This entry develops a theoretical framework for reconceptualizing marketing competencies in response to the AI revolution, focusing specifically on curriculum adaptation in higher education. Drawing from marketing education literature and professional competency models, the entry proposes a structured approach to understanding how traditional marketing competencies evolve when intersecting with AI capabilities. The framework introduces a Competency Evolution Matrix that maps the transformation of core marketing skills in an AI-enhanced environment. Through comprehensive analysis of current educational offerings at leading international institutions, the entry identifies prevalent pedagogical approaches ranging from sequential learning models to strategic executive-focused programs and reveals significant geographical variations in curriculum design, with North American institutions emphasizing practical applications while European counterparts integrate stronger ethical and regulatory dimensions. The entry identifies critical gaps in existing programs, including limited interdisciplinary content, insufficient AI ethics training, and minimal industry collaboration in course design. By analyzing the intersection between traditional marketing fundamentals and emerging AI-driven capabilities, the entry establishes a theoretical basis for curriculum adaptation that is both academically robust and practically implementable. This theoretical contribution provides clear guidelines for marketing departments seeking to evolve their educational offerings while maintaining essential marketing foundations and preparing graduates for an increasingly AI-augmented professional landscape. The entry further suggests that successful adaptation requires balancing technical AI proficiency with strategic marketing principles and ethical considerations, ensuring graduates can effectively leverage AI technologies while maintaining critical human oversight in decision-making processes.

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Reconceptualizing Marketing Competencies for the AI Era

  • Miguel Ángel García-Madurga,
  • Ana-Julia Grilló-Méndez

摘要

This entry develops a theoretical framework for reconceptualizing marketing competencies in response to the AI revolution, focusing specifically on curriculum adaptation in higher education. Drawing from marketing education literature and professional competency models, the entry proposes a structured approach to understanding how traditional marketing competencies evolve when intersecting with AI capabilities. The framework introduces a Competency Evolution Matrix that maps the transformation of core marketing skills in an AI-enhanced environment. Through comprehensive analysis of current educational offerings at leading international institutions, the entry identifies prevalent pedagogical approaches ranging from sequential learning models to strategic executive-focused programs and reveals significant geographical variations in curriculum design, with North American institutions emphasizing practical applications while European counterparts integrate stronger ethical and regulatory dimensions. The entry identifies critical gaps in existing programs, including limited interdisciplinary content, insufficient AI ethics training, and minimal industry collaboration in course design. By analyzing the intersection between traditional marketing fundamentals and emerging AI-driven capabilities, the entry establishes a theoretical basis for curriculum adaptation that is both academically robust and practically implementable. This theoretical contribution provides clear guidelines for marketing departments seeking to evolve their educational offerings while maintaining essential marketing foundations and preparing graduates for an increasingly AI-augmented professional landscape. The entry further suggests that successful adaptation requires balancing technical AI proficiency with strategic marketing principles and ethical considerations, ensuring graduates can effectively leverage AI technologies while maintaining critical human oversight in decision-making processes.