Thirty years ago, the promise of an automated future began with the re-branding of accounting systems into something vendors began calling ERP (enterprise resource planning) systems. At the time, the re-positioning of accounting systems as a system that centralized data for all departments and processes within an enterprise — including produce, deliver and account for customer orders, was seen as the cure for data and process blindness within organizations. While this represented a milestone in how companies valued, managed and leveraged data throughout their organization, it also resulted in a lot of unintended consequences, leading to unanticipated amounts of internal IT/development/operational overhead, along with all sorts of bottlenecks to accessing and making use of business data in a timely fashion.
As a result, rather than focusing on business results and how to achieve them, IT departments, finance departments and anyone else supporting data-driven processes, spend tons of effort and plenty of dollars just trying to get the data right, and to maintain the systems intended to secure that data.
The next milestone in the evolution of ERP systems was the transformation from on-premise systems and proprietary integrations, to centralized, cloud-hosted systems. The promise here was that putting these systems in the cloud would simplify upgrades, deliver greater ease of use, cost efficiency and scalability.
In many cases, cloud ERPs have delivered on those value propositions, and companies have benefited from the transition. But unfortunately, the new model didn’t address the bigger issue of access to accurate, timely data in support of optimizing a business process.
Jumping ahead a few innovation cycles, this brings us to one of the most recent business tech milestones: robotic process automation (RPA).
RPA, which came to prominence within the last decade, represents a new approach to solving the problems of IT overhead when it comes to process automation. The goal is to relieve the burdens related to automating business processes by making them easy enough to configure, implement and modify without a lot of heavy lifting from IT. Instead, the power is put in the hands of the process owner.
Except that there’s still a problem. RPA doesn’t address the original problem of timely access to business data, without a lot of cost and effort across the board — including the onerous, manual tasks of converting data into a format that an ERP system (on-prem or cloud) can manage.
Up until the last decade or so, ALL of these approaches dodged one central issue: how can we manage and make use of our business data if we rely on our employees to manual key in information from emails, documents and other unstructured data sources, and require departments full of IT people and developers to maintain these systems?
According to Gartner, only 96% of organizations claim to have a digital initiative to transform and automate their business processes. Unfortunately, MOST of those organizations are focusing on automation, but not on process intelligence. And up until recently, ALL automated processes were essentially unintelligent and robotic, driven by a fairly rigid set of workflow rules. Which is to say that they could reliably make any process go faster, whether that process made sense or not. Adjusting, adapting and evolving those automated processes to deal with exceptions and errors either required on-going monitoring and intervention from a process owner — or recoding and reconfiguration by IT/development staff.
This is why up until recently, business process automation hasn’t fully delivered on its promise. First, the assumptions from the ERP world was that most business data was structured. It isn’t. Second, workflow automation systems assumed that workflow rules could handle enough of a process to cost justify the expense and effort. It might — sometimes, but only for high-volume, high-velocity processes like accounts payable, and even then, exceptions inflate the cost/effort leading to sub-optimized ROI.
But today, intelligent process automation addresses the issues that data management and rules-driven, unintelligent automation cannot. Leveraging machine learning, intelligent automation solutions adapt to exceptions with one-time guidance from a process owner, without the need for IT intervention. These systems adapt by modifying their algorithms after recording the actions of a process owner behind a mouse and keyboard.
With this kind of adaptability, it becomes easier and more cost effective to extract data intelligently from business documents, relieving staff from data entry duty. That is a huge hurdle to overcome, and doing so in a way that is flexible and adaptable is huge. From there, automating routing, approval and ERP transaction entry is practically icing on the cake.
There’s certainly a lot of hype out there when it comes to the promise of artificial intelligence and process automation. But when you look at it from the perspective of what companies have been trying to achieve for decades without the kinds of tools and techniques that are practical and available today, we really are witnessing a next wave of disruptive innovation.
To learn more about how to transform your business processes — adding intelligence instead of just digitizing your analog world, contact Artsyl Technologies and request a demonstration of the ActionSuite of intelligent process automation applications for accounts payable, accounts receivable, claims management and more.