a quantitative study using a survey sample of 174 industrial AI use cases. With this data, we assess 3 hypotheses and critically discuss the influence of 4 factors extracted from the literature: organization age, AI maturity, manufacturing strategy, and innovation capacity. Further, we evaluate a conceptual model to predict AI's effect on innovation capacity in the context of Industry 5.0. A Bayesian Network is trained on the dataset that supports diagnosis and prediction of innovation capacity types with an overall path-level accuracy of 95%. This study provides researchers with a new understanding of the interplay between artificial intelligence and human intelligence in innovation management. It provides practitioners with a quantitative decision framework to guide their transition towards Industry 5.0.
Bécue et al. (Wed,) studied this question.