Benefits and Business Uses of Natural Language Processing

Article updated on 07/09/26

Understanding the business uses of Natural Language Processing can help your organization realize all of the benefits of AI and strategically coordinate goals at every level. In this post, we will cover everything you need to know about Natural Language Processing, and how it can take AI to the next level for widespread use in your organization.

What is NLP?

Natural Language Processing (NLP) is an engine that analyzes user input, aims to detect user intent, and identifies the relevant answer, knowledge, or automated process. NLP is a subfield of computer science and artificial intelligence focusing on language interactions between humans and computers. NLP’s ability to detect intent and account for regional language differences is helpful in providing contextualized answers and communicating the proper information to people no matter where they are in the world or which language they prefer. NLP also takes into account colloquialisms and other important factors when someone is searching or communicating with AI incorporated with IT Service Management (ITSM) — the set of practices organizations use to design, deliver, and manage IT services.

AI without NLP or machine learning, ML, (which refines answers over time by analyzing search terms, inputs, and data) may be able to regurgitate programmed data, but only when specific terms are entered.

For example, consider if you are searching in a self-service portal for knowledge articles about how to reset your cell phone. Without NLP, you may be able to pull up these articles with specific search terms like “cell phone reset” or “reset cell phone”. But, unless the AI is programmed with additional parameters it will not pull up articles for “iPhone reset” or “mobile phone not working”. With NLP, however, especially when equipped with machine learning, the AI can analyze and understand your sentiment or intent. So if you refer to a cell phone as a mobile phone, or if your regional language has another term for reset, you can still find the information for which you are searching.

The Business Benefits of Natural Language Processing

The search example above illustrates only the most surface-level application of NLP. The business uses of Natural Language Processing and the benefits go well beyond simple search results.

The most important aspect of NLP is that it helps AI interfaces, such as chatbots and virtual assistants, better understand human language so they can converse with humans more efficiently and more accurately. Consider a service desk scenario: an employee submits a ticket saying “Outlook keeps crashing when I open attachments from external senders.” Without NLP, a keyword-matching system might route this to a generic “email issues” queue. With NLP — particularly when combined with machine learning — the system can parse intent, identify the specific application, the trigger condition, and the user population affected, and route the ticket to the right team with relevant context already populated. That difference in routing accuracy compounds across thousands of tickets per month. It is the gap between a self-service tool that frustrates users and one they actually return to.

When it comes to increasing user engagement with self-service or self-help platforms, including with the addition of a chatbot or virtual assistant, you need to ensure that customers will feel comfortable engaging and conversing with these technologies. That starts with these technologies actually understanding what the end user is asking for, in the user’s native language, and providing accurate responses. After all, nothing is more frustrating than trying to decode pre-programmed terminology to access help.

AI equipped with NLP and ML can power an employee self-service portal. When connected to an intelligent knowledge management database and service management solution, this portal automates responses to common requests. The result is significant time savings for both agents and employees — and when low value-added activities are automated, agents can focus on more complex problems while employees spend less time waiting for resolutions to their technical issues.

In short, NLP offers a way to save time and money for agents, employees, and really anyone who utilizes the self-service portal.

Natural Language Processing Use Cases: 7 Real-World Business Applications

The benefits of NLP extend beyond the IT department. The following examples are just a few of many possible business uses for NLP:

1. Connect Remote Workers from Various Regions to Necessary Knowledge in Any Department

One of the main uses for NLP is through an employee self-service portal. This is especially helpful when you have a team of remote workers or multiple offices spread throughout geographical regions who may have different ways of phrasing the same requests, but who also need access to helpful information.

NLP in a self-service portal doesn’t necessarily need to be tied to connecting employees with the IT service desk. It can help connect remote workers to answers they might need from each other by creating knowledge articles connected to their roles and stored within an intelligent knowledge management database. It can also connect employees to other internal service departments, like Human Resources, facilities, or marketing. This can help employees feel empowered to solve their own problems – be they IT-related or role-specific – even in a remote work landscape.

2. End-User Customer Support Automation

NLP enables customer support chatbots to understand requests phrased differently across languages and regions, routing users to the correct resolution without human intervention. Employee support isn’t the only area where NLP can help connect people to knowledge — externally facing customer support portals can also utilize NLP to provide personalized service to customers, which ultimately supports and contributes to the greater business goals.

