This study presents a comprehensive analysis of the influence of conversational artificial intelligence (AI)—a subset of AI that enables machines to simulate human-like conversations through natural language processing (NLP)—on consumer decision-making within the digital marketing landscape. Through a systematic literature review and advanced clustering techniques, we offer a novel perspective on the evolving research in this field. Our methodology combines TF-IDF vectorization with K-means clustering and silhouette analysis to identify and examine five distinct thematic clusters: Consumer Behavior and Engagement, Sentiment Analysis and NLP in E-Commerce, Artificial Intelligence in Marketing, Trust and Technology Adoption, and Big Data and Predictive Analytics. This clustering approach provides valuable insights into the temporal, disciplinary, and geographical dimensions of the research landscape. By synthesizing findings from 78 scholarly articles, we highlight the transformative potential of conversational AI in shaping marketing strategies and enhancing consumer experiences. Our analysis reveals emerging trends, critical gaps, and future directions for research, offering decision-makers in both academia and industry a structured framework for understanding and leveraging conversational AI in consumer-centric marketing initiatives. The principal contribution of this article lies in its data-driven approach to mapping the research landscape to identify key thematic clusters, emerging trends, and underexplored areas in the field. By integrating computational clustering methods with a systematic literature review, we provide a more structured and granular understanding of the field, identifying key thematic intersections and underexplored areas. This study not only advances theoretical knowledge but also offers practical insights for businesses and researchers seeking to optimize AI-driven consumer engagement strategies.

Using 285 responses obtained in a survey of high- and middle-level managers of firms located in Spain and by implementing clustering techniques, we identify differences in the conception and adoption of a digital strategy and the use of social media. A driver for these differences is represented by whether companies are adopting a B2C or a B2B business model. Specifically, B2B companies are slower to generate an overall digital strategy compared to B2C firms. Moreover, B2B firms adopting a digital strategy are prioritizing the use of more professionally oriented platforms (e.g., LinkedIn), while B2C firms are favoring the use of more socially oriented services (e.g., Facebook). Additionally, we find that those B2C companies without a clear digital strategy are nonetheless significantly using social media, particularly social networks, while B2B companies are delaying the use of social media until conceiving a clear digital strategy.

The rapid emergence of Generative AI (GAI) in recent years, coupled with its potential to revolutionize a vast array of industries, functions, and tasks, has led to an increasing number of companies—including digital businesses and e-commerce firms—to evaluate its immediate application at both operational and strategic levels. One of the existing tools to support such business decisions is the Gartner Hype Cycle (GHC), where AI in general, and GAI in particular, have been positioned for years. Notably, in Gartner’s latest report, GenAI occupies a concerning position, as it appears to be entering a phase where declining investment and interest are driven by its inability to meet initial expectations. This paper aims to assess whether the scenario outlined in the report can be objectively confirmed through public and replicable indicators that any researcher can utilize to address this question. Additionally, we explore the possibility that certain technologies—such as GAI, that due to its training and improvement requirements have been introduced in the market as a free tool and targeting individual users rather than solely corporate clients —may bypass some phases of the Gartner curve. Our main contribution is the proposal and testing of indicators that can be used for this purpose, yielding key insights from an exploratory rather than confirmatory perspective, with implications for companies’ adoption of GAI and particularly for digital and e-commerce businesses. Finally, we highlight the main limitations identified and outline future research avenues to address them.