AI in billing: Enhancing efficiency in Media and Information Services

In most large organizations, billing is a complex, resource-intensive process. It requires extensive operational oversight, takes up significant time, and often leads to operational inefficiencies. Ideally, companies would like to focus on delivering exceptional products and services to customers rather than be bogged down by the intricacies of billing. But in reality, billing processes create substantial complexity that drain energy and resources. Moreover, the number of people who truly understand billing workflows end to end within most organizations is usually small and those individuals are in high demand.

This complexity is even more pronounced in the Media and Information Services (M&IS) industry, where companies frequently deal with unique and multifaceted billing challenges. These challenges are driven by factors such as diverse product offerings, complex customer demands, and the need for frequent system integrations. 

Billing challenges in the media and information services industry

M&IS leaders must constantly grapple with the following core issues:

• Huge product variation requires different billing models. Within a single company, different divisions may be selling a variety of products, from subscriptions and usage-based services to licenses and ad hoc purchases. Additionally, bundling and tiered discounting are commonplace. These variations complicate the management of a unified billing platform.
• Multiple fulfilment systems and data integration. To support this diverse product portfolio, M&IS companies often rely on a variety of fulfilment systems, each responsible for different aspects of product delivery. This means that data must be aggregated from multiple sources to create accurate and comprehensive bills. Achieving seamless integration between these systems remains a challenge.
• Enterprise client bespoke requirements. In the B2B space, M&IS companies work with large enterprise clients who often demand tailored billing arrangements or invoicing structures. This can create additional complexity, as the billing system must accommodate specific customer needs, potentially diverging from standard billing processes.
• Evolving needs. New service offerings, delivery models, and consumption patterns are emerging rapidly in the M&IS industry. Additionally, frequent mergers and acquisitions create further complications, especially when integrating diverse systems and platforms.

These challenges result in high complexity, messy processes, and unnecessary costs. For many M&IS organizations, particularly those frequently acquiring new businesses, it often seems easier to "throw resources" at the problem, with manual interventions (sometimes offshore) becoming the default workaround.

How using machine learning and Generative AI in billing can help

Despite the inherent complexity, Generative AI (GenAI) is emerging as a transformative tool for M&IS companies looking to streamline and enhance their billing operations. Through intelligent data integration, real-time anomaly detection, and even support for customer queries and disputes, GenAI offers significant promise in resolving key billing challenges. Some of the main applications include:

• Intelligent data integration. With multiple fulfilment systems, achieving seamless integration is difficult, but GenAI can make the process more manageable. By analysing metadata and schemas across disparate systems, GenAI models can automatically create mappings between fields and suggest transformations to harmonize them. By training on historical integration efforts, GenAI can infer relationships and improve data synchronisation. Solutions such as Cloudera’s data lakehouse technology paired with GenAI can support more efficient data ingestion and integration across multiple platforms.
• Support customer queries and disputes. GenAI can automate responses to common billing inquiries, analyse billing data, and clarify complex invoices for customers. Conversational AI tools like IBM Watson or other NLP-focused GenAI models can enhance customer service by providing personalized, real-time support based on historical billing patterns and customer preferences.
• Flag issues and identify anomalies. GenAI models can be trained on historical billing data to recognize the typical patterns of invoices, payments, and charges for various customer segments or product lines. By continuously analysing incoming data, AI models can identify discrepancies, such as unusually high charges or missing invoices, in real-time, allowing companies to address these issues before they reach customers. Tools like Salesforce Einstein or bespoke GenAI-powered solutions for financial data are already being used to detect irregularities, minimising billing disputes.
• Understanding bespoke billing requirements. GenAI can be employed to review customer contracts and identify billing requirements that may deviate from standard processes. By analysing complex contract language, AI models can surface custom billing needs and ensure they are reflected in the system. This use of GenAI can also reduce errors in interpreting contractual terms that affect billing.

Acknowledging the challenges and limitations of Generative AI in billing

While the potential of Generative AI to address billing challenges is clear, organizations must approach its adoption with realistic expectations. For instance, integrating multiple legacy systems with GenAI solutions remains a significant hurdle. Many M&IS companies rely on outdated infrastructure, and the complexity of migrating or integrating AI tools with these legacy systems should not be underestimated. Legacy systems may require substantial customisation or replacement, and their compatibility with modern AI technologies is often not guaranteed.

Moreover, although GenAI models can support anomaly detection and process automation, they are not infallible. Billing data often includes complex variables such as customer-specific discounts, custom pricing structures, and special billing arrangements that may not always align with AI's predefined patterns. While AI can flag anomalies, human judgment will continue to be essential to resolve complex issues.

Additionally, data privacy and compliance considerations remain top of mind for many M&IS organizations, particularly given the sensitive nature of billing information. Implementing GenAI solutions will require robust safeguards to ensure customer data is handled securely and complies with privacy regulations.

Lastly, human expertize will remain critical as AI solutions in billing evolve. GenAI can support the automation of routine tasks, but the interpretation of complex billing nuances and the resolution of disputes will require human oversight to maintain the accuracy and trust that customers expect.

Conclusion

Billing within the Media and Information Services industry remains a complex, resource-draining process. However, Generative AI presents a valuable opportunity for M&IS companies to reduce this complexity, improve operational efficiency, and deliver better customer outcomes. From enabling seamless data integration to identifying billing anomalies and supporting customer service teams, GenAI can streamline traditionally cumbersome processes and free up resources for more strategic pursuits. Nevertheless, M&IS leaders should be mindful of the ongoing challenges in implementing AI solutions—particularly in integrating them with legacy systems, ensuring compliance, and maintaining the necessary human expertize. By approaching GenAI adoption strategically, M&IS companies can transform their billing processes and unlock new efficiencies, paving the way for improved customer satisfaction and reduced operational costs.

To learn more about how Generative AI can transform your billing processes, reduce complexity, and enhance efficiency, get in touch. We can support you to implement AI in your billing operations and stay ahead in the Media and Information Services industry.