What is Natural Language Processing in business?

Although we don’t always realize it, natural language processing (NLP) is an integral part of modern business technology. Large Language Models (LLMs), which have become prominent with ChatGPT, are a subset of this broad field.

Natural Language Processing (NLP) – table of contents:

But are ChatGPT or Google Bard the best for all business tasks? Of course not! What then are other business applications of NLP, and how does natural language processing technology benefit business and shape its future?

What is natural language processing?

Natural language processing (NLP) is a technology that enables machines to understand, interpret and generate human language. Its main goal is to allow human-machine communication in natural human speech. To carry on a casual conversation, NLP models must be able to understand context, linguistic nuances and even jokes and sarcasm.

Only large language models (LLMs) can perform these most difficult tasks. Thanks to the vast amount of data they have been trained on, they can understand the subtleties of language and generate answers that are not only technically correct but also sound natural and human.

However, NLP is not just about big language models. Indeed, many of the applications of NLP do not require such powerful tools. If AI is processing credit applications, its language skills don’t need to be great. All it needs is to learn how to search through various types of templates and forms and find the fields in them that contain the necessary data. Such models are much smaller, simpler and require less computing power than LLM.

Why does your company need NLP?

Your company needs NLP first and foremost so that it can be managed based on data, and so that your employees don’t have to do necessary but simple and repetitive tasks and can focus more on the important tasks. But what specifically can artificial intelligence do for you?

Listen to your customers. Analyze the tone and content of statements

NLP enables a better understanding of customers by analyzing texts published on social media. Sentiment analysis and social listening, one application of NLP, helps companies understand what customers think about their products or services. For this purpose, you can try the following tools: Sentione, Brand24 or Hootsuite.

Don’t waste time searching. Finding information in scanned documents

Although soon all company documents will have to be digital, there are still many companies sending out paper invoices and collecting fading receipts. Therefore, the second area where NLP can help is in finding information in company documents. An important part of machine understanding of what has been scanned is to distinguish relevant from irrelevant data. That is, recognizing essential information from, for example, the branding of the company that sent the document or accidental distortions.

The recognized documents, or the information read from them, are then transferred to a digital database. In this way, they are very easy to find. What’s more, they can provide input for further actions, for example:

Respond quickly to threats and detect anomalies.

Language analysis can identify disturbing patterns that may indicate potential fraud or attacks. For example, a bank can monitor conversations to detect attempts to defraud customers, and your company can notice unusual occurrences. Other similar examples include:

Benefit from the experience of others. Knowledge management

NLP can also contribute to better knowledge management in the organization by automatically creating meeting summaries and notes. This way, information is more easily accessible to all team members. Also, searching company documents on the intranet, the product knowledge base, or finding all purchases and documents related to a single customer can be surprisingly easy using NLP.

Skip repetitive steps. Automate natural language processing of documents

Natural language processing makes it possible to automate tedious tasks such as document processing, leading to time savings and increased productivity.

This is because automatic document processing primarily saves time and relieves employees from performing tedious and repetitive tasks that require high precision.

Let’s start with the simple transcription of data from paper documents into customer service programs. It may mean many hours of moving your eyes from a black-and-white table to a monitor screen, or it may be limited to putting a paper contract into a scanner and possibly handling ambiguities and exceptions.

However, automation in natural language processing is not just about handling written documents. AI can, using speech recognition, (STT), speech-to-text systems, create meeting summaries and notes, as they do, among others: Otter, Rev, or Descript.

Areas of application of AI and NLP in business

AI and natural language processing have many applications in business. Popular uses of these technologies in business are shown in the table below:

Type of input data	Examples of AI & NLP applications
Fixed written documents	Processing of insurance applications
Fixed written documents	Personalized automated mail handling
Fixed spoken language	Automatic creation of subtitles for movies

	Creating bibliographic suggestions
Vivid written language	Chatbots on e-commerce sites
	Social media content moderation
Live spoken language	Voice machine operation
	Therapeutic voicebots
Many written languages	Automatic localization of mobile applications
Many spoken languages	Synchronous translation of international conferences

Natural language processing in the future

Natural language processing (NLP) and artificial intelligence (AI) bring many benefits to business, from automation and increased efficiency to better understanding of customers, to creating natural user interfaces and knowledge management. These technologies are not only crucial to how companies operate today, but also have great potential for the future, opening up new opportunities for innovation and growth.

The future of natural language processing looks promising. It is marked by the unbelievably rapid development of LLMs, which are increasingly powerful and use multimodal solutions, that is, they learn to understand images and sound.

As a result, the technology is likely to become increasingly advanced, enabling machines to understand and generate human language even better. Given the achievements of researchers at Stanford University, who are successfully experimenting with digital agents that autonomously learn the language in a digital environment to accomplish their goals – the future of NLP looks bright and fascinating.

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Author: Robert Whitney
JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.
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