Natural language processing (NLP) refers to a computer’s ability to tackle language-based tasks. Beyond just understanding text or speech, it also covers generating natural language content. NLP is a key element of intelligent automation—a collection of technologies that empower computers to automate knowledge work and boost human productivity. Other pillars of intelligent automation include computer vision (analyzing images and videos in contexts such as self-driving cars or medical diagnostics), cognitive functions like decision-making based on data, and execution abilities that integrate these skills into seamless automated processes.
Below are several practical NLP applications and the ways they can benefit your business.
NLP Technologies in Action
Chatbots and Cognitive Agents
These systems answer questions, retrieve information, or schedule appointments without the need for human intervention. Basic chatbots operate on predefined rules (e.g., “if the user says X, then reply with Y”), while more advanced, self-improving cognitive agents leverage deep learning to refine their conversational skills, sometimes convincingly mimicking human interaction. While many chatbots engage via text (through messaging or SMS), others incorporate voice—and even video—interactions. Examples include ANZ Bank’s “Jamie,” which assists customers with banking services, and Google Duplex, known for making real-world appointments by conversing with human receptionists.
Unstructured Information Management (UIM)
UIM platforms sift through vast amounts of unstructured data—like journal articles, patents, contracts, or health records—to extract meaning without relying on extensive manual keyword searches. This technology transforms loose data into a structured, searchable format, classifying information and identifying clusters or trends. This capability is essential for managing large volumes of documents quickly and accurately.
Sentiment Analysis
This application of NLP evaluates opinions and emotions expressed in unstructured texts, such as tweets or reviews, to gauge approval or disapproval of a brand. It enables businesses to monitor public sentiment in real time and adjust strategies accordingly.
Speech Analytics
Combining unstructured information management with sentiment analysis, speech analytics converts phone call transcriptions and chat logs into structured data for real-time sentiment evaluation. Call centers use this technology to provide agents with instant feedback and suggestions during calls, as well as to alert managers when negative sentiment is detected.
Machine Translation
Machine translation allows for the conversion of text from one language to another. While not always perfect in producing grammatically polished or idiomatic translations, it offers a general understanding of content in unfamiliar languages. Tools like Google Translate, used by approximately 500 million people daily, exemplify the power of this application across over 100 languages.
Information Classification
This process sorts content into predefined categories, such as filtering spam emails or tagging catalog products. Using similar machine learning techniques as those found in image classification for medical diagnostics or self-driving car recognition, it builds its rules by learning from a large dataset of examples. This automation saves time and improves accuracy in tasks ranging from email filtering to categorizing support tickets.
How NLP Benefits Your Business
- Chatbots and Cognitive Agents: By handling straightforward customer queries, these systems reduce the need for extensive call center staffing. They also support human agents with more complex inquiries, thus expanding customer service capacity and improving satisfaction.
- Unstructured Information Management: These platforms automate research-intensive tasks. For example, legal professionals can quickly scan through patents or case law, and medical researchers can identify potential drug interactions without manually reviewing thousands of documents.
- Sentiment Analysis: Real-time monitoring of customer feedback, especially during new product launches or campaigns, enables businesses to quickly adjust strategies and respond to negative trends before they escalate into larger public relations issues.
- Speech Analytics: Enhancing call center operations, this technology provides immediate coaching and performance feedback, helps identify successful sales techniques, and enables the optimization of cross-selling and up-selling strategies—all without incurring additional training or recruitment costs.
- Machine Translation: Facilitates access to international content and communication across language barriers, ensuring that businesses can stay informed about global developments and collaborate effectively without always relying on human translators.
- Information Classification: Automates routine classification tasks, such as filtering spam or organizing products, which saves time and reduces errors, ultimately streamlining customer support and digital content management.
A Real-World Example
Consider a hotel chain that once relied on a team of 240 customer care agents handling over 20,000 interactions daily—across phone, email, and social media. With high workloads leading to low morale and a turnover rate of 40%, customer satisfaction was suffering, rating below five out of 10.
The hotel implemented an omnichannel cognitive agent capable of interacting across multiple communication channels. This agent mimicked human behavior, continuously improved through machine learning, and even recognized customers using biometric data like voice or facial features. After just three months, customer satisfaction soared to nine out of 10, and employee turnover dropped by more than 70%. Human agents were freed up to handle more complex, value-added tasks, reducing overall pressure.
In Summary
Natural language interfaces are revolutionizing human-computer interaction by moving beyond traditional, button-based systems to more intuitive, language-driven processes. This evolution not only simplifies interactions but also integrates multiple cognitive technologies into a cohesive framework—heralding a future where humans and machines work in a more symbiotic, efficient partnership.
