NLP, or Natural Language Processing, is one of the fastest-growing areas of applying artificial intelligence. Many of the best minds work on how to enable fluent and spontaneous conversations with machines. It is not only about tools that transform speech into written text or the digitization of documents. The stake is understanding the intentions of contextual questions, automatic translation to reflect the author’s style, voice operation of machines, and even self-created texts by artificial intelligence.

Natural Language Processing in business – table of contents:

  1. Introduction
  2. NLP, or what?
  3. The areas of interaction between AI and Natural Language Processing
  4. Summary


Why do systems equipped with artificial intelligence need the ability to understand text and speech? This is the key question that determines the tasks of NLP, i.e. Natural Language Processing from the business point of view. The technological advancement of the entire process depends on the purpose of language processing.

The goals of NLP can be as different as:

  • automatic document processing,
  • social media monitoring,
  • intelligent location of products and services,
  • and support for chatbots and virtual assistants.

But what is NLP all about?

NLP, or what?

Simply put, Natural Language Processing (NLP) must be based on its understanding. However, the complexity of this understanding depends on the intended application of the model.

The most important differences in how an NLP-enabled AI works depend on the form of the input and output data – how we communicate with the AI ​​and how it communicates with us. And also on how unique or natural-sounding results we want to achieve.

If AI deals with the processing of loan applications, its language skills do not have to be great. It is enough that he learns to search various types of templates and forms and find fields containing the necessary data.

If, on the other hand, AI is to learn how to use chat or translation, the task becomes much more complicated. It covers the problem of understanding words, their sets and subsets, but most of all, understanding the relationship between words.

The open academic WordNet is most often used as the starting point for creating language comprehension. It contains the largest clusters of words sorted by meaning in the world. However, as already mentioned, free WordNet is just the starting point for language learning AI.

The second part is one of the many available artificial intelligence models. Some of them can be downloaded – even for free – from the websites of foundations dedicated to creating future solutions. Artificial intelligence learns language by creating networks of parameters, i.e. connections between words and other data. The learning model is called the black box model – not even the creators themselves know how the links created by AI arise and run.

The greatest difficulty is combining these two areas. In other words, teaching AI a language and using it for a specific purpose will be such an important service and profession for the future bot trainer. It will be a person who will not only guide the new artificial intelligence through the process of learning the meanings of words. Most crucially, the bot trainer will teach the AI ​​to respond accurately to linguistic stimuli, to behave appropriately when data is missing – for example, due to poor voice quality – and to make decisions.

The areas of interaction between AI and Natural Language Processing

Although the defining area of ​​NLP’s operation is natural language, a significant difference is whether AI deals with:

  • fixed documents or with a live language processed in real-time,
  • with a spoken or written language, and finally
  • with one national language, or several languages.

Using this distinction grid, we can distinguish the following examples of business applications resulting from the interaction of AI and NLP:

Natural language processing


Natural language processing, or NLP, is one of the most complex branches of modern technology. Most often it is defined as a technology, where we receive both input and output in a natural language – in written or spoken form.

In combination with artificial intelligence, NLP enables natural communication with the digital world, taking into account the nuances of meaning, intentions and emotions, as well as the automation of many tasks, the performance of which often requires the cooperation of various applications or specialists from several fields.

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What is NLP, or Natural Language Processing in business | AI in business #5 robert whitney avatar 1background

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.

AI in business:

  1. Artificial intelligence in business - Introduction
  2. Threats and opportunities of AI in business (part 1)
  3. Threats and opportunities of AI in business (part 2)
  4. AI applications in business - overview
  5. What is NLP, or natural language processing in business
  6. Automatic document processing
  7. AI and social media – what do they say about us?
  8. Automatic translator. Intelligent localization of digital products
  9. AI-assisted text chatbots
  10. The operation and business applications of voicebots
  11. Virtual assistant technology, or how to talk to AI?
  12. Business NLP today and tomorrow
  13. How can artificial intelligence help with BPM?
  14. Will artificial intelligence replace business analysts?
  15. The role of AI in business decision-making
  16. What is Business Intelligence?
  17. Scheduling social media posts. How can AI help?
  18. Automated social media posts
  19. Artificial intelligence in content management
  20. Creative AI of today and tomorrow
  21. Multimodal AI and its applications in business
  22. New interactions. How is AI changing the way we operate devices?
  23. RPA and APIs in a digital company
  24. New services and products operating with AI
  25. The future job market and upcoming professions
  26. Green AI and AI for the Earth
  27. EdTech. Artificial intelligence in education