This empirical investigation examines the systemic, ethical, cultural, and operational frictions arising from the deployment of artificial intelligence (AI) in cross-border customer communications within multinational enterprises across Science, Technology, and Medicine (STM) publishing, financial services, audit/litigation, and global subscription operations. Grounded in Socio-Technical Systems Theory, Affordance Theory, and Intercultural Communication Theory, this study employs a mixed-methods explanatory sequential design (N = 71 quantitative survey respondents; N = 6 qualitative in-depth interviews, focus groups, and case artifacts) to determine how algorithmic mediation reshapes international service encounters. Quantitative analyses including One-Way ANOVA, multi-variable linear regression, parametric t-tests, Z-tests, discriminant function analysis, and exponential smoothing forecasting demonstrate that while AI integration significantly optimizes operational throughput, uncurated algorithmic deployment creates severe friction in high-context, high-uncertainty-avoidance cultural environments. Linear regression reveals that explicit AI transparency and disclosure strongly predict customer retention and trust resilience (β = 0.538, p < 0.001), while non-disclosure accelerates churn and brand degradation. Discriminant analysis confirms that cultural intelligence (CQ) alignment, geo-conditional consent architectures, and hybrid human-in-the-loop (HITL) escalation protocols are primary determinants separating high-performing global service networks from those suffering cross-jurisdictional compliance failures. The study synthesizes these empirical findings into an original, multi-layered governance framework, the Algorithmic Communication and Ethical Governance Framework (ACEGF) offering actionable protocols for global service managers, technology developers, and international compliance bodies to ensure sustainable, ethical, and culturally resonant cross-border business operations.