What are the most challenging processes to automate in RPA?

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What are the most challenging processes to automate in RPA?

RPA is a technology that uses software robots (also known as bots) to perform rule-based tasks that were previously done by humans. These bots can be programmed to perform a wide range of tasks, from simple data entry and copy-pasting to complex decision-making processes. Imagine a bot performing all your scary statistics homework. Yes, with RPA it is totally possible.

While RPA has proved to be a game-changer for many businesses, some processes are still challenging to automate. In this blog post, we will discuss the most challenging processes to automate in RPA and explore the reasons behind their complexity.

Unstructured Data Extraction

One of the most challenging processes to automate in RPA is unstructured data extraction. Unstructured data is any data that does not have a specific format, making it difficult for traditional automation tools to interpret. Examples of unstructured data include text from emails, images, and videos. In contrast, structured data has a well-defined format, such as data in spreadsheets and databases.

RPA bots require structured data to perform tasks, making it challenging to automate processes that involve unstructured data. However, recent advancements in Natural Language Processing (NLP) and Computer Vision (CV) have enabled RPA bots to interpret and extract unstructured data more efficiently. Isn’t it amazing to witness the advent of the digital age? Before we used to cry rivers over unstructured data but with RPA advancements, the game has been changed totally.  

Decision-Making Processes

Decision-making processes involve analyzing data, identifying patterns, and making informed decisions. These processes can be challenging to automate as they require a higher level of cognitive ability. While RPA bots can follow pre-defined rules and make simple decisions, they struggle with complex decision-making processes that involve subjective analysis.

However, with the emergence of Artificial Intelligence (AI) and Machine Learning (ML), RPA bots can now learn from data and make more informed decisions. This integration of AI and RPA is known as Intelligent Automation, which can automate complex decision-making processes and improve business outcomes. With this progress, now you can take a back seat and enjoy making precise decisions. Now bots can take your decisions while taking every parameter into consideration.

Legacy Systems Integration

Many organizations still use legacy systems that are not compatible with modern automation tools. Legacy systems are old, outdated software and hardware that may not support the latest technologies. These systems can be challenging to integrate with RPA bots as they require specific protocols and interfaces.

RPA bots need to interact with legacy systems to perform tasks, making it challenging to automate processes that involve legacy systems. However, some RPA vendors offer legacy system integration solutions that can help organizations automate legacy systems. This is however one challenging task and requires keen concentration.

Human Interactions

Many business processes involve human interactions, such as customer service, sales, and support. These interactions can be challenging to automate as they require human-like communication and emotional intelligence. RPA bots can follow pre-defined rules and interact with humans through chatbots and email automation, but they may not be able to handle complex interactions.

However, with the emergence of Natural Language Generation (NLG) and Natural Language Understanding (NLU), RPA bots can now communicate more effectively with humans. These technologies can enable bots to understand human language and respond appropriately, making it easier to automate processes that involve human interactions.

Process Variability

Processes that have a high level of variability can be challenging to automate as they require bots to adapt to different scenarios. For example, a process that involves data entry from multiple sources may have variations in the format, structure, and quality of the data. These variations can make it challenging for bots to automate the process.

However, RPA bots can now adapt to process variability through Cognitive Automation. Cognitive Automation involves using AI and ML to train bots to identify and adapt to different scenarios. With this technology, bots can learn from data and make decisions based on context, making it easier to automate processes that have a high level of variability.

To Sum It Up,

RPA has revolutionized the way businesses perform their tasks. However, there are still several processes that are challenging to automate in RPA. These include unstructured data extraction, decision-making processes, legacy systems integration, human interactions, and process variability. Despite the challenges, recent advancements in AI and ML have enabled RPA bots to overcome some of these obstacles. For example, NLP and CV can help bots interpret and extract unstructured data, while NLG and NLU can improve human interactions. Meanwhile, Cognitive Automation can help bots adapt to process variability and make informed decisions.

In addition, organizations should choose an RPA vendor that offers solutions for these complex processes. Many RPA vendors now offer Intelligent Automation and Cognitive Automation solutions that can help organizations automate complex processes and improve business outcomes.

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