NLP AI Explained: How Artificial Intelligence Understands Language

Natural language processing (NLP) is a subset of artificial intelligence (AI) that helps computers understand and respond to human language. You can think of it as a machine translation layer that turns everyday words and phrases into data that AI can act on.

You've likely benefited from natural language processing (NLP) without realising it. If you've ever changed a flight or gotten styling recommendations while shopping online, you've probably worked with an AI agent powered in part by NLP.

NLP is the engine that helps businesses use AI agents to revolutionise sales, service, commerce, and marketing. This article will explain how it works, why it matters, and how businesses can use NLP to build better experiences for colleagues and customers.

• What is natural language processing (NLP)?
• How natural language processing works
• How NLP is used in everyday AI applications
• How businesses are using NLP today
• The 5 key benefits of NLP for businesses
• Limitations and challenges of NLP
• What does the future hold for NLP?
• Summing up: The importance of NLP and AI today
• FAQs

Natural language processing is a subfield of AI and machine learning focused on helping computers understand, interpret, and generate language in the same way as humans. When a customer support chatbot answers your “Where’s my order?” query, or an AI assistant drafts up an email for you based on a prompt, NLP is a big part of what makes that possible.

NLP is closely linked to machine learning. Rather than constantly sticking to a fixed set of grammar rules, NLP systems learn linguistic patterns from data so they can recognise intent and pick out key information (like a date or product name). This is what makes NLP flexible enough to handle real-world language, even if it contains things like typos or slang.

While AI, machine learning, and NLP are closely related, there are several details that set them apart. Here’s a table to sum up the core differences:

Put together, these capabilities are what make AI models capable of adapting, learning, and conversing with humans in a way that feels natural.

At a high level, NLP turns language (text and audio) into logical forms that machines can understand and process. From there, the model applies learned patterns to produce an output in language we can understand. Let’s take a closer look at how it works.

Here’s a simple step-by-step breakdown that shows how raw language becomes something AI can understand and respond to.

1. Text input/data collection: First, the NLP system receives language data from sources like customer chats, emails, call transcripts, articles, social media feeds, or information pulled from customer relationship management (CRM) systems.
2. Feature extraction: Next, the NLP system converts the raw data into a machine-readable format (usually numerical values in a matrix). This process helps the system extract the terms that matter (such as key dates and questions) while reducing noise that isn’t useful for the task.
3. Extracting meaning: With the key information prepared, the NLP model analyses the input to identify what it’s trying to accomplish. This is achieved by analysing key terms, pinpointing topics, and looking for signals that show intent.
4. Interpreting context: To improve the accuracy of its output, the NLP system also accounts for ambiguity, such as how the word “charge” could reference billing or powering a phone. To determine which meaning applies, modern context-aware models will look at surrounding words or past conversation context to infer user intent.
5. Decisioning and output: Once the NLP model has extracted the meaning in context, it produces an outcome, such as an answer or recommended next step. It will then display the result back to the user in natural language.

In business settings, these steps often run continuously in seconds, enabling faster and more consistent service across channels.

Not all NLP systems learn the same way. Some rely on predefined language rules, whereas others use statistical, machine learning, or deep learning methods to learn patterns and interpret language more accurately. Here are the most common approaches:

Some NLP systems rely on hand-built rules like grammar patterns and follow “if this, then that” logic. This approach can work well for narrow use cases (like recognising a keyword or providing a yes/no answer to a predefined query), but it will often struggle with the ambiguity and variety that crop up in everyday conversation.

Statistical NLP systems learn linguistic patterns from large volumes of text. This helps systems handle the many different ways people can express the same idea. This is also why NLP can work with data from various sources, like books, social media feeds, CRM databases, and news articles, rather than just structured business data.

Modern NLP relies heavily on machine learning (ML), where models learn patterns from data and improve by testing how well outputs match the intended outcome. Depending on the use case, NLP might use a large language model (LLM) for broad, conversational tasks, or a small language model for faster, lower-cost analysis on targeted jobs.

Deep learning is a form of machine learning that mimics the human brain's structure and uses computational power to find patterns in data. This requires larger amounts of data and more time to process, but it enables far more nuanced language understanding, leading to stronger performance on complex tasks.

Some of today’s most advanced deep learning NLP systems are built on transformer architectures, which interpret meaning using context rather than key terms alone. Examples include BERT (used for tasks like classification and entity extraction) and GPT (used for generating language).

NLP is the engine behind many of the AI-driven experiences we engage with every day. It helps systems understand what we mean, even when we don’t get the wording perfect, extract the right information, and then respond in a way that feels natural.

The table below highlights some common NLP capabilities and where you’ll see them.

The natural language processing field is also big news for businesses, and use cases span dozens of industries. The ability to analyse large amounts of unstructured business data while also helping with manual tasks like generating meeting notes is just the beginning.

Here are some areas where natural language processing is having a significant impact.

• Sales: Natural language processing makes it easier to draft emails, mimic human reasoning, learn from sales calls, and measure performance. Conversational AI tools can also help sales reps better use their most essential piece of software, their CRM. For example, when you’re on a call where a potential customer is asking for data, you can type a natural-language question in Slack to your AI agent to pull the info from your CRM without pausing your conversation.
• Customer service: NLP powers personalised service interactions via AI chatbots and autonomous agents, leading to reduced response times and higher deflection rates. It also makes self-service far easier and more effective.
• Marketing: NLP also helps marketing teams target the right customers, gauge sentiment, and draft content for engagement. For example, Agentforce can help marketers design campaigns and write briefs as well as produce an audience segment analysis, thanks to powerful NLP.
• Commerce: NLP helps companies improve their commerce platforms by understanding their target audience’s product needs, tracking trends and feedback, and analysing user behaviour that exists in the review sections on their site or out on social media. AI agents for manufacturers and retailers can use NLP to absorb vast catalogues of information about your business to write product descriptions, understand customers’ posted reviews, and optimise any text related to your product or service.
• Industry specialisation: NLP technology helps facilitate advancements in industries as varied as healthcare, government, media, nonprofits, and more. From greater team collaboration to faster case resolutions, NLP is being used to create greater efficiencies and better user outcomes.

