How Machine Learning is Changing the Way the Back Office Works

Intelligent Automation Elevates the Role of the Back Office as a Source of Innovation and Insight

Few companies have come to understand just how profoundly new technology approaches to process automation are changing the game when it comes to automating business processes—particularly those that impact high-volume financial processes like accounts payable and accounts receivable. Advances in AI, though more specifically in machine learning, have made the process of configuring workflows, creating definitions to classify documents and extract data, and supporting integrations to ERP/ECM/other business systems far simpler and more adaptable to changing business conditions and exceptions.

Intelligent process automation that leverages innovations like machine learning, low code development techniques and integration platforms as a service promise to deliver the proven benefits of automation in general, including increased productivity, tighter cycle times and greater process visibility. They also offer the potential to tap into deeper insights into business performance, thanks to more timely access to reliable business data extracted from invoices and sales orders.

What’s new and different is that these benefits come with a much lower operation cost and with far less IT/Development overhead. In practical terms, this makes automating AP vendor invoices or AR customer orders a no-brainer. But is also means that beyond this low-hanging fruit, companies can extend the capabilities of IPA technology platforms and apply the experience gained by automating well understood, high volume transaction process to other lower volume, but high value processes where data, documents and decisions are involved.

So, what’s new is NOT the promise of automation. It’s the promise of automation that can be applied more easily and more flexibly to a far broader range of business problems in creative, employee-empowering ways.

Power to the Process Owner

A recent McKinsey report validates what has been born out in the marketplace when it comes to intelligent process automation: predictable manual processes, especially data processing, lend themselves best to the kinds of automation innovations we are seeing today thanks to AI, machine learning and low code development. The cost and effort associated specifically with sorting through documents to identify the kinds of data they contain and the process they support can be radically reduces using intelligent process automation solutions. Extracting data from those documents and applying business logic to them to route them for approval in a way that is quick and convenient produces the same order of magnitude in cost/effort reduction.

In the past, strictly rules-based automation solutions delivers predictable results and fast ROI, but in a way that passed the buck in terms of cost/effort to IT and development, where business rule-related algorithms needed to be reconfigured or recoded every time an exception occurred to the rules.

Applying Machine Learning to Data Classification and Extraction

Traditional technologies applied to automation processes like accounts payable or accounts receivable relied on optical character recognition (OCR) to digitize paper documents or transform digital files into sources of structured data that could drive a workflow or support matching documents in a procure to pay process.

The success of OCR depends on document quality for the system to be able to parse through things. A poor-quality document, or a non-standard document format, can produce understandably poor quality or less than reliable results. When the system fails to recognize characters, or fails to extract data at all, manual correction is required by process owners, raising the costs and complexity of the process. A system that achieves 80% efficiency in this regard is still 20% inefficient—not only leaving room for improvement, but money on the table. When it comes to high volume processes, that 20% represents a lot of work and still consume a good deal of time for process owners. And data extraction is just one example of the kinds of exceptions that can be encountered in any automated workflow.

To address these kinds of errors and exceptions in the past, technical personnel had to reconfigure workflows or create customized templates specific to a document type so that the system knew where to look for the right information in a document—or know where to look to validate a word or amount based on other data sources like an existing ERP vendor record.

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To address this problem in a more scalable way, machine learning-enhanced systems record input/corrections conducted manually by process owners, then analyzes them for patterns. Once it has keyed into a pattern and defined a repeatable approach to resolve a common error or exception, the system can modify its own algorithm and “learn” how to deal with the problem on its own, without manual user guidance.

Unlike humans, intelligent process automation, doesn’t make the same mistake twice. As the system learns, it becomes more accurate and improving continuously.

In a recent Gartner report, the analyst firm predicted that within two to five years machine learning and IPA platforms or process automtion will have reached widespread mainstream adoption. Over the next decade, the firm predicts that IPA will give rise to a new, highly disruptive class of technologies that will allow companies to adapt more rapidly and anticipate trends pro-actively.

As the speed of business accelerates, companies who fail to embrace intelligent process automation will be left behind and will be forced to play catch up in a hurry.

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