AI, on the other hand, has captured most of our attention in the last few years, given the business impact it is having on organizations and the new wave of generative AI (GenAI) which has again excited leadership as they cautiously look at ways it can be utilized in different facets of the business.

It is not surprising given AI encompasses a broader range of technologies, including machine learning, natural language processing (NLP) and computer vision, that take intelligent automation to the next level going well beyond what RPA bots can perform. In fact, Gartner research states “90% of robotic process automation (RPA) vendors will offer generative-AI-assisted automation by 2025” (Gartner: Magic Quadrant for Robotic Process Automation).

We could draw distinct lines in an AI vs. RPA side-by-side comparison, showing the pros and cons of both, but that viewpoint is very limiting and constrains the potential combined value. The best approach is an open discussion on the investments enterprises have made into RPA, redefining the work between humans and machines, and detailing a multi-year strategic plan that outlines corporate business goals and use of combined technologies to deliver the biggest impact to the business.

If we consider all the ways in which bots are used today, everything from manipulating and inputting data into systems like ERP, CRM, ECM, EHR and more, accessing and moving financial data between systems and spreadsheets and creating a better onboarding experience for employees and customers, software bots have proven technically powerful at delivering significant business impact with quicker time to value. Early on, enterprises laid the foundation making sure their RPA strategy covered all the bases, from identifying the right use cases, design and implementation, and planning for how they would scale and support the deployment of thousands of bots.

RPA failed early on when organizations attempted to use bots to handle processes that involved highly unstructured data, especially with document extraction.

Enterprise RPA strategies quickly adapted and started to include intelligent document processing (IDP) which could handle the processing of unstructured data coming from images, PDFs and emails. Business processes like AP automation, legal contract reviews, supply chain logistics and onboarding employees or customers were ripe for new technology and approaches to automation. IDP proved to be the next key pillar to a digital transformation strategy, giving intelligent automation teams the technology they needed to read, extract and organize meaningful information from all documents.

The core of IDP is all about AI, specifically the historic use of optical character recognition (OCR), natural language processing (NLP) and machine learning technologies. This natural progression of RPA into processing unstructured data meant IDP solutions would go the same way as RPA, introducing low code / no code offerings that utilized machine learning and introduced pre-trained models to understand documents right out of the box. This gave enterprise automation teams exposure to AI but did not require individuals to have deep domain expertise with AI skill sets.

The ability to process unstructured data is a key measurement of how relevant the use of RPA is today. Taking RPA’s ability to automate simple tasks and processes and combining it with AI to incorporate understanding and learning capabilities has continued to make RPA platforms relevant especially when it involves document processes.

Still, some may be skeptical about whether RPA platforms can truly remain relevant as part of an enterprise automation strategy. RPA will likely retain its relevance given the technology provides key support functionality to make AI work more smoothly including cleansing AI data, bridging gaps with legacy systems, incorporating humans into the process, performing simple tasks where AI is not even required, and even monitoring activities involving AI.

With the latest wave of AI embracing GenAI and large language models (LLM), the combined RPA and AI approach offers great promise to advancing AI agents, ones that act on behalf of an individual, making rational decisions based on data and are always learning to produce optimal results and performance. The potential benefits are unlimited when we consider an AI agent could engage with employees and customers and participate in critical business decisions just like humans do.

Consider common use cases like analyzing and auditing financial documents, understanding insurance claims and underwriting reports, or summarizing and comparing legal contracts. These types of document processes are extraordinarily complex data driven tasks that rely on complex data structures in documents, but GenAI now opens the door to quicker time to implement along with more accurate information.

The RPA platform provides a solid foundation for these AI agents by using the RPA platform architectures designed to operate thousands of bot automations. This will be necessary as humans and AI agents work independently or together. Consider an intelligent document process could extract the data from a set of documents, and then a human steps into not only review, but interact with the information to derive additional insight from the data.

The pace at which GenAI is moving is incredibly fast. What was not possible a year ago is now possible today, and six months from now we will be having a new conversation. One of the biggest challenges to GenAI is ensuring trust and data security around the services. This is a very similar challenge that RPA platforms faced when it came to managing human credentials that bots use to access systems. GenAI takes the challenge to an entirely new level, where we can imagine oversight of these AI agents will require knowing what systems and data was used by the AI agents or monitoring behavioral changes that occur in responses to data. Effective application management of a dynamic digital workforce growing in complexity will be required, to provide oversight of not just the RPA and AI micro-automations, but all the systems and data being accessed and processed.

The business benefits achieved from the combination of AI and RPA are solid. Individually RPA and AI solve different problems, but when combined it becomes transformative. In particular, the strengths perform well when automating complex unstructured document processes (hard to reach data), an area where there is no shortage of opportunity and business upside to automating the extraction of data that drives insight and better business decisions.

Furthermore, the solid platform foundation by which RPA comes from to quickly automate existing work patterns through UI integration has a tremendous upside to AI agents that also require access to vast amounts of information to perform more advanced cognitive tasks.