Integrating Knowledge Management and AI for optimizing customer experience
a systematic review
DOI:
https://doi.org/10.23925/2179-3565.2026v17i2e76453Palabras clave:
Artificial Intelligence, Knowledge Management, Customer Experience, AI-enabled Services, Personalization, Service Speed, Knowledge-supported AI Systems, Customer SatisfactionResumen
In today’s digital service landscape, organizations increasingly deploy Artificial Intelligence (AI) technologies in the hope of delivering faster, more personalized, and seamless customer experiences. However, many firms continue to face challenges in translating AI capabilities into measurable improvements in customer satisfaction, largely because AI tools often operate without adequate support from structured Knowledge Management (KM) practices. When the knowledge base behind automated systems is fragmented or inconsistent, customers encounter variability in service quality, reduced trust, and limited personalization depth. This study investigates how AI-enabled service attributes supported by KM-related perceptions shape overall customer experience by analyzing empirical data from 300 customers who regularly engage with AI-driven service platforms. The dataset captures key experiential dimensions, including personalization, service speed, ease of use, trust in AI, and the perceived usefulness of knowledge support embedded in automated service responses. Using descriptive statistics, correlation analysis, regression modelling, and ANOVA, the study finds that trust in AI systems and perceived KM usefulness exert the strongest influence on overall satisfaction, while other attributes such as personalization and ease of use demonstrate weaker predictive power. The results also reveal that customer satisfaction does not significantly differ across varying levels of AI interaction frequency, suggesting that qualitative aspects of AI-based interactions matter more than the volume of usage. Overall, the findings emphasize that optimal customer experience arises not from AI functionality alone, but from the quality, clarity, and reliability of the knowledge that informs AI outputs. The study provides empirical insights to guide organizations in strengthening KM foundations to support AI systems more effectively and enhance customer experience outcomes.
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