Artificial intelligence (AI) is an increasingly notable presence in society, industries, and organizations, making its necessity felt more in managerial decisions and practices. This paper aims to outline the importance of the topic related to the increase in the adaptability, agility, and resilience of the management system as a result of AI integration, resorting to a bibliometric type of research. A total of 107 papers from the period 2007–2024 exported from the Web of Science Core Collection database were analyzed, with support of Biblioshiny software. This topic is proving to be one of heightened global interest, being comprehensively addressed by world leaders in AI research and technologies such as the United States, China, Great Britain, France, India, and beyond. Collaborative relationships established between geographic regions are captured, noting the power and expansion of the theme on all continents of the globe. Likewise, its thematic and strategic evolution is characterized as a surprising one, managing to incorporate and relate concepts with a strong technical and IT character such as feature extraction, machine learning, reinforcement learning with concepts of a managerial nature as supporting customer-tailored interaction, employee skills development, company productivity, and innovation.

This study investigates the predictive contribution of digital infrastructure to GDP per capita growth across 27 European Union Member States over the period 1995–2024, using a balanced panel of 810 country–year observations and an explainable machine learning framework. An XGBoost model trained on six World Bank indicators—fixed broadband subscriptions, internet users, mobile subscriptions, patent applications, R&D expenditure, and secure internet servers—achieves a training R² of 0.804 and a test R² of 0.430 under temporal out-of-sample validation spanning the COVID-19 structural break. TreeSHAP decomposition identifies fixed broadband as the strongest predictor of model-estimated GDP per capita growth (mean |SHAP| = 0.948; bootstrap rank 1 in 78% of 50 resamples; Friedman Chi-square (5) = 168.16, p < 0.001), providing predictive support for Hypothesis H1. Innovation indicators, represented by patent applications and R&D expenditure, exceed the pre-specified materiality threshold, providing predictive support for H2, while SHAP dependence plots reveal pronounced non-linear threshold patterns consistent with S-curve diffusion theory, supporting H3. Temporal SHAP decomposition identifies three structural phases: broadband dominance (1995–2007), crisis-induced reconfiguration (2008–2013), and quality convergence (2014–2024). The framework reconciles contradictory findings from prior literature by visualizing the complete functional form of the broadband–growth relationship without imposing a parametric specification.