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This study aims to propose an architecture and presents the implementation of a unified chatbot that faces the challenges of heterogeneous communication channels. This approach enables the interaction with the chatbot to be carried out over multiple communication media on a single platform.

The chatbot was embedded in a unified communications framework. Furthermore, it has been developed and tested using the information and communications technology (ICT)Core platform. Three test scenarios have been considered in the context of a digital marketing company, which include the use of multiple channels such as text, audio and e-mail. Usability and empirical tests were performed to collect both qualitative and quantitative data.

The results indicate that the proposed model improves the completion rate and enables the chatbot to interact with the customer by capturing information over multiple channels. The findings also reveal that digital marketing organizations can use a unified chatbot in their marketing campaigns, which contributes to improving the quality of customer interaction, message personalization and continuous learning throughout the process.

While the use of a chatbot is a relatively common practice among companies, its integration into unified communications networks is an emerging topic. Proposals for integration into a unified communication channel have mainly focused on access to the same account and conversations from multiple devices or access platforms. This approach, while useful, does not allow for the integration of information from multiple sources. Alternatively, an integrated architecture is suggested in which a chatbot obtains knowledge from multiple sources and uses it to increase the quality of communication with the customer.

This paper aims to explore the intersection of artificial intelligence (AI) and marketing within the context of knowledge management (KM). It investigates how AI technologies facilitate data-driven decision-making, enhance business communication, improve customer personalization, optimize marketing campaigns and boost overall marketing effectiveness.

This study uses a quantitative and systematic approach, integrating citation analysis, text mining and co-citation analysis to examine foundational research areas and the evolution of AI in marketing. This comprehensive analysis addresses the current gap in empirical investigations of AI’s influence on marketing and its future developments.

This study identifies three main perspectives that have shaped the foundation of AI in marketing: proxy, tool and ensemble views. It develops a managerially relevant conceptual framework that outlines future research directions and expands the boundaries of AI and marketing literature within the KM landscape.

This research proposes a conceptual model that integrates AI and marketing within the KM context, offering new research trajectories. This study provides a holistic view of how AI can enhance knowledge sharing, strategic planning and decision-making in marketing.