5 Ways NLP & RPA Enable Intelligent Automation in 2024

Due to their wide range of use cases, robotic process automation (RPA) and natural language processing (NLP) are widely adopted in enterprises:

• 53% of organizations have already started their RPA journey and a further 19% of respondents plan to adopt RPA in the next two years.
• The global NLP market is expected to reach ~$43B by 2025.

Leading RPA vendors have started to integrate AI capabilities into their automation tools to create intelligent bots that allow end-to-end process automation with decision-making capabilities. NLP is one of the key AI capabilities that add intelligence to RPA bots. 

In this article, we’ll explore five ways that NLP enables intelligent automation in the enterprise.

1. Extracting data from unstructured sources

Structured data, such as financial records or customer databases, is relatively easy to access and analyze. However, between 80%¹ to 90% of business data comes from unstructured sources such as:

• Documents including invoices, contracts, reports, order forms, etc.,
• Emails,
• Customer feedback,
• Social media posts.

These data sources can provide a wealth of valuable insights for businesses, but only 18%² of organizations take advantage of them.

Intelligent RPA bots with NLP capabilities can understand natural language and:

• Connect to different enterprise applications that generate unstructured data,
• Understand the context of the data source,
• Extract and validate relevant data and convert it into a structured format.

For example, for invoice processing, an intelligent bot can monitor incoming invoices and flag the ones that are not received via the company’s electronic data interchange (EDI). Then, it can leverage NLP to extract relevant data such as bank account, date, or ordered item, and record extracted information in company systems.

2. Generating reports

Timely and accurate reporting is essential for all businesses, but the process can be time-consuming and repetitive. For instance, more than 50%³ of report delivery departments have to deliver the same set of data several times, and 50% of managers are not satisfied with the speed of delivery.

NLP-powered intelligent bots can:

• Log in to company systems and collect structured and unstructured data to be included in the report,
• Input collected data into designated documents such as spreadsheets or database tables, and perform necessary manipulations to data,
• Generate reports in the required format, save them, and send them to relevant staff.

Report automation allows employees to focus on more value-added tasks in reporting and reduce manual data entry errors.

3. Providing better customer services

80%⁴ of customers say that they are willing to pay more to companies that provide better customer services and nearly a third of customers state that they would stop interacting with a brand they loved after a single bad experience.

Intelligent automation can improve the way companies manage their customer relations. In addition to natural language understanding (NLU) capability that allows intelligent bots to understand and manipulate human languages, bots with conversational AI capabilities can also leverage natural language generation (NLG) and:

• Assist customer service representatives during customer interactions by retrieving relevant customer information from company systems and providing it to the representatives.
• Provide 24/7 customer service through chatbots that can answer customer queries through text or voice channels.
• Enable self-service customer service with which customers can create their accounts, change account information, schedule and manage appointments, learn about product shipping status, request refunds, and more.

Check our article on customer service chatbots for more.

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4. Analyzing customer sentiment for marketing & customer service

Sentiment analysis is a subset of NLP that deals with understanding the negative, positive, or neutral attitude in a text or audio. More than half of companies stated that they have adopted technologies that analyze customer sentiment, and it is expected to exceed 80% in 2023. 

Using sentiment analysis, companies can measure customer satisfaction, monitor the reputation of their brand, and improve their products and services. NLP-powered intelligent bots can:

• Monitor user feedback across different channels such as social media platforms, emails, forms on the company website, review platforms, or customer service calls,
• Conduct sentiment analysis to measure customer sentiment about a product, service, or the brand as a whole,
• Generate customer sentiment reports that present overall positivity score, word cloud of the most used words in customer feedback, or more detailed analyses.

5. Classifying information

In addition to classifying customer sentiment, NLP allows businesses to categorize unstructured information containing natural languages into other predefined groups. 

Considering that most business data is unstructured and text data can be messy, structuring it into useful groups can provide valuable insights to businesses. Use cases of text classification with intelligent bots include:

• Spam detection: NLP models can determine whether emails, text messages, or messages from social media platforms are spam and send them to the inbox or spam folder by analyzing their content.
• Resume evaluation: NLP-driven intelligent bots can extract relevant information like education, skills, and previous roles from a candidate’s resume, and match candidates to a position based on their profile.
• Intent classification: Intelligent bots can understand the intent of customer feedback and send a notification to relevant staff. For instance, if a potential customer sends an email that says, “The software looks pretty cool”, bots can categorize the intent as “Interested” and direct the email to the sales department (Figure 2).
• Support ticket routing: Similarly, intelligent bots can route customer support tickets to relevant staff according to their content.
• Language detection: Bots can detect the language of a text and translate it into the user’s native language or route the document to the appropriate team.

If you have questions about NLP, RPA, or intelligent automation, feel free to ask:

External Links

• 1. Harbert, Tem. Feb 1, 2021. “Tapping the power of unstructured data“. MIT Sloan. Retrieved Nov 9, 2022.
• 2. Davenport, Tom; Guszcza, Jim; Smith, Tim; Stiller, Ben. Jul 25, 2019. “Analytics and AI-driven enterprises thrive in the Age of With“. Deloitte. Retrieved Nov 9, 2022.
• 3. “How to meet top management reporting expectations?” (PDF). Deloitte. 2016. Retrieved Nov 9, 2022.
• 4. Amaresan, Swetha. Aug 29, 2022. “Why Customer Service is Important: 16 Data-Backed Facts to Know“. HubSpot. Retrieved Nov 9, 2022.