7 Steps to Successfully Implement Intelligent Processing Automation (RPA, Machine Learning, and AI)

Updated: Sep 14

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 for good reasons. Removing repetitive processes and streamlining integral ones free employees from menial work, allowing 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 that focuses on single-process efficiencies. Because most current versions of RPA don’t require the complex systems, or the infrastructure integration, that more complex AI might, it is a bonus for companies with limited AI support.


However, the simplicity of RPA is also found to be somewhat limiting, as it often uses 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.


What is Intelligent Process Automation?


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 learns how to do them better over time, even when working with unrelated software systems, and or when scaled up throughout the enterprise.


Additionally, IPA has the flexibility to deal with shifting trends in enterprise applications that include agile DevOps, micro-services, cloud-native applications, and containers, to name a few. All of these technologies provide companies more leeway as to how and where applications are run, optimizing user experience, up-time, 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 the times are changing, and change with it, we must.


7 considerations for Intelligent Process Automation


IPA offers tremendous competitive advantage when companies create a clear roadmap to success. Let’s explore some key considerations for organizations seeking pursue RPA. And, more importantly, let us discuss how migrating towards successful IPA deployments can result in a more efficient workforce.


Align IPA with your business goals


To fully optimize the power of IPA, you must create a roadmap that links perfectly with your business strategy. Know what you seek to accomplish with IPA and keep that in your mind as you roll out your solution.


Optimize first, IPA next


IPA will have a much greater impact when the processes you plan to automate are already optimized to their fullest. From there, AI and ML will help bring them to the next level.


Go full stack with IPA


One of the biggest mistakes you can make investing in IPA is to use a single aspect of it rather than its full portfolio of capabilities. This requires a great deal of thought at the business, IT, and data science levels. Starting small is alright, but not thinking end to end will be both limiting and wasteful.


Avoid pre-conceived notions of automation


Automation can be wonderful or devastating to company culture. Work to make it the former, rather than the latter. Employees may be concerned about the elimination of jobs, for instance, not realizing that, in many cases, IPA helps them do their jobs better.


Say no to silos


RPA is meant for individual tasks. IPA is intended for processes. Investing in IPA but treating it like RPA will not lead to value-packed outcomes. Instead, think holistically about how to implement IPA to maximize its impact across departments and software to get the most out of your investments and to ensure automation truly adds value.


Make sure it’s capable of omni-channel


IPA solutions need to be scalable for omni-channel applications. They should work via web, text, email, and voice. If omni-channel capabilities aren’t designed into deployments, it will be difficult to fully automate the process serving your teams or customers.


Develop best practices for continued innovation

Implementing IPA is not a one-step application. If the architecture is sound, it can become something you’ll continually expand and scale as your company grows. As such, you’ll need to develop clear standards for management and governance to ensure future projects are fully compatible and aligned with your current IPA strategies.


Rolling out IPA is not a one-and-done process. Continue to audit your systems and work with our experts, as well as your internal IT teams to ensure you are maximizing your automation usage over time.


This blog post is an adaption of an article by Automation Anywhere. To read the original article, click here. https://www.automationanywhere.com/company/blog/rpa-thought-leadership/seven-steps-to-successfully-move-toward-ipa


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