In today’s competitive marketplace, companies need to move more efficiently and effectively than ever before. Customers have come to expect 24/7 support, marketing and sales teams need real-time insights and, of course, cutting costs wherever possible is still essential. This is the precise reason why process automation has become such an integral part of thriving in the digital age.
The Futurum 2018 Digital Transformation Index found that 50% of all companies surveyed ranked robotics and automation as the top focus area for their digital transformation efforts, and with good reason. Removing repetitive processes from employees’ day-to-day responsibilities and streamlining processes frees them to focus on solving problems, serving customers, and doing the things they actually enjoy.
Until now, automation in the workplace has taken place in the form of Robotic Process Automation (RPA), or straight-line automation — a script-based technology in its most rudimentary form, and an artificial intelligence (AI)-driven technology in more modern versions that focuses on driving single-process efficiencies. Because it’s so simple, most current versions of RPA don’t require complex systems or infrastructure integration that more complex AI requires — a bonus for companies with limited AI support.
However, this more simplistic RPA is also found to be somewhat limited, often using screen scraping rather than true AI/machine learning (ML) to deliver UI automation, making the system inflexible — especially with regard to handling modern data center architectures.
This is precisely why RPA adoption and, in some instances, RPA solutions, have been oversold and/or don’t deliver the value promised — costing too much, taking too long to implement, or being too difficult to scale. This is where Intelligent Process Automation (IPA) must become the focus for the enterprise.
IPA is a more advanced type of process improvement that offers greater efficiencies and cost-saving results. It’s a set of technologies — including RPA, machine learning, and AI — that all work together to execute multiple human and automated processes in an ever-changing context.
IPA doesn’t just learn how to do tasks — it continues to learn how to do them better over time, even when working with unrelated software systems, and when scaling throughout an enterprise.
Additionally, IPA is more flexible to dealing with shifting trends in enterprise applications that include agile DevOps, microservices, cloud-native applications, and containers, to name a few. All of these technologies provide companies more flexibility as to where applications are run to optimize user experience, uptime, security, and more.
However, most current RPA solutions are designed for timeless monolithic applications that run on mainframes and perhaps are updated only every few years. Due to the lack of APIs in many of the applications where automation is being used, this is the only way it has worked. But change moving forward is a must.
IPA offers tremendous potential for competitive advantage when companies create a clear roadmap to success. Let’s explore some key considerations for organizations seeking to pursue RPA and, more importantly, migrate toward successful IPA deployments to drive a more efficient workforce.
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