Advanced applications of RPA have moved beyond targeted single process automation to an end-to-end business process management approach. This companywide approach takes a holistic view of how you apply and integrate RPA with other technologies across the business. These include generative AI, machine learning (ML), and natural language processing (NLP) among others. Although incremental implementation will still be the norm, each process will be part of a broader roadmap to creating businesses driven by intelligent automation, which I’ll explain later.
Past blogs have talked about RPA’s future from the standpoint of digital transformation, hyperautomation, low code, NLP, and integration with BPM and data analytics. This time, we’ll look at advanced applications of RPA in business processes and its limitless potential with AI and cloud.
Generative AI and RPA are already playing a major role in the next level of automation. Every sector can see the benefits of handling complex tasks derived from unstructured data. This is a fundamental principle of intelligent automation where predictive analytics drives:
The blue skies of possibility get cloudy due to the complexity of gathering the right data and training large language models (LLMs). This is true from ingestion and preprocessing to training and creating practical production-ready applications. Low code and citizen developers can have a substantial impact on reaching the desired application outcome, but it’s no silver bullet.
There is some clearing of the skies with approaches like RPA marketplaces, cloud-based RPA-as-a-Service (RPAaaS) and RPA for security support. Let’s look at what these advanced RPA techniques bring to the table in furthering a holistic, business-wide process automation approach.
The idea of enhancing operational efficiency and productivity with advanced RPA techniques is just an empty phrase without concrete tools, processes, and outcomes. Here are some of the emerging approaches and technologies that will set the stage for advanced applications of RPA.
Intelligent Process Automation (IPA) combines AI/ML and RPA to automate processes with unstructured data like images and text. What makes it exciting and more practical is it doesn’t need huge training data sets or rule-based decision-making. It starts with optical character recognition (OCR) to make text from unstructured sources into structured machine-readable data suitable for RPA.
This can be vital to every sector with complex processes and stringent regulations where the resulting tools must be capable of continually learning, such as:
RPA and IPA are complimentary processes that can go a long way to bringing operational efficiencies and innovation.
RPA marketplaces are gaining steam as they make pre-built assets available to companies, so they can automate more functionalities across a wider variety of processes. This will enable the creation of an RPA blueprint for near- and long-term automation roadmaps across an organization.
RPA as a Service (RPAaaS) is an emerging model that enables organizations to access RPA capabilities through a cloud-based subscription. This democratizes access to automation by:
Security is a major company-wide concern for SMBs and enterprises. AI, ML, and RPA will play a leading role in mitigating risk and meeting compliance by identifying and providing elimination support for:
RPA is now being used in automation tools known as Security Orchestration Automation and Response (SOAR) products to automate security processes and perform analyses.
More advanced applications of RPA will be possible through greater interconnection and integration with other solutions for business-wide impact. The goal of this approach is to automate more complex tasks while lowering cost, skill, and bot development/testing time. Approaches discussed in this blog will bring greater democratization to the practical use of AI/ML and RPA for SMBs and enterprises.
Like traditional RPA, these emerging approaches must be tailored to each organization’s business outcome needs. Every business and RPA roadmap is different, so you won’t find any statistics or metrics in this blog. The goal is to help prepare decision makes to see the advanced applications of RPA through the lens of long-term planning.
This requires more than just the consultation of an RPA vendor, which often comes much later in a long line of important decisions. Organizations want to make sure RPA plans define the path to achieving near- and long-term business outcomes. The right consulting partner capable of looking at all aspects of the business is the key to helping create a customized, long-term blueprint.
There will always be changes that are necessary as innovative approaches and potential opportunities become evident. Solugenix has built a reputation of being the ideal long-term partner. Our diverse IT services and consulting support help organizations see beyond a single RPA project to achievable, real-world business outcomes that can deliver in ways that matter.
To learn more about how Solugenix can partner with you to realize intelligent automation that drives real business outcomes, click here.