In recent years, robotic process automation (RPA) has become a valuable tool for any organization; 94% of companies have implemented it or plan to do so in the coming years. Reasons for the growing popularity of RPA are not lacking: companies have the opportunity to automate repetitive processes that used to be executed manually.
On the one hand, automation frees up time for human workers; they can focus on more satisfying tasks that require a creative, empathic approach and critical reflection. On the other, repetitive tasks are executed in less time and without errors. All this leads to a higher level of employee satisfaction, significant cost savings, improved productivity, faster digital transformation and many other benefits for companies.
However, to the extent that digital environments continue to grow and evolve, they begin to be evident the limits of robotic process automation. This is where intelligent automation, incorporating technologies like artificial intelligence (AI) and machine learning (ML), comes into play. When these technologies are combined with RPA, digital workers can learn from and collaborate with their human counterparts on the most demanding processes.
doWhere does RPA fall short?
The truth is that RPA solutions have their limitations. They are rule-based, which means that they can only deal with the linear processes for which they have been programmed. They are perfect for tackling predictable and repetitive tasks, as they can complete high-volume actions in a fraction of the time previously needed. However, a software RPA cannot understand more heterogeneous or ambiguous processes.
The RPA will only be able to perform tasks for which it has been programmed: it can navigate pre-drawn paths and execute pre-programmed keystrokes. It can also identify and extract data from systems for which it has been prepared. While its usefulness for performing a wide range of predefined actions is undeniable, its weak point is precisely this: all actions must be predefined.
This is where intelligent automation can help. It encompasses several different AI technologies, such as e.g. eg machine learning (ML), interaction with structured and unstructured data, processing intelligent document and natural language processing (ASR). Combining intelligent automation with the interactive capabilities of RPA results in an automation system capable of processing a much broader range of highly functional tasks.
Is it better to start with RPA before implementing intelligent automation?
RPA is the foundation on which intelligent automation is built, along with AI: a software RPA is the platform that provides AI capabilities to work processes. It is a symbiotic relationship in which everyone wins.
AI provides greater functionality to the RPA platform, necessary to automate increasingly complex tasks, such as: eg interaction with people, decision making, reading, writing and understanding documents. At the same time, an RPA platform favors the incorporation of AI into the company, since it implements it directly in the processes in a structured and simple way.
For example, a banking system that screens transactions for signs of fraud will extract account data to feed into an AI algorithm. The AI engine examines the typical spending patterns of a customer to point out possible irregularities or the financing of illegal activities. Possible anomalies identified have to lead to a series of actions on different fronts: alert the relevant authorities, freeze the transaction, contact internal auditors and notify the client. Intelligent automation allows you to manage all these actions easily, interacting with various systems (both inside and outside the organization) and without the need for programming.
It could be said that the potential of work processes supported by the application of intelligent automation together with AI and RPA is almost infinite.
Is it too late to switch to intelligent automation?
Along with the rest of the technology industry, the world of automation continues to develop rapidly. Investing in a current technology only to find that the demands of the environment have changed after implementing it can be immensely frustrating (and costly). The good news is that RPA and AI are inextricably linked and even if a heavy investment has been made in robotic process automation, it is not too late. Chances are the system you’ve adopted provides you with a solid framework from which to start your journey to intelligent automation.
Moving to intelligent automation will help maintain a competitive advantage that many companies are already capitalizing on. Deloitte’s annual automation survey notes that 73% of the more than 400 organizations surveyed worldwide have started to integrate intelligent automation; that’s a notable jump from 58% the year before. On the other hand, Gartner identifies “hyper-automation” (which includes intelligent automation) as one of the main technological trends strategies of the last three years, as the scalability of intelligent automation and its ability to change the traditional business model to drive productivity, innovation, efficiency and, ultimately, return on investment, contribute to its growing popularity.
It is clear that intelligent automation in companies will continue to gain ground; Those organizations that decide to implement it today are likely to be among the most poised to enjoy continued growth and stability for many years to come.
Signed: Carlota Ortiz, Product Specialist, SS&C Blue Prism