Natural language processing is vitally important because it helps AI systems work with the language and information businesses deal with on a daily basis. This means it can deliver new efficiencies and even new technologies, like AI agents that help teams serve customers better.

Here are some of the different benefits that NLP can offer to businesses:

NLP assists with automating parts of AI-automated workflows that once required constant manual input. For example, it can classify incoming messages and route them to the right people, extract key details from a form, or summarise a meeting in an instant.

All of this reduces time spent on repetitive and time-consuming tasks, freeing up humans to focus on more strategic work.

NLP makes customer interactions faster and more valuable by enabling smoother self-service and AI-assisted support. Instead of forcing customers to enter the perfect prompt with exact match keywords, NLP chatbots and AI agents can understand intent, ask questions to clarify, gather information from case history, and then respond in plain language.

As an example, One NZ simplified its prepaid portfolio to seven new plans and needed an easy way to migrate hundreds of thousands of existing customers, who previously had to follow complex digital journeys. To fix this, they employed Agentforce as a self-service digital assistant.

Now, when a customer wants to switch, Agentforce can pull real-time customer and account data, provide recommended plan options, and answer questions through NLP, all without employee intervention. This saves time while making the customer experience frictionless.

Much of the customer data that businesses have is unstructured, such as customer feedback, reviews, call transcripts, and chat logs.

The sheer volume of that data would be too much for a human team to classify and organise in a reasonable amount of time, but a computer with NLP capabilities can do it in seconds, identifying themes, sentiment, recurring issues, and emerging trends. This turns unstructured text into structured insights that teams can act on.

NLP also improves how people find and use information within organisations, especially when it’s spread across large knowledge bases and document repositories. Instead of relying on searching through exact keywords, employees can use NLP-powered search engines that assess natural language inputs for what they mean and provide the right results.

Natural language processing drives improvements in efficiency by powering agentic AI solutions like Agentforce. The wide range of agentic use cases that rely on NLP, from lead qualification for sales teams to deflecting common issues for support teams, shows just how valuable it can be in almost every aspect of company operations.

While NLP is always improving and offering new benefits to businesses, it does have practical limitations, especially when inputs are messy and data is imperfect. Here are four challenges to consider and how to overcome them.

• Language ambiguity: The language we use is full of double meanings and hidden context. NLP models are improving at understanding these cues, but they may misinterpret intent if key information is missing. Solving this starts with knowing how to prompt AI effectively. Use follow-up questions, be clear with intent, and include additional information to give clues that guide the model to the right data.
• Data accuracy: Only 53% of IT leaders say they fully trust their organisation’s data accuracy. If your customer records aren’t accurate, the NLP won’t be either. It’s crucial to build a strong data foundation to keep customer information clean, consistent and up to date.
• Bias and fairness: NLP is only as strong as the data we feed it. If that data is biased or unbalanced, the model will amplify the problem. To mitigate this, keep your training data diverse and accurate, then test performance across different user groups to verify the model is fair. You should also integrate a human review process for high-risk use cases.
• Privacy and data security: NLP systems are often tasked with processing sensitive information like emails and transcripts. To keep this data secure, organisations need strong data handling, retention policies, access controls and auditability. Tools like Salesforce Shield can help here by encrypting sensitive data and making it easier to monitor and safeguard.
• Consumer trust: 61% of customers believe advancements in AI make it more important for companies to be trustworthy. To build that trust, businesses need to be transparent about data sources for generative AI and about how customer information is used and protected. In addition, let customers know when they’re speaking to an AI agent; per our survey, 72% consider this kind of transparency important.

These steps will help businesses use NLP responsibly while keeping customer relationships strong. See our guidelines for responsible AI usage to learn more.

The capabilities of NLP are only going to get stronger in 2026 and beyond, especially as models get better at handling context and intent. We can look forward to improvements in summarisation and search, and an uptick in specialised large language models that offer more security, control and speed for niche business use cases.

You can also expect new developments such as real-time translations, greatly simplified user interfaces, and search functions that are increasingly more flexible and better mimic how humans talk. As we've seen in recent years, natural language processing has evolved quickly, and that rate is likely to accelerate.

That said, there are still several risks businesses need to contend with. With bias, ethics, and environmental concerns front and centre, there will be increasing regulatory and social pressure to address the challenges of AI while maximising its benefits. The burden is on businesses to keep their NLP solutions safe, build in harm detection and data security features, remain transparent about data sources for generative AI, and ensure customer information is protected.

The good news is that the businesses that can get this right will be well-positioned to take advantage of everything NLP can offer. Invest in data integrity early, ensure governance and put customers at the heart of your AI strategy to build trust and long-term value.

NLP is what makes modern AI useful in the real world. It helps systems understand language and respond in ways that feel human. To put it into practice, build a strong data foundation and implement guardrails that keep outputs accurate and secure. From there, you’ll be ready to test a use case that improves productivity and provides a better experience for your customers.

Now that you have a clearer understanding of what NLP entails, you're ready to find out how it can work for you in your business. Learn more about Agentforce and how it can create better ways to connect with customers and help your employees be more productive. Or start for free to try it for yourself today.