Artificial intelligence for emotion-aware customer journeys: A systematic literature review

Abstract

Emotions play a crucial role in shaping customer perceptions and behaviors, influencing satisfaction, loyalty, and brand attachment. In the era of artificial intelligence (AI), firms have gained unprecedented capabilities to capture, interpret, and respond to emotional cues, transforming traditional approaches to customer experience management. As customer interactions increasingly shift to digital environments, AI-driven techniques allow organizations to extract deeper insights from large volumes of customer-generated data, offering new opportunities to understand expectations and affective states. However, despite the growing integration of AI in marketing, the mechanisms through which emotional awareness enhances customer journeys remain conceptually underexplored and marked by considerable fragmentation. This study conducts a systematic literature review (SLR) to address this gap, following PRISMA guidelines to ensure methodological rigor and transparency. A total of 201 peer-reviewed articles published between 2015 and 2025 were screened, of which 35 met the inclusion criteria for in-depth analysis. The findings reveal that AI technologies such as sentiment analysis, natural language processing, machine learning, and deep learning models enable firms to detect and forecast customers’ emotional states. These emotional insights contribute to more personalized, adaptive, and engaging interactions across different stages of the customer journey. Despite these advancements, the review reveals limited progress toward predictive emotional modeling and real-time dynamic personalization. Challenges remain related to data quality, cultural and linguistic diversity, algorithmic transparency, and ethical concerns, including privacy and the risk of emotional manipulation. Overall, this study consolidates current knowledge, underscores both theoretical and managerial contributions, and provides directions for future research. It highlights the need for stronger predictive capabilities, enhanced emotional modeling, and responsible deployment of AI to design emotionally-aware and customer-centric journeys.