Natural Language Processing (NLP) is a type of artificial intelligence that enables computers to interpret human language. It assists machines in understanding language in context and generating responses that resemble human language. For example, banks use NLP to extract key information from loan applications, while healthcare providers use it to interpret clinical notes.

It is a subfield of artificial intelligence that focuses on building systems that can perform tasks traditionally requiring human reasoning. NLP focuses on language and allows machines to engage in conversation or process written text more effectively than rule-based systems. It adds a layer of understanding that enables computers to handle natural language inputs, rather than relying on exact instructions.

Modern NLP systems are often built by training machine learning models on large volumes of text or spoken language. The system learns how language is used in context and improves at identifying the intended meaning behind user input. Once trained, it can perform tasks like answering customer service queries or highlighting key insights from a document. 

The NLP market is growing rapidly, with some forecasts estimating a compound annual growth rate (CAGR) of 38.4% through 2034.

How does Natural Language Processing (NLP) work?

Enterprise NLP applications, including virtual banking assistants and multilingual support systems, rely on a multi-stage process. It begins with broad language learning and progresses to task-specific training, testing and live use.

It can be broken down into the following stages:

Pre-training the language model

To build a general-purpose language model, engineers train it on large volumes of raw text. Before training begins, the data needs to be cleaned. 

The first step is tokenization, which splits sentences into smaller parts like words or sub-words. 

Techniques such as stemming and lemmatization then reduce each word to its base form — so “running” and “ran” become “run.” Unimportant words, such as “the” or “and,” are often removed through stop word removal to reduce noise.

It is then transformed into embeddings  — vector-based numerical representations that capture word meaning from context.

During pre-training, the model learns using self-supervised learning — a method where it predicts missing or following words depending on the model type. It also learns grammar patterns. For example, part-of-speech tagging — identifying the grammatical role of each word, such as noun or verb — helps the model label sentence structure. Dependency parsing identifies how words are related in a sentence, such as linking the subject to the verb or the verb to its object.

At this stage, the model has a broad grasp of language but is not yet task-ready.

Fine-tuning for real-world tasks

The model is then refined using smaller, labeled datasets tailored to specific business needs, such as answering questions or detecting tone.

Techniques include named entity recognition — identifying people, places, or dates in text; topic modeling — clustering text into subjects; and coreference resolution — linking different words or phrases that refer to the same entity.

With these refinements in place, the model can identify and extract meaningful information; these are key capabilities in natural language understanding (NLU).

Testing the model before deployment

The fine-tuned model is tested on new data to see if it can generalize rather than memorize. It is evaluated on various tasks. For example, intent classification detects what a user wants, entity recognition involves identifying important words or phrases, and sentiment detection is recognizing tone or emotional state.

Performance is measured using metrics such as accuracy, precision, recal,l and F1 score — a balance of precision and recall to give an overall performance score.

If the model underperforms, engineers may fine-tune it further, retrain it on better data, or adjust internal parameters to improve its output.

Preparing for deployment

Once the model passes its evaluation, it is prepared for integration into a real product or service. Integration marks the beginning of the post-training phase.

The internal parameters — known as model weights — are saved and loaded onto a server. These weights contain everything the model has learned. The model is then hosted on infrastructure with enough memory and processing power to respond to user requests.

An API is set up so that other software, such as websites or customer support systems, can send input to the model and receive a response. At this point, the model is ready for real-world use.

Processing live input and interpreting meaning

After deployment, the NLP model begins handling live input. Spoken language is converted to text, then preprocessed using the same steps as during training. Words are tokenized, simplified through lemmatization or stemming, and converted into embeddings, ensuring the format matches what the model was trained on.

Once the input has been prepared, the NLP system analyzes its structure to uncover meaning. It applies the techniques learned during training  — such as part-of-speech tagging, dependency parsing, coreference resolution, and topic modeling  — to understand how the words relate to one another and the intended meaning behind the user’s message.

By combining these methods, the system builds a structured understanding of the message. Rather than relying on keywords alone, it considers grammar and context to determine the appropriate response or action.

Generating and delivering a response

Based on its interpretation, the model predicts a relevant response. It could return an answer, suggest a next step, or trigger an internal business workflow.

The output is then delivered to the user as a message on a screen, a spoken reply, or an automated action in the background.

Some enterprise systems collect user feedback to monitor response quality and enhance long-term performance.

Natural Language Processing (NLP) use cases

Enterprises across sectors such as finance and healthcare use natural language processing to streamline manual processes and extract insights from language data.

The following examples show how NLP is applied across a range of business functions.

Extracting insights from unstructured business documents

When text is written in a free-form style, it can be challenging to locate specific information. NLP systems are trained to detect relevant content, which may be buried in long passages of free-form text. For example, a financial institution might use NLP to extract client names or identify risk-related terms in regulatory filings. By interpreting language in context, the system extracts structured meaning from loosely written content after segmenting and processing it.

Powering enterprise conversational systems

People may express intent in varied and incomplete ways, often using ambiguous or informal phrasing. NLP enables systems to interpret unpredictable input by recognizing patterns in syntax, word choice, and phrasing. In retail, NLP helps virtual assistants understand product-related questions even when customers phrase them differently.

Detecting sentiment in customer and patient communication

NLP models can detect implied emotional signals in language, such as approval, frustration, or doubt. They examine how words are arranged and used to infer emotion. For example, a short question might indicate dissatisfaction if it follows a delay or lack of progress. Once a model has identified the tone, it can respond in a way that aligns with the detected emotional tone of the message. If a complaint or concern is detected in a customer message, the system can escalate it for human resolution.