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NLP stands for Natural Language Processing, and this phenomenal innovation is, in many ways, driving the future of AI. To begin with, NLP technology is actively transforming how we interact with machines, automate tasks, and drive innovation. As machines and computers become more comfortable interacting with humans, it will set the stage for smoother human-machine interactions, seamless multilingual communication, and increased automation and innovation. Expect more advanced chatbots, improved healthcare diagnostics and treatments, and an increased thrust on ethical AI. Nearly all industries, from healthcare to insurance and retail to manufacturing, stand to benefit from the evolution of natural language processing in AI.
Introduction
In many ways, NLP and the rest of the AI stack are building a new world - a world where machines are trained to comprehend humans and respond appropriately.
Forward-looking organizations, from tech startups to established enterprises, are increasingly investing in NLP-powered apps and systems to streamline operations and drive productivity and business results.
In fact, Grand View Research states, “The global NLP market size was close to USD 439.9 billion by 2030 and is likely to grow at an impressive CAGR of 40.4% from 2023 to 2030.” The rapid growth in the NLP space is a testament to the fact that businesses across the globe are willing to invest in this technology.
The growth in NLP will also help push the existing boundaries of Artificial Intelligence and make AI a far more precious asset in the future. That’s what we’ll focus on in this blog. We’ll learn about the fundamentals of NLP. More importantly, we’ll look into ways this technology will help build a new era of AI.
Let’s start with what natural language processing (NLP) means.
Natural Language Processing (NLP) in AI: An overview
Natural language processing (NLP) is a pivotal innovation in modern AI. The simplest way to understand NLP is to imagine a bridge connecting humans with machines at a far deeper level than ever before.
We use NLP in numerous real-life situations. So, when you interact with a chatbox installed on a website, with voice assistants such as Siri and Alexa, or with tools translating languages in real-time, you are using NLP-powered apps and systems.
NLP uses an ever-increasing number of techniques to understand, process, and generate human language. The most common natural language processing techniques are tokenization, stemming and lemmatization, and named entity recognition (NER).
Natural Language Processing techniques
The entire ecosystem of Natural language processing (NLP) thrives on a multitude of techniques, such as tokenization and transformer models. These natural language processing techniques supercharge an ever-growing number of use cases, from highly interactive chatbots to sentiment analysis. Let’s take a quick look at some of the most common natural language processing techniques:
- Tokenization: It is a key NLP technique that machines use to understand and converse with human beings. Tokenization breaks down large chunks of text into smaller and manageable chunks, which we call ‘tokens.’ Let me share a quick example. In a sentence like ‘NLP connects humans with machines,’ tokenization can be divided into multiple tokens such as ‘NLP,‘ ‘Connects,’ ‘Humans,’ and ‘Machines.’
- Named entity recognition (NER): It’s another critical NLP technique used across a range of applications and systems. The primary task of this NLP technique is to run through the text input and identify and label key information such as name, date, and location. For example, in the sentence, ‘Phil visited London in December,’ NER would identify a) Phil as a person, b) London as a location or place, and c) December as a date or time. By enabling an app or system to understand the who, what, where, and how of a sentence or text, NER boosts the efficiency of modern search engines and other apps and systems to comprehend the user's search intention and deliver highly personalized results or information.
- Stemming and lemmatization:The third NLP technique on our list is ‘stemming and lemmatization.’ Both are different natural language techniques, and we’ll talk about both in brief. First, let’s talk about stemming. Stemming, as you can sense from the word itself, is about removing suffixes and prefixes from the words in a sentence. For example, the words "programming," "programmer," and "programs" can all be stemmed to "program." Lemmatization is a more complex NLP technique than stemming. It breaks down a word into its root form or lemma to ensure better analysis of the text. However, both the natural language processing techniques - stemming and lemmatization - aim to abstract the complexity surrounding the words to provide better data analysis and output.
In addition to tokenization, named entity recognition, stemming, and lemmatization, AI apps, and systems development companies use several other NLP techniques, such as text classification, sentiment analysis, and text summarization.
We’ve now familiarized ourselves with some popular natural language processing techniques. Now, let’s explore another key aspect of NLP: how it differs from core AI technology. Yes, NLP is a type of AI, but it’s also evolved into a world of its own.
NLP and AI: Let’s discuss some key differences
NLP and AI are related in more than one way. One strengthens the other. However, it does not mean that NLP and AI are the same. It’s essential to understand what differentiates them from each other. Let us share a quick comparison table that explores the key differences between NLP and AI.
| Aspect | Artificial Intelligence (AI) | Natural Language Processing (NLP) |
|---|---|---|
| Definition | The broad field of building systems that perform tasks requiring human-like intelligence | A subfield of AI focused specifically on enabling machines to understand, interpret, and generate human language |
| Scope | Covers vision, robotics, planning, reasoning, language, and more | Covers only language-based tasks: text and speech |
| Core Goal | Simulate general or task-specific intelligence | Bridge the gap between human communication and machine understanding |
| Example Techniques | Machine learning, deep learning, computer vision, expert systems, robotics | Tokenization, named entity recognition, sentiment analysis, machine translation, text generation |
| Example Applications | Self-driving cars, fraud detection, robotics, recommendation engines | Chatbots, voice assistants, spam filters, translation tools, document summarization |
| Relationship | The umbrella discipline | One specialized branch within AI, often combined with machine learning |
Everyday use cases of NLP
Whether you know it or not, NLP has entered our lives, and we use it like every day of our lives. Here are some of the examples of NLP in action:
- Google Search: Let’s say you search for "Where can I find a pizza place near me?" Google uses NLP to understand that you’re asking for nearby pizza places, not just pizza recipes. It looks for keywords like “find,” “pizza,” and “near me” to give you the right results.
