AI-DRIVEN BUSINESS ANALYTICS FOR COMPETITIVE ADVANTAGE IN SERVICE-ORIENTED ENTERPRISES: CUSTOMER EXPERIENCE AND EFFICIENCY
DOI:
https://doi.org/10.63125/mx0k6019Keywords:
AI-Driven Business Analytics, Customer Experience, Operational Efficiency, Service-Oriented Enterprises, Process Mining, Conversational AI, Personalization, Reinforcement LearningAbstract
Drawing on a PRISMA-guided systematic review, this paper synthesizes evidence on how AI-driven business analytics create competitive advantage in service-oriented enterprises by improving customer experience and operational efficiency. Searches across major databases (Scopus, Web of Science, IEEE Xplore, ACM, ScienceDirect, ABI/INFORM, Emerald, PubMed), complemented by snowballing, yielded 115 peer-reviewed studies from 2015 to 2025. Across the corpus, four in five studies reported improvement in at least one focal outcome, 45.2 percent achieved joint gains in experience and efficiency, 10.4 percent showed trade-offs, and only 3.5 percent reported deterioration. Mechanisms associated with joint gains include conversational AI and agent assist, prescriptive routing and scheduling with reinforcement learning or optimization, and forecast-to-schedule loops; voice-of-customer text and speech analytics and personalization consistently lift experience, while process mining, robotic process automation, and capacity planning reduce cycle time, queues, and cost-to-serve. Deployment pattern and governance matter: human in the loop configurations and programs with privacy safeguards, drift monitoring, fairness checking, and override policies halve the incidence of trade-offs compared with low maturity implementations. Sector analyses indicate that data rich, SLA intense contexts such as telecom, financial services, and logistics often realize balanced benefits, while public services and healthcare see efficiency first unless communication and escalation are redesigned in tandem. Methodologically, stronger designs confirm that effects persist when prediction is coupled to decision rights and evaluated with value linked metrics. The review offers an integrative framework and actionable guidance for prioritizing mechanisms that change decisions in the flow of work and for institutionalizing scalable analytics capabilities.