To illustrate how this might work in practice, consider a hypothetical scenario modeled on how global consumer chatbots operate: a bot used worldwide to process refunds, request shipping status, and ask when new items will be in stock must serve a wide range of customers. Without NLP, customers in the U.S. may request shipping status by saying something like “where’s my package?” while customers in another part of the world might type something closer to “post delivery date” — and both customers would be directed to a different area, rather than the system recognizing that they are asking the same question.

By providing contextualized answers based on the region and dialect, customers can expect more personalized, one-stop-shop service through the chatbot.

3. Security Authentication

NLP also plays a role in identity verification within IT service management workflows. When a user contacts the service desk — whether through a chatbot, voice channel, or self-service portal — NLP-powered systems can analyze conversational patterns, verify contextual knowledge, and flag anomalies that suggest an unauthorized access attempt.

More practically, NLP enables dynamic, context-aware identity challenges: rather than static security questions, the system can generate verification prompts based on the user’s recent activity or role-specific knowledge.

For IT security teams managing high-volume service desk interactions, this capability reduces the risk of social engineering attacks while maintaining a frictionless experience for legitimate users. It is a use case that sits at the intersection of service management and zero-trust security architecture — an increasingly important integration point for enterprise IT organizations.

4. Feedback Collection and Report Generation

Collecting feedback from users in different regions can be a tough task, especially when you want to leave a few areas open for manual input rather than relying on drop-down menus. But with NLP, you can more easily decipher and refine that feedback using automation. For example, imagine you have a chatbot that asks users to give feedback at the end of an interaction with your service desk. As part of NLP, sentiment analysis — which interprets user feedback by categorizing responses as positive, negative, or neutral regardless of phrasing or language — can help decipher the intent of the answer.

After the feedback and data is collected and accurately stored, NLP can help analyze the correct data needed to generate reports, regardless of the way the feedback was provided or phrased. Over time, machine learning refines this process, improving the accuracy of both feedback interpretation and automated report generation.

5. Automated Ticket Classification and Routing

In IT service management, one of the highest-volume, lowest-value activities is manual ticket triage. An agent reads an incoming ticket, determines its category, assigns a priority, and routes it to the appropriate team — a process that can take several minutes per ticket and introduces inconsistency at scale. NLP automates this process by analyzing the free-text content of a ticket, identifying the intent and affected system, and classifying and routing it without human intervention.

The operational impact is significant: faster time-to-assignment, more consistent categorization, and agents freed from repetitive triage work to focus on resolution. Organizations that have deployed NLP-based ticket classification consistently report reductions in mean time to resolution, particularly for high-frequency, well-understood incident types.

6. Knowledge Article Recommendation at Point of Incident

Traditional knowledge management relies on users knowing what to search for which means they must already understand the problem well enough to describe it in the system’s terminology. NLP removes that dependency. When an employee submits a ticket or begins typing in a self-service portal, an NLP-enabled system analyzes the input in real time and surfaces relevant knowledge articles before the ticket is even submitted.

For IT organizations, this capability directly reduces tier-1 ticket volume: users who find a resolution through a suggested article never escalate to an agent. For agents handling tickets that do come through, NLP-powered agent assist surfaces the same relevant context during the live interaction, reducing the time spent searching for resolution steps and improving first-contact resolution rates.

7. Proactive Incident Detection Through Log and Alert Analysis

NLP is not limited to user-facing interactions. IT operations teams generate enormous volumes of unstructured text data — system logs, monitoring alerts, event descriptions — that contain early signals of emerging incidents. NLP can parse this data at scale, identify patterns that precede known failure modes, and trigger automated responses or alerts before users are impacted.
This shifts the service management posture from reactive to proactive: rather than waiting for a user to report that a system is down, the operations team is already investigating the root cause. For organizations managing complex, distributed IT environments, this capability represents a meaningful reduction in both incident frequency and the cost of unplanned downtime.

Time to Incorporate NLP in Business

The organizations that extract the most value from NLP are not necessarily the ones with the most sophisticated technology — they are the ones that deploy it against well-defined processes with clean underlying data. NLP cannot compensate for a fragmented knowledge base, inconsistent ticket categorization, or a self-service portal that users have already learned to distrust. The technology amplifies what is already there.

That means the first question is not “which NLP tool should we buy?” but “is our service management foundation ready to support it?” For IT leaders working through that assessment, understanding how NLP integrates with your existing ITSM workflows — and where the data quality requirements are — is a useful starting point. A structured evaluation of your current self-service maturity can surface the gaps before they become expensive implementation problems.