- Talking to Siri or Alexa: When you say, “Hey Alexa, play my favorite song,” Alexa uses NLP to figure out what you’re asking for. It knows “play” means you want to listen to something, and “favorite song” is the one you like the most. Without NLP, Alexa wouldn’t know what to do with your words!
- Reading Emails: If you’ve ever seen Gmail suggest replies like “Thanks” or “Got it,” that’s NLP. The program reads your email and tries to understand the conversation to suggest a sensible response.
We have shared just the tip of the iceberg regarding how NLP is becoming an essential part of our lives. However, you must have a gist of how NLP impacts us all. Now, let’s get down to the value that the proper applications of NLP solutions can generate.
When Do You Need NLP vs. a Broader AI/ML Approach?
Reach for NLP specifically when the problem is language-shaped: understanding, classifying, translating, summarizing, or generating text or speech (support tickets, contracts, chat transcripts, voice commands). Reach for a broader AI/ML approach when the input is not primarily language — images, sensor data, numeric/tabular records, or sequences of user behavior — or when the task combines language with another modality (e.g. reading a scanned invoice image, which needs OCR plus NLP together). In practice, most enterprise 'AI' initiatives that involve documents, support queues, or conversational interfaces are really NLP projects wearing an AI label — identifying that early changes which team, data, and vendor evaluation criteria you need.
What Is NLP Used For?
NLP is used to let software work directly with human language instead of rigid commands — powering chatbots and voice assistants, automatically routing and summarizing support tickets, extracting key terms and clauses from contracts, translating content across languages, detecting sentiment in reviews and social media, and filtering spam. Any workflow where a person currently has to read, sort, or respond to text or speech is a candidate for NLP automation.
How Does NLP Work?
NLP works by breaking language down into pieces a model can process, then applying statistical or deep learning models to find patterns in that structure. A typical pipeline tokenizes text into words or sub-words, tags parts of speech and named entities, and converts the result into numerical vectors (embeddings) that capture meaning. Modern NLP systems, built on transformer architectures, use these embeddings to understand context across a full sentence or document rather than word by word, which is what allows today's models to hold coherent conversations and generate fluent text.
Business applications of Natural Language Processing
Natural language processing, or NLP, has numerous use cases across nearly all industries. However, the most common uses of NLP in the business world include:
- Speech recognition: NLP tech helps computers and machines to understand, manipulate, and generate human-like content or responses in the form of text, video, image, etc.
- Data analysis: NLP can help businesses analyze unstructured data to find patterns, trends, and customer preferences. This can help companies to make better decisions, understand customer behavior, and optimize marketing strategies.
- Supply chain management: NLP can help businesses manage their supply chains by forecasting demand, selecting suppliers, and fulfilling orders.
- Customer support: NLP-powered chatbots can handle routine customer queries, freeing up human agents for more complex issues.
- Document processing: NLP tools can automatically classify, extract key information, and summarize content.
- Language translation: NLP can translate text from one language to another while preserving meaning, context, and nuances.
- Email filtering: NLP algorithms can categorize and sort emails, reduce spam, and highlight important messages.
- Financial fraud protection: NLP can help insurance and credit card companies analyze data to protect against fraudulent transactions.
NLP in Manufacturing
In manufacturing, NLP is most commonly applied to unstructured maintenance and quality data: extracting failure patterns from technician notes and work orders, converting voice commands into machine instructions on the shop floor, and mining supplier contracts or compliance documents for risk clauses. It's also used to power internal knowledge-base search, so engineers can query equipment manuals and past incident reports in plain language instead of keyword search.
NLP Use Cases in Manufacturing
Common, proven NLP use cases in manufacturing include: predictive maintenance triage from free-text technician logs, automated quality-defect classification from inspection notes, supplier and compliance contract review, voice-driven shop-floor instructions and safety checklists, and internal technical documentation search/Q&A. Each of these reduces manual document review time and surfaces patterns that are otherwise buried in unstructured text.
A few final thoughts
Natural language processing is a powerful technology, which is increasingly driving innovation across the AI landscape.
Nearly every industry stands to benefit from advancements in natural language processing in AI, which will eventually make machines more humane and beneficial for our world.
To harness NLP's full value and drive business forward, you must strategically build, buy, and integrate NLP-powered solutions within your IT infrastructure. That’s where an AI-first technology consulting partner, such as Kellton, can help you navigate the complex landscape of NLP with greater clarity and confidence.
Looking to apply NLP in your business? Talk to our AI/ML team
Let's TalkFrequently Asked Questions Related to NLP in AI
What is NLP in AI?
NLP (Natural Language Processing) is the subfield of AI focused on enabling computers to understand, interpret, and generate human language, in both text and speech. It sits inside the broader field of AI alongside computer vision, robotics, and other specialized disciplines.
What does natural language processing enable machines to do?
NLP enables machines to read and understand text, interpret spoken language, determine sentiment and intent, translate between languages, summarize documents, and generate human-like written or spoken responses.
Is NLP the same as AI?
No. NLP is one specialized branch of AI. AI is the umbrella field covering any system that performs tasks requiring human-like intelligence; NLP specifically covers language-based tasks within that broader field.
What is NLP used for in business?
Businesses use NLP for chatbots and virtual assistants, automated support ticket routing and summarization, contract and document analysis, sentiment analysis on customer feedback, and translation or localization of content at scale.
How is NLP used in manufacturing?
In manufacturing, NLP extracts insights from unstructured maintenance logs and quality reports, powers voice-driven shop-floor instructions, automates supplier/compliance document review, and enables plain-language search across technical documentation.
