Top 30+ NLP Use Cases in 2025 with Real-life Examples

Natural Language Processing (NLP) has become a driving force behind business transformation across industries. The NLP market is projected to hit $53.42 billion by the end of 2025, growing at a remarkable annual rate of 24.76% to reach $201.49 billion by 2031, as companies increasingly look for competitive edges through these technologies.¹

We’ve explored NLP’s 30 most impactful use cases, organizing them by applications, how they work, relevant industries, and business functions, with real-world examples for each.

General applications

1. Translation systems

Modern NLP-powered translation has advanced significantly since the 1950s, when Georgetown University and IBM demonstrated the first machine capable of automatically translating 60 Russian sentences into English. Today, translation systems utilize sophisticated neural networks to deliver near real-time translations across hundreds of languages with impressive accuracy.

Example

Google Translate, DeepL, and Microsoft Translator now provide contextual translations that recognize idiomatic expressions, technical terminology, and cultural nuances. These systems are continually improving as they learn from user interactions and corrections.

2. Autocorrect

NLP-driven autocorrect functions have evolved from basic spelling corrections to comprehending context and user intent. Modern autocorrect systems utilize three key methods:

1. Linguistic experts create rule-based approaches to apply specific language rules
2. Machine learning algorithms are trained on large datasets to identify patterns and errors
3. Hybrid systems combine rules and machine learning for optimal performance across various types of errors.

Example

Grammarly is an excellent tool that provides error correction, style suggestions, tone analysis, and clarity improvements for various contexts and document types.

3. Autocomplete

Autocomplete technologies combine natural language processing (NLP) with various machine learning algorithms, including supervised learning, recurrent neural networks (RNN), and latent semantic analysis (LSA). These technologies can predict the next word, phrase, and entire sentences based on user patterns, industry context, and language conventions.

Example

For instance, Google’s Smart Reply analyzes email content to suggest relevant responses. At the same time, professional writing tools like Jasper and OpenAI’s GPT models can transform partial sentences into complete paragraphs, ensuring a consistent tone and style.

4. Conversational AI

Conversational AI is one of the most prominent applications of Natural Language Processing (NLP). It powers customer service chatbots, virtual assistants, and voice-activated devices by combining intent recognition, entity extraction, and response generation to mimic human conversation. Natural language understanding (NLU) is essential for conversational AI, as it enables the system to comprehend user queries and generate appropriate responses.

Example

Intercom utilizes advanced customer service chatbots that handle routine inquiries, process orders, and escalate more complex issues to human agents when needed. According to Juniper Research, chatbots will save businesses over $8 billion annually by 2026 by reducing customer service costs.

Youtube video explaining the logic behind the chatbots.

5. Automated speech recognition

Automatic speech recognition (ASR) converts acoustic sound waves into digital text through a complex process:

1. Splitting audio into individual sounds (tokens)
2. Analyzing each sound’s acoustic properties
3. Applying NLP and a variation of advanced algorithms to identify the most probable word matches
4. Converting processed sounds into accurate text

This technology powers voice search on smartphones, voice-controlled smart home devices, and accessibility features for individuals with disabilities.

Example

Amazon’s Alexa processes billions of voice commands daily with an increasingly natural understanding of different accents, dialects, and speaking styles.

Figure 2. Speech recognition process²

6. Automatic text summarization

Automatic text summarization has progressed from basic extraction methods to advanced abstractive techniques that generate new content while capturing key information. The current methods include:

• Extractive summarization: This technique involves selecting and combining the most essential existing sentences from the source material.
• Abstractive summarization: This approach creates new text that conveys the essential meaning of the original content.
• Hybrid approaches: These methods combine both extraction and abstraction to achieve optimal results.

Natural Language Processing (NLP) is applied during the text interpretation steps, which include:

1. Removing filler words from the text.
2. Breaking the text down into shorter sentences or tokens.
3. Creating a similarity matrix to represent the relationships between different tokens.
4. Calculating sentence ranks based on semantic similarity.
5. Selecting the highest-ranked sentences to generate the summary.

Figure 3. Steps of the summarizing text process of NLP models.³

Example

Bloomberg utilizes natural language processing (NLP) for summarization to condense thousands of financial news articles into concise client briefings. This allows clients to quickly grasp market-moving information without reading extensive reports.

7. Large Language Model(LLM) powered chatbots

LLM-powered chatbots, such as ChatGPT from OpenAI, LaMDA/Bard from Google, and Claude from Anthropic, have advanced natural language processing (NLP). These AI-driven agents utilize deep learning to produce human-like text. They are used for machine translation, part-of-speech tagging, optical character recognition (OCR), and handwriting recognition. Trained on large datasets from the internet, these models can:

• Generate human-like text across various styles
• Answer complex questions with a nuanced understanding
• Summarize lengthy documents while preserving key insights
• Translate languages with near-human accuracy

Organizations use LLM-powered chatbots via APIs to improve content creation, customer service, and information retrieval.

