AI-driven Customer Experience, Marketing Intelligence and Anti-money-laundering Analytics in Digital Enterprises: A Critical Integrative Review

Abstract

Artificial intelligence has become embedded in three enterprise decision domains that are usually studied separately: customer experience, marketing intelligence and anti-money-laundering analytics. Their technical foundations overlap, yet their objectives, evidence conditions, error costs and accountability requirements differ sharply. This critical narrative review integrates scholarship on these domains to assess where artificial intelligence creates defensible value, where empirical claims outrun the evidence, and how digital enterprises should govern shared analytical capabilities without collapsing unlike decisions into a single automation logic. Literature was selected from accessible scholarly indexes, repositories and article records for the period 1 January 2015 to 8 June 2026, with earlier methodological work retained where conceptually necessary. The synthesis indicates that artificial intelligence is most credible when the target decision is well specified, feedback is informative, model outputs are connected to an organisational action, and meaningful human escalation remains available. In customer experience, personalisation and conversational systems can improve relevance and interaction efficiency, but privacy concerns, disclosure effects, algorithm aversion and perceived loss of agency make customer response strongly context dependent. In marketing intelligence, predictive gains do not automatically translate into incremental value because data are endogenous to earlier decisions, causal questions remain distinct from prediction, and organisational analytics capability mediates adoption. In anti-money-laundering analytics, supervised, graph, temporal, active-learning and hybrid approaches can improve alert prioritisation and pattern discovery, yet generalisability is constrained by extreme class imbalance, weak or selective ground truth, proprietary data, adaptive adversaries and limited prospective external validation. Across all three domains, the central design problem is not whether to automate, but how to align objectives, data provenance, interpretability, human authority and monitoring with the consequences of the decision. The review therefore argues for portfolio-based enterprise AI governance: shared infrastructure should be combined with domain-specific evidence standards, utility functions and escalation controls.