How do you automate invoice processing with RPA and AI?

Repetitive work can be automated by RPA like the invoice needs to be read and data needs to be fed in a desired web portal...but combining AI would come into picture if the invoice is unstructured and can be available in more than 10 different formats... AI is self-learner which can enhance the Automation solution.

The RPA delivers the processing for accessing documents (e.g. Email attachments). The individual documents are passed to the AI functionality which has been configure to understand the structure of invoices namely, invoice header (e.g. Supplier name, invoice date), the invoice lines and the invoice footer (e.g. Totals). The AI uses its knowledge of patterns to cope with the different invoice layouts, etc. The RPA takes the data extracted by AI and performs the additional logic like PO matching, posting the data into the Accounts Payable system, plus handling of exceptions (e.g. unknown supplier). It is combination that uses the strengths of both technologies.

RPA (Robotic Process Automation) is software automation that performs rule-based, repetitive tasks, while AI (Artificial Intelligence) is technology that enables machines to simulate human intelligence, including tasks like learning, problem-solving, and decision-making. RPA streamlines processes, while AI adds cognitive abilities like natural language understanding and image recognition to automate more complex tasks.

It is essential to go beyond just automating invoice processing; one must also meticulously examine the existing manual workflow and enhance it through Robotic Process Automation (RPA). Particular emphasis should be placed on the project's design phase, as a well-considered design plays a central role in the project's overall success. This design phase determines the efficiency with which the automation system will function. It is imperative to take into account aspects like workflow, exception management, and user interfaces when crafting the design.

Process mining techniques can be used to understand the as-is procession more detail free from human bias, assuming the process is stable enough. Process simulation techniques can be used to understand the as-is and to-be process flows. Before automating any business process, it is prudent to ensure requisite data is present and correct. For example, the approval hierarchy and contact details for approvers must be accurately maintained for the system to work autonomously without error. This is likely an HR process; HR must be consulted as a stakeholder during software design. Therefore, as with many automated processes, it is wise to collaborate cross-functionally.

To design an invoice processing automation solution, identify key requirements, select suitable RPA tools, and integrate them with AI components for data extraction and validation. Define workflows, create templates for various invoice formats, and implement exception handling. Test rigorously, monitor performance, and continuously improve the system for efficiency and accuracy.

Now a days numerous Intelligent Document Processing (IDP) vendors offer advanced AI and ML capabilities for document processing. Therefore, it is imperative to exercise caution when selecting the ideal IDP platform prior to commencing the project. It is advisable to conduct thorough Proof of Concepts (POCs) to ensure that all stakeholders have a clear understanding of the extent to which AI is employed in document automation. This approach helps in establishing accurate expectations for the project.

Implementing an invoice processing automation solution involves configuring RPA bots to receive, extract, and validate invoice data, then route it for approval and payment. Develop APIs or connectors for system integration, set up exception handling, and deploy in a controlled environment. Continuously monitor, evaluate, and adjust the solution for optimal performance and compliance.

Optimize invoice processing automation by refining data extraction algorithms, enhancing exception handling with AI, and reducing false positives. Implement machine learning for smarter decision-making, monitor performance metrics, and continuously refine processes. Regularly update templates for varying invoice formats and leverage analytics to identify bottlenecks and streamline workflows for efficiency gains.

In my experience, the invoice processing project has yielded exceptionally positive results, with a well-refined invoice automation process that led to substantial cost savings in human resources and the elimination of labor-intensive manual tasks. This optimized workflow significantly diminished errors and empowered the team to focus on addressing complex invoices that were not easily automated, while straightforward invoices continued to be processed automatically without interruption. In summary, the project efficiently met its goals and provided valuable advantages to the organization.

I my experience the work of a team of 25 can be undertaken almost entirely by a single automated intervention. This can save an organisation in the region of ￡750k PA. It is worth noting that automated interventions are unlikely to be able to operate completely autonomously and it would be advisable to have at least one process expert on hand to oversee the processing.

Automating invoice processing with RPA and AI enhances accuracy, reduces manual errors, speeds up invoice approval, lowers operational costs, and ensures compliance. AI improves data extraction, handling exceptions, and enables intelligent decision-making, while RPA streamlines workflows, leading to increased productivity and enhanced financial management.