The Role of Artificial Intelligence in HRM–Marketing Integration: A PLS-SEM–Informed Review

Abstract

AI is revolutionizing HRM and marketing through data-driven decision-making, predictive analytics, and automated workflows. Academic research on AI-driven HRM–Marketing integration has grown as companies adopt AI-enabled employee and customer strategies. Partial Least Squares Structural Equation Modelling (PLS-SEM) is the leading analytical method for modelling complex, multidimensional, and often formative phenomena, including AI capability, people analytics, customer analytics, and cross-functional performance. However, a systematic evaluation of PLS-SEM in this intersection is absent. This systematic literature review synthesizes 97 peer-reviewed studies (2015–2025) from Scopus and Web of Science using PRISMA 2020. The review highlights internal–external experience alignment, cross-domain predictive analytics, and algorithmic coordination as AI-enabled integration pathways. While PLS-SEM is well-suited for capturing these relationships, findings reveal methodological inconsistencies, such as weak justification, limited algorithm configuration reporting, insufficient predictive assessment, and underuse of advanced techniques (e.g., higher-order constructs, PLSpredict, FIMIX-PLS, endogeneity controls). This paper demonstrates how AI promotes cross-functional HRM–Marketing alignment and offers best practices to improve the rigour, transparency, and predictive value of future PLS-SEM–based research.