The Challenge: Struggling to Keep Up with Invoice Processing

No wonder, Invoice processing is a critical part of any retail operation, and when done right, it keeps vendors happy, stock flowing, and cash management under control. But as this retailer expanded, their traditional invoice processing system began to show cracks:

• Manual Data Entry Errors: Our customer teams manually entered invoice data, mistakes were inevitable. From incorrect amounts to mismatched vendor details, these errors caused delays in payments and strained relationships with suppliers.
• Slow Processing Times: Invoices were taking too long to process. Delayed approvals meant late payments, leading to penalties and disrupted supply chains.
• Inconsistent Approvals: The lack of a streamlined, automated approval process meant that some invoices got stuck in the system, while others went through without the necessary oversight.
• Data Fragmentation: Invoice data was scattered across different systems, making it hard to get a clear view of outstanding payments, vendor performance, or trends that could help improve cash flow and inventory management.

The customer needed a solution that could handle the growing volume of invoices quickly, reduce errors, and streamline approvals, all while providing insights into their financial operations.

So, Why didn't we choose just Generative AI?

YES, Generative AI is great!, but not for this scenario in our case, also when it comes to handling structured data like invoices, it’s not always the best fit. Processing invoices requires precision, data extraction, validation, and risk assessment - areas where different AI tools excel. That’s why after multiple initial assessments and workshops with customers, finding the real business problem - we decided to go with a hybrid AI approach, combining multiple AI technologies with human oversight for the best results.

The Solution: 

Here’s how we applied our hybrid AI solution to completely transform the retailer’s invoice processing system:

1. Automating Invoice Data Entry: The first step was to automate the data entry process. Using AI-powered tools, we digitized invoices, both electronic and paper, reducing manual input by 90%. This automation eliminated human errors in entering invoice details, ensuring that data was consistent and accurate right from the start.
2. Classifying and Routing Invoices Automatically: With hundreds of invoices coming in daily, manually sorting through them was slowing everything down. Our AI solution automatically classified invoices based on their type whether standard payments, credits, or adjustments - and routed them to the correct department. This reduced the time spent on sorting, ensuring that high-priority invoices were handled promptly.
3. Accurate Data Extraction from Invoices: One of the biggest challenges in invoice processing is pulling out key information vendor names, amounts, due dates, and payment terms accurately. For this, we employed natural language processing (NLP) tools designed for extracting structured data from documents. This approach was far more reliable than manual entry or generative AI and ensured that every important detail was captured correctly.
4. Automating Invoice Validation and Risk Management: We integrated AI-driven validation and risk management into the invoice workflow. The system flagged any invoices with unusual entries like unexpected price increases or discrepancies in quantity. By comparing current invoices to historical data, we were able to catch errors early, reducing mistakes by 75% and avoiding costly overpayments or disputes with suppliers.
5. Streamlining Approvals with Rule-Based Automation: Not every invoice requires hands-on approval. For low-risk, routine invoices, we implemented rule-based automation that allowed the system to approve them automatically. This sped up the processing time for about 60% of the invoices. For larger, more complex invoices, or those flagged as high-risk, the system sent them to the appropriate personnel for review.
6. Human Oversight for High-Risk Invoices: Even though AI handled the bulk of the work, human expertise was still crucial for reviewing complex or high-risk invoices. To assist with this, we used AI to generate summaries that provided context for these invoices. This gave managers the information they needed to make quick, informed decisions, without having to dig through all the details themselves.
7. Improving Cash Flow Management with Predictive Analytics: By analyzing trends in invoice data, we implemented predictive analytics to help the retailer manage their cash flow better. The system forecasted when large payments would be due and identified potential bottlenecks in the payment process. This helped them optimize their payment schedules and reduce the risk of late payments or penalties.