Example
Morgan Stanley has deployed OpenAI technology to analyze thousands of research reports and provide financial advisors with instantly relevant information from their vast knowledge base.

8. Cross-domain and multilingual capabilities

Modern natural language processing (NLP) systems increasingly function across different languages and specialized fields. They focus on:

• Managing technical terminology in areas such as medicine, law, and engineering.
• Recognizing subtle linguistic differences across dozens or even hundreds of languages.
• Transferring knowledge and skills from high-resource languages to low-resource languages.
• Understanding specific jargon and conventions unique to various domains.

Example

The EU’s eTranslation service utilizes advanced NLP to translate legal and technical documents in all 24 official EU languages, ensuring the consistency of specialized terminology and legal concepts.

Retail & e-commerce use cases

9. Customer service chatbots

Retail chatbots now handle everything from initial product inquiries to post-purchase support. These intelligent assistants improve customer satisfaction while reducing service costs. They can answer FAQs, schedule appointments, book tickets, process and track orders, cross-sell, and onboard new users. According to a report, retail chatbots are projected to drive $112 billion in retail sales by 2026.

Example

H&M’s chatbot assists customers in finding products that match their style preferences, while Sephora’s Virtual Artist uses NLP to understand beauty-related queries and recommend appropriate products.

10. Market intelligence

Marketers can use Natural Language Processing (NLP) to analyze product reviews, social media discussions, and competitor messaging to identify emerging trends and consumer sentiments. This enables them to:

• Quickly identify product issues before they escalate
• Recognize shifts in consumer preferences
• Analyze competitive positioning
• Detect trending products or features early on

Example

Unilever employs NLP-driven social listening tools to track consumer reactions to new product launches and adjust marketing messages accordingly, resulting in more effective campaigns and product enhancements.

11. Semantic search enhancement

E-commerce platforms use advanced semantic search algorithms that go beyond simple keyword matching to understand shopping intent. These systems can interpret long-tail search queries, identify product attributes, and match them with relevant inventory.

Example

Amazon’s search engine understands that searching for “lightweight summer dress under $50” should yield affordable and breathable dresses designed for warm weather, even if those exact phrases do not appear in the product descriptions.

Healthcare use cases

12. Dictation

Physicians document clinical procedures and results by dictating the processes into a voice recorder or to a medical stenographer. This audio is later transcribed into text and entered into Electronic Medical Records (EMR) and Electronic Health Records (EHR) systems. Natural Language Processing (NLP) can be utilized to analyze the voice recordings and convert them into text, which is then integrated into EMRs and patients’ records.

13. Clinical documentation of unstructured medical data

Healthcare professionals dedicate around 6 hours of their 11.4-hour workday to electronic health record (EHR) data entry. Natural Language Processing (NLP) solutions significantly alleviate this workload by:

• Converting dictated notes into structured text
• Extracting key clinical information from unstructured physician notes
• Automatically coding procedures and diagnoses
• Identifying pertinent information for billing and compliance

Example

Nuance’s Dragon Medical One is utilized by over 550,000 physicians worldwide. This NLP tool transcribes clinical documentation with an accuracy rate exceeding 99%, allowing doctors to save an average of 2 hours each day.

14. Clinical trial matching

Natural Language Processing (NLP) enhances clinical decision support by analyzing patient records, medical literature, and treatment guidelines. These systems can:

• Identify patients who meet specific criteria for clinical trials
• Flag potential medication interactions or contraindications
• Suggest appropriate diagnostic tests based on symptom patterns
• Recommend treatment options based on similar cases

Example

Mayo Clinic has implemented NLP systems that analyze unstructured clinical notes to identify patients with particular conditions who may benefit from targeted interventions, ultimately improving early detection and treatment rates.

15. Computational phenotyping

Phenotyping involves analyzing a patient’s physical or biochemical traits, known as the phenotype, using genetic data from DNA sequencing. In contrast, computational phenotyping combines structured data—like electronic health records and medication prescriptions—with unstructured data, including physicians’ notes, medical histories, and laboratory results.

This approach enables various applications, such as categorizing patient diagnoses, discovering novel phenotypes, screening for clinical trials, conducting pharmacogenomics studies, and analyzing drug-drug interactions (DDIs).

In this context, natural language processing (NLP) is utilized for keyword searches in rule-based systems. These systems search for specific keywords (e.g., “pneumonