Revenue Assurance (RA) has long occupied a critical but often under-resourced position within organizational finance functions. Traditionally reliant on periodic audits and manual reconciliation, RA frameworks have struggled to keep pace with the scale and velocity of modern digital transactions. This paper investigates how the adoption of Artificial Intelligence (AI) encompassing machine learning, predictive analytics, and robotic process automation   reshapes these frameworks in practice. Drawing on survey responses from fifty professionals with direct exposure to AI-enabled RA environments, the study finds that automation and real-time anomaly detection yield measurable gains in billing accuracy, fraud containment, and financial oversight. At the same time, respondents consistently flag implementation barriers: prohibitive upfront costs, a shortage of domain-specific AI expertise, and unresolved questions around data governance. The paper concludes that while AI does not eliminate the underlying complexity of revenue protection, it shifts the locus of RA work from reactive error-correction toward proactive risk management   a shift with significant long-term implications for how organizations structure their financial control functions.

Keywords— Revenue Assurance; Artificial Intelligence; billing; implementation; revenue; locus.