Revenue assurance has become a critical priority for service-intensive organizations, where complex operations, high transaction volumes, and dynamic customer interactions create significant risks of revenue leakage. Traditional revenue assurance frameworks often rely on reactive auditing and manual reconciliation, which are limited in scalability and effectiveness. This study examines how artificial intelligence (AI) and big data analytics can be strategically leveraged to transform revenue assurance practices into proactive, predictive, and adaptive systems. By utilizing AI-driven algorithms, organizations can detect anomalies in real time, identify fraudulent patterns, and optimize billing accuracy across multiple service touchpoints. Machine learning models enhance revenue integrity by continuously learning from transactional data, improving detection precision, and minimizing false positives. Big data plays a complementary role by providing large-scale integration of structured and unstructured datasets from diverse sources such as billing systems, customer usage patterns, and network logs. Advanced analytics enable organizations to uncover hidden trends, correlate disparate variables, and predict future revenue risks with higher accuracy. Together, AI and big data empower organizations to implement end-to-end revenue assurance strategies that not only safeguard financial performance but also enhance customer trust and regulatory compliance. Furthermore, this paper highlights the importance of embedding revenue assurance into broader digital transformation initiatives. Service-intensive organizations such as telecommunications, banking, healthcare, and logistics can significantly benefit from integrating predictive risk assessment, automation, and real-time monitoring into their operational frameworks. The adoption of AI-powered dashboards and visualization tools further enhances decision-making by providing executives with actionable insights into revenue streams and potential vulnerabilities. The findings suggest that revenue assurance strategies grounded in AI and big data are not merely defensive measures but drivers of competitive advantage, enabling organizations to reduce losses, optimize operational efficiency, and sustain long-term growth. Future directions include the integration of blockchain for secure auditing and the development of adaptive compliance frameworks aligned with global standards.