4 Key Challenges to Adopting NLP with RPA?
Business operations can be categorized into structured and unstructured workflows. Although structured workflows have benefited from robotic process automation (RPA), there still remains a significant challenge in reducing the numerous unstructured manual procedures that exist within an organization. The integration of natural language processing (NLP) in RPA can potentially strengthen the automation strategy in these cases.
Robotic process automation (RPA) and artificial intelligence (AI) are powerful technologies that offer automation and optimization capabilities across various business processes, ranging from data entry to customer service. However, their implementation comes with challenges and risks that necessitate careful planning, management, and governance.
Achieving an excellent user experience and maximizing returns throughout the automation process have become crucial aspects of RPA. However, the question arises as to whether this is feasible for the majority of daily business operations conducted by users. While RPA developers have long been able to automate end-to-end procedures, the technological advancements in NLP now provide them with the confidence and precision to extend automation to the most complex and secretive priming processes.
Natural language processing (NLP) and robotic process automation (RPA) are increasingly being used by businesses because of the variety of application cases. Take a look at the following statistics:
- 19% of respondents said they planned to use RPA in the next two years, and 53% of associations had already begun their RPA journey.
- The global NLP market is estimated to reach 43B by 2025.
Leading RPA merchandisers have begun to incorporate AI features into their automation platforms. These result in intelligent bots with decision-making abilities that enable process automation from beginning to end. NLP is one of the AI features that gives RPA bots more intelligence.
What is Robotic Process Automation (RPA)?
Robotic process automation (RPA) is the term used to describe the automation of routine, rule-based business operations tasks, many of which involve data processing or routing. By allowing employees to concentrate on high-value tasks, RPA lowers costs and boosts profitability for businesses. RPA tools are used by businesses to configure automation, also referred to as "robots." RPA bots come in three varieties: attended, unattended, and hybrid.
Similar to grading a support ticket, each RPA bot may independently do only one task. Additionally, numerous robots may work together to automate a more complicated procedure, such as extracting information from a scanned bill, analysing it to determine the roles and values involved, and then automatically submitting a transaction from accounts payable.
What is Natural Language Processing – NLP in RPA?
Computers can better understand human language by using techniques from the field of computer science known as natural language processing (NLP). First off, low-position text analytics functions dissect expressions into their component pieces, such as parts of speech. Additionally, NLP models extract meaning from those fragments, much as they would when evaluating their sentiment, categorising them into buckets, or identifying them as named individuals.
Structured, unstructured, and semi-structured documents are all subject to natural language processing in RPA in order to recognise, extract, and organise data for additional analysis. Structured and semi-structured document processing and unstructured document use cases are the two categories into which NLP operations in RPA fall.
The use cases for structured and semi-structured documents are covered by the first category of NLP operations for RPA. These therefore include handling insurance claims, processing invoices, and contract analysis. Businesses can speed up and automate business operations with the use of NLP for structured and semi-structured document processing. Additionally, it lowers expenses by allowing employees to focus on higher-value jobs.
RPA application cases for unstructured documents are more in line with conventional organisations that value the opinions of their customers and employees. Additionally, the automation features NLP for numerous workforce analytics tasks, support ticket categorization, and client review analyses. By enhancing customer experiences, boosting employee engagement, and directing product roadmaps, NLP for VoC and VoE process automation aids businesses in maximising profits.
What are the Key Challenges to adopting NLP with RPA?
Here are the 4 key challenges to adopting NLP with RPA:
- Language complexity and diversity:
Natural Language Processing (NLP) requires understanding of different languages and dialects, including regional variations, slangs, idioms, and nuances. This makes it challenging for bots to accurately interpret natural language input.
In addition, translating these linguistic features into machine-understandable code is a challenging task. Different languages have different rules, structures, and syntax, further complicating the process of creating effective NLP algorithms that can accurately analyze and understand text.
As a result, developing NLP models that can effectively handle language complexity and diversity is a major challenge. It requires significant advances in language processing technology and a deep understanding of the nuances of human language. Therefore, RPA developers must account for these challenges and implement effective solutions that focus on data pre-processing and feature extraction to ensure accurate results.
- Data quality and quantity:
Data quality and quantity are major challenges for adopting NLP with RPA. Poor data quality can lead to inaccurate results, while insufficient data quantity can render the model ineffective.
NLP with RPA requires a large amount of high-quality data to train and fine-tune machine learning models. This data must be well-labeled and relevant to the task at hand. If the data is noisy, inconsistent or incomplete it can lead to errors in the output, impacting the accuracy of the results. Furthermore, NLP models require a considerable amount of data for them to generalize well beyond the training data and produce reliable predictions. Inadequate data quantities reduce the effectiveness and the generalizability of NLP models.
Therefore, the availability of high-quality and sufficient data sets is fundamental in NLP with RPA. Organizations need to invest resources into collecting and preparing the required data in order to maximize the effectiveness and efficiency of NLP with RPA solutions.
- Personalization:
Personalization of NLP models refers to the practice of tailoring the model to a specific user or a use case. While personalization can bring accuracy benefits, it can also be challenging when implementing NLP with Robotic Process Automation (RPA). One challenge is that personalization requires large amounts of training data, which can be time-consuming and expensive to acquire. Additionally, personalization may make the NLP model less flexible and harder to integrate with existing RPA systems. Finally, personalization can lead to privacy concerns if sensitive data is being used to train the model. Therefore, it's important to weigh the benefits and drawbacks carefully when considering personalization for NLP models used in RPA.
- Integration and flexibility:
Integrating NLP with Robotic Process Automation (RPA) requires flexibility to adapt to new languages, technologies, and use-cases. Bots must be easy to retrain or update, with an ability to adjust to fluctuations in input data and user interactions.
Integrating NLP with RPA can be complex due to differences in technology and data structures, which can make it difficult to transfer information between the two systems. RPA tools are often built to perform specific tasks and workflows. So, when integrating with NLP, they may not be flexible enough to adapt to changing business needs or variations in language use.
Ending Note
While NLP and RPA technologies offer significant benefits for businesses, adopting them together presents several key challenges. Overall, adopting NLP with RPA requires careful planning and execution to address these challenges and realize the benefits of both technologies. To overcome these challenges, businesses should work closely with technology vendors and subject experts to ensure a smooth adoption while mitigating potential risks.