Implementación de la inteligencia artificial en la gestión del cliente
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Universidad de Medellín
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El acelerado desarrollo de las tecnologías de inteligencia artificial (IA) está transformando la manera en que las organizaciones interactúan con sus clientes. Según Davenport y Ronanki (2018), el 84% de las empresas considera que la IA les proporcionará una ventaja competitiva significativa. Sin embargo, su aplicación estratégica y sostenible en la gestión de relaciones con los clientes (CRM) permanece subestimada. Este estudio tiene como objetivo general determinar el impacto de la implementación de tecnologías de IA en la gestión de relaciones con clientes y estrategias de marketing empresarial. El marco teórico se fundamenta en los conceptos de automatización inteligente, toma de decisiones basada en datos y estrategias centradas en el cliente. La metodología incluye un análisis bibliométrico para mapear las tendencias más relevantes en la literatura científica sobre IA y CRM, seguido de un análisis de contenido de 197 artículos científicos. Para la búsqueda narrativa se profundizó en artículos de selección selectiva que muestran operaciones de procesamiento de lenguaje natural (PLN), segmentación latente de Dirichlet (LDA) y detección de emociones para extraer y evaluar las tendencias emergentes, patrones de implementación y resultados obtenidos. Las principales variables vinculadas a cinco categorías: automatización, personalización, predicción, comprensión emocional y mejoramiento de la experiencia del cliente, las cuales han sido claves en la mejora del potencial de revitalización de la gestión de clientes para permitir experiencias anticipadas, adaptativas y emocionalmente inteligentes.
The accelerated development of artificial intelligence (AI) technologies is transforming the way organizations interact with their customers. However, its strategic and sustainable application in customer relationship management (CRM) remains underexplored. This study aims to characterize how AI contributes to the evolution of customer management processes through an integrative approach. The theoretical framework is based on intelligent automation, data-driven decision-making, and customer-centric strategies. The methodology included a bibliometric analysis to map the most relevant trends in the scientific literature on AI and CRM, followed by text mining and content analysis of a corpus of 197 academic articles. For the in-depth narrative analysis, a representative sample of 25 key articles was selected, allowing for the identification of core topics and emotional clusters. Natural language processing (NLP), latent Dirichlet allocation (LDA), and emotion detection models were applied to extract and interpret thematic and affective patterns. The results validated five key categories: automation, personalization, prediction, emotional understanding, and ethical concerns. The study concludes that AI has the potential to revolutionize CRM by enabling anticipatory, adaptive, and emotionally intelligent customer experiences. Future research should focus on evaluating implementation models by sector and assessing the performance impact of AI-driven CRM strategies.
The accelerated development of artificial intelligence (AI) technologies is transforming the way organizations interact with their customers. However, its strategic and sustainable application in customer relationship management (CRM) remains underexplored. This study aims to characterize how AI contributes to the evolution of customer management processes through an integrative approach. The theoretical framework is based on intelligent automation, data-driven decision-making, and customer-centric strategies. The methodology included a bibliometric analysis to map the most relevant trends in the scientific literature on AI and CRM, followed by text mining and content analysis of a corpus of 197 academic articles. For the in-depth narrative analysis, a representative sample of 25 key articles was selected, allowing for the identification of core topics and emotional clusters. Natural language processing (NLP), latent Dirichlet allocation (LDA), and emotion detection models were applied to extract and interpret thematic and affective patterns. The results validated five key categories: automation, personalization, prediction, emotional understanding, and ethical concerns. The study concludes that AI has the potential to revolutionize CRM by enabling anticipatory, adaptive, and emotionally intelligent customer experiences. Future research should focus on evaluating implementation models by sector and assessing the performance impact of AI-driven CRM strategies.
Palabras clave
Inteligencia Artificial (IA), Aprendizaje automático, Macrodatos, Procesamiento del Lenguaje Natural (NLP), Analítica de Datos, Automatización, Machine learning, Big Data