Processing invoices is a fundamental and critical component of business operations. But it’s a slog. Each supplier has its own quirks, each invoice its own nomenclature—one company’s “payment term 15 days” is another’s “payment due in two weeks.” Even if invoices come from the same supplier every month, procurement agents change, formats vary, and typos creep in. And of course, invoices are just the tip of the documentation iceberg. Every day, at every company, at every level of management and operations, employees need to extract details from contracts, leases, tax forms, surveys, and other documents.
The good news? Artificial intelligence (AI) offers ways to perform these complex, integrated tasks far more efficiently. These solutions are seamless and scalable, simple to operate, and easy to manage. Using a variety of innovative AI techniques, organizations can process documents faster and simplify operational procedures; fewer errors mean fewer corrections and retractions. Recent research by PwC on automating analytics found that even the most rudimentary AI-based extraction techniques can save businesses 30–40% of the hours typically spent on such processes.
We all know about the paradigm-changing use of AI for Netflix recommendations, chatbots that impersonate customer service agents online, the dynamic pricing of hotel rooms, and the creation of routes for delivery companies. These efforts are the value creation engines of countless large, successful companies. What we’re talking about here is a decidedly less splashy and, at face value, more pedestrian use of AI—it’s aimed at reducing costs and optimizing operations rather than transforming or creating industries. But this boring AI is actually quite exciting, because it confronts issues that all companies wrestle with, and because the gains in productivity (and hence margins and valuations) are real.
Yet despite its huge potential, PwC’s AI Predictions 2021 found that only 28% of executives have prioritized using AI and machine learning for information extraction, significantly less than for other uses, such as chatbots and solutions for workplace safety. Some leaders are likely overwhelmed by the time and resources required to develop, scale, and integrate these advanced technologies. Some will be hesitant to trust AI or will feel skeptical about its utility. Others may simply be overlooking the value of automated information extraction because it is a back-office function. Regardless of the reason, they are missing an opportunity to streamline processes and improve their return on investment.
The paperwork problem
Any company that audits a client’s books spends an enormous number of hours every year gathering evidence and verifying transactions to confirm that the balances and transactions associated with the client’s financial statements are correct; this is known as a “test of details.” For nearly three decades, workers have used spreadsheets (first Lotus 1-2-3, then Microsoft Excel) as the primary tool to complete the test of details.
Today, the evidence for these audits usually appears in PDF form—invoices, account statements, receipts—and it can run into the thousands of pages. Information residing on these PDFs must be manually entered into the spreadsheet. For a midsized company that processes 100,000 pages of documents annually at three minutes per page, it would take approximately 5,000 person-hours to complete the task; at US$50 per hour, that’s $250,000.
Now, what if the same company could deploy augmented intelligence? This is the term for applications based on adaptive systems powered by machine learning in which the algorithms learn from human experience, but humans make the ultimate decision. The AI tool can “read” text on each of the invoices and use relational data search to quickly identify supporting documentation that the organization had previously tagged as being important—a powerful shortcut when trying to manage millions of invoice exceptions. Even though paper invoices can be unique to each supplier, AI techniques can identify important fields in the different invoices, such as unit cost and quantity, and calculate ledger balances automatically. By implementing an AI solution and assuming the 40% estimate above, the example midsized company could save 2,000 hours for every 100,000 pages processed.