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Enterprise 2.0: Digitizing AP Invoice Processing Workflows with AI

Enterprise 2.0: Digitizing AP Invoice Processing Workflows with AI

December 3, 2020

AI is opening a vast landscape of opportunities with immense growth potential for modern-day businesses. AI-powered automated processes harness the power of automation, flanked by AI to build game-changing resilient business workflows for the enterprise of the future. It also paves the way for Enterprise 2.0: that drives faster task resolution rate, cost reduction, and turbocharges productivity.

The first step for a successful digital transformation journey is to identify silos across processes; silos are mostly formed by employees engaging in time-consuming manual activities. Subsequently, evaluating the possibility of automating the manual processes is also required.

An accounts payable workflow is also plagued by siloes, where employees often spend much of their time manually processing documents and then entering the data into the enterprise storage system.

The silo’ed operational mentality impacts operations by hampering employee morale and adversely affects productivity. It often ends up contributing to the overall failure of a company or its products and culture.

To overcome these silos, many organizations have adopted automation and document capture technology to automatically extract data from invoices and feed it into the AP workflow. Then eventually, publishing the data into the enterprise's system of record. This approach is plagued with multiple operational challenges and has not yet delivered on the promised ROI.

Challenges with the traditional data extraction technology

Traditional OCR technology uses templates for extraction, which is why the process witnesses very low extraction accuracy; this also leads to many unwanted errors. Operators also need to create templates based on invoice structure to extract the data successfully. The template creation process is painful and takes a lot of time. Traditional OCR solutions struggle to extract data from handwritten invoices; as a result, extraction accuracy is highly affected, and the solution cannot be trusted.

AP teams process numerous invoices from various vendors; manually creating new templates to accommodate every new invoice structure witnessed in the system is an inefficient approach towards digitization goals. Low data extraction accuracy and the painstaking manual template creation process are significant bottlenecks for any AP team.

AI powered invoice processing

The new generation of automated invoice processing technology uses AI and machine learning models to extract data from invoices. It ensures high extraction accuracy and eliminates the painful template creation process. It can intelligently recognize the invoice's overall structure to extract all the meaningful field and line item data. Advanced machine learning technology helps the system to identify specific patterns in the invoice as well.

Using AI for invoice processing frees up agent time as extraction accuracy is increased, and manual creation of templates becomes unnecessary. It contributes to an increase in AP team productivity, saves cost and time.

AI-powered smart decision making

AI’s biggest strength is its ability to provide an automated stream of intelligence across the Account Payable workflow. It helps the AP teams to align their operations based on changing business requirements.

AP process owners can now have the holistic visibility of their workflow. Invoices are seamlessly routed to approvers powered by AI. It also helps to enforce robust monitoring and compliance standards across the invoice processing cycle.

Building an AI-powered invoice processing workflow helps AP process owners make better decisions. It provides practitioners with real-time visibility across process health, document pipeline, error rates, staff productivity, accruals and liabilities, and other critical key performance indicators. AI empowers process owners to quickly identify bottlenecks and fastens exception handling by identifying the root cause and paving the way for faster resolutions.

About the author

Aritro Chatterjee, Product Management, Kanverse.ai

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