Natural language processing (NLP) helps machines understand and generate human language. When built into enterprise systems, it improves access to information, speeds decision-making, and opens up new ways to work with data.
In the early days of AI, natural language processing was hit or miss. Frustrated employees could be heard shouting at their screens when the system misunderstood their prompts. But today, NLP has come of age. With the rapid advancement of AI and machine learning technologies, you can now ask your system a plainly worded question and get back a useful, structured response every time. And when NLP is integrated into your systems, it can access data and information across the business, giving users results that are not only accurate but robust and actionable.
Natural language processing (NLP) is a type of artificial intelligence that allows computers to understand, interpret, and generate human language. It draws on computational linguistics, machine learning, and statistical models to make language usable by machines, not just in terms of words but also meaning, intent, and tone. NLP allows people to interact with systems through natural speech or text, instead of relying on technical commands or fixed inputs.
Natural language processing starts by breaking language into pieces the computer can process – such as words, parts of speech, or even syllables. It then assigns meaning to those pieces based upon what it’s learned. Data is the food that helps all AI and machine learning systems grow because just like humans, the more examples a system encounters, the better it gets at spotting how language typically behaves.
Most NLP uses a type of AI called deep learning. These kinds of systems rely on more than a set of fixed rules – they learn instead from vast amounts of real-world language scenarios. As they evolve, they begin to capture not just syntax and grammar, but tone, nuance, and context. Deep learning models do not treat each word in isolation. They look at how words relate to one another across an entire sentence or paragraph which helps them to understand intent and deeper meaning.
Older NLP systems had to establish context and relationships on a word-by-word basis. Results were slow and unsophisticated. Today, language processing technologies use transformer-based models. These models read whole chunks of text at once, rather than one piece at a time. This allows them to pick up on subtle cues such as sarcasm or confusion, and to extrapolate meaning from only partial technical instructions.
Natural language processing brings together a variety of techniques to help machines interpret and generate human language. Deep learning is driving many recent NLP breakthroughs. But the more traditional rule-based and statistical approaches still play a major role in many enterprise use cases.
In traditional models, short, common words like “the” or “is” were filtered out to speed up processing. More advanced, deep learning-based systems, however, tend to retain these words because as we all know, even small words can subtly change meaning.
To make text machine-readable, NLP systems first break it into manageable parts. These “tokens” can be individual words, punctuation marks, or subword units like prefixes and suffixes (“re-,” “visit,” “-ed”). Tokenization is the starting point for deeper analysis.
Stems and lemmas are the base forms of words. Using these techniques, “running” becomes “run,” “revisited” becomes “revisit.” This helps the system recognize different forms of the same concept wherever it comes across it.
NLP systems use grammatical analysis to identify how words relate to each other. Is “record” a noun or a verb? PoS tagging clarifies meaning, while parsing helps the system understand who did what to whom in a sentence.
These are older models that see documents as unordered collections of words. Because they ignore word order, they’re not much use these days. But in some high-speed tasks like document classification and information retrieval, they have their place.
NER identifies and classifies important pieces of information such as names, dates, locations, and product codes. As the system learns, this catalog of recognizable items grows and can be extracted or connected to other systems or compliance databases.
In its vast training datasets, NLP learns to analyze and recognize sentiment and tone. It becomes able to gauge emotional content including risky signs such as customer frustration, rising employee dissatisfaction, or subtle shifts in public opinion.
Intent detection is particularly useful as it interprets what the user is trying to achieve, even if their phrasing is incomplete or wonky. Intent detection is increasingly used by virtual assistants to help route requests, get answers, or trigger accurate workflow actions.
People have accents, workplaces can be noisy, and a thousand other things can make speech recognition a challenge. But while there’s always room for improvement, modern speech recognition technologies have reached an impressive and reliable capacity.
This is the ability to generate natural language from spreadsheets or structured data – converting it into fluent responses, clear summaries, and reports. Language generation is an essential partner to speech recognition in any holistic language processing system.
All the techniques listed above can be applied to a variety of different models and use cases. But fundamentally, NLP is a bit of a two-sided coin. To function optimally, it must be able to make sense of human language inputs and be equally capable of generating outputs that those same humans can easily understand. NLU (natural language understanding) and NLG (natural language generation) represent the two sides of this linguistic coin.
NLP is the broader field. It includes any method for helping machines understand, interpret, or generate human language whether spoken or written. Large language models (LLMs) like ChatGPT are one way to do that. They use deep learning to handle complex NLP tasks like answering questions, generating content, or interpreting inconsistent input. But they don’t replace all of NLP. Tools like speech recognition, tokenization, or grammar parsing still play key roles alongside LLMs. These days LLMs pretty much dominate the conversation, but in practice, they’re actually part of a larger toolkit.
To function at their best, NLP tools should be able to access whatever information and data sets are needed to give users the most robust responses possible. But of course, data security and governance must be the overarching guide for any such integrations. The good news is that today’s AI-powered solutions are built specifically with cybersecurity and compliance in mind. This lets you balance the benefits of cross-business data access with the reassurance of clear and reliable protocols.
NLP tools only have access to use the data you authorize. Access rights and encryption are baked into modern enterprise NLP, keeping it locked down and only pulling from sources where it has explicit permission.
Balancing silos and security is a challenge. Admins can authorize access to prevent the use of sensitive information – and also to reduce the ability of departments to hoard data that actually should be accessible.
Connected to systems like ERP, logistics, and CRM, NLP can correlate a shipment delay with weather disruptions – giving you both an answer and a contingency plan for a question like: “What’s holding up order XYZ?”
By drawing from internally integrated data such as IoT feeds or production schedules, and external data (like news or market indexes), NLP delivers insights that reflect real-world actionable dynamics.
Data stays in your cloud environment. NLP integrations are set up to follow the same governance and cybersecurity protocols as your core systems. Nonetheless, NLP is new territory for many users, so regular security training is important.
With its mind-boggling capabilities, it's no wonder that business users find new ways to leverage NLP every day, to boost efficiency and competitiveness.
Employees can ask natural, simple questions like “What was last quarter’s revenue in EMEA?” or “Where’s the next shipment from Dallas?” – and receive fast, plain-language responses.
NLP tools can scan long reports, contracts, or regulatory texts and surface key points quickly. This saves hours of manual reading and reduces the risk of errors or oversight.
In environments like warehouses or shop floors employees may not have free hands. This is just one case where voice-driven tools help teams request information or trigger workflows.
It frustrates employees to have to dig through digital files and systems to find the info they need. Saying, “find me the July sales report,” and then immediately getting it is so much smoother.
NLP can analyze unstructured data such as open-ended survey responses, emails, or support chats to uncover (and report on) trends in satisfaction, frustration, or unmet needs.
NLP systems can be taught to flag language in contracts or communications that may signal regulatory or legal risk. This helps focus teams and protect against threats.
By making complex systems easier to access and understand, NLP delivers benefits that ripple across the organization:
Users can get the answers they need in seconds, without relying on analysts or sifting through layers of menus and reports.
Natural language interfaces lower the barrier to entry, helping teams engage more fully with the tools and data available to them.
When insights are easier to surface and understand, it leads to quicker, more confident decisions across departments.
Users don’t need to memorize system commands or workflows. They can simply ask for what they need, in their own words.
By streamlining everyday administrative tasks and by reducing the hours spent looking for information, employees can focus on more value-added work.
When NLP is paired with AI and workflow automation, it can trigger actions such as scheduling a shipment or flagging an exception. All it takes is a simple question or request.
Even just a few years ago, NLP was a niche tool with somewhat limited applications. But today, the sheer power of modern AI-driven solutions has given language processing an important strategic place in many core industries. Below are just a few real-world ways it can be used:
Voice-driven tools reduce errors and speed onboarding by guiding plant workers through assembly steps and safety checks. NLP also powers virtual assistants to answer parts inquiries which lowers downtime and boosts planning.
Users can ask for updates on things like maintenance logs and sensor alerts. By scanning this data, NLP can surface real-time insights on equipment failures or problems – ensuring the right steps get taken before problems occur.
Important pieces of data can be extracted from clinical notes, discharge summaries, or claims. The ability to quickly pinpoint and access this information makes complex tasks like billing, compliance, and reporting faster and more accurate.
NLP-powered digital assistants help operations staff log quality issues, check ingredient sources, or verify compliance using voice or chat. In a sector with so many unpredictable dependencies and variables, any extra layer of assurance is important.
Designers and merchandisers deal with lightning-fast market shifts. Without missing a beat, they can ask “What’s trending in Tokyo?” or “What’s our denim inventory?” And they’ll get a fast and accurate response without having to dig around internally and online.
The volume of technical manuals and regulatory documents is overwhelming. NLP can sift through these quickly to flag compliance issues, extract requirements, or answer engineer queries – streamlining audits and speeding repairs.
While enterprise NLPs are built to be robust and resilient, bad information and flawed data can certainly proliferate in the closed system of a business. As with any dynamic and extremely powerful tool, NLP needs to be responsibly and professionally managed and trained.
Currently, employees spend up to two hours per day simply looking for data and information. Beyond the obvious financial implications of this inefficiency, it also leads to frustration, data squirreling, and other impediments to a smooth-running team. From a customer’s point of view, loyalty and goodwill erodes with every minute they spend waiting for answers. They want personalized responses to their specific needs. And they want them fast and accurate. The good news is that the tools exist today to facilitate amazing and unprecedented levels of understanding and clarity for your customers and your teams. All you have to do to get started is decide to use them.
See how Infor GenAI, supported by NLP, is embedded directly in Infor CloudSuite processes – helping your entire team become hyper-productive in everything they do.