Tackling the Root Cause of the World’s Billing Problem

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Identifying cost savings for customers who have been consistently overcharged only solves one part of the global billing problem. Technology service providers, such as telecoms companies, are also at risk when failing to accurately bill their clients. Losing customers and breaching regulatory compliance are the foremost hazards, particularly at this time of increased data scrutiny and customer advocacy.

It has never been easier for customers to switch suppliers, and now, AI-enabled solutions are revealing the hidden inaccuracies of the bills they are paying. Because of this reality, technology service providers can no longer afford to risk overcharging valuable clients, who are likely to pursue long-term cost savings with a different supplier in the event of overpayment and inefficiency.

While many technology service providers have strived to improve billing accuracy, even the best traditional approaches result in widespread inconsistency. Even when customers choose to stay with a supplier despite billing issues, supplier customer service and account management teams are becoming inundated with escalations, which comes at a great cost to overall operational efficiency.

Legacy Limitations to Addressing Billing Errors

The systems commonly used by suppliers to investigate billing errors involve mammoth rule-based engines, usually operating via SQL data sources. Although this method was deemed to be the best option to date, it continues to suffer from a wide range of problems.

On the one hand, SQL-backed systems require continuous and extensive updates, while also relying on rules that often fall short or fail when engaging in real-world customer interactions. Other challenges include product behaviors not syncing with set rules, and poor business alignment that leads to an inability to define important concepts. A lack of flexibility and relevance is at the heart of the problem, but new technologies and design considerations have finally led to a lasting solution.

Fixing the Problem for Suppliers and Buyers Alike

As machine learning and artificial intelligence (AI) technologies have matured, they have given way to solutions that are transforming technology service billing. Equipped with a solution built on these foundations, organizations can now process and investigate massive amounts of invoice data in real-time, enabling new levels of accuracy and efficiency. Armed with the right solution, suppliers and buyers can eliminate bad data, improve efficiency, and prevent significant losses forever.

The solution created by Thinking Machine Systems is the leading example of this kind of solution. We provide an AI platform designed to consolidate analytics between technology service invoices and contracts, revealing billing inaccuracies that are resulting in unduly high costs for customers.

For buyers of technology services, our solution enables them to uncover immediate cost savings and equips them with advanced spend intelligence. With this level of insight, customers can effectively map out future deals that will provide them with the best service at the best price.

Above all, our technologies are improving the way in which suppliers and buyers work together, improving data quality and the way in which previously siloed teams interact and do business with each other. Whether you are looking to solve this issue as a supplier or buyer of technology services, find out more about our solutions at https://www.thinkingmachine.co.

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Richard Martin
Co-founder

TMS

TMS is an AI company that creates cost savings from Telecom expenses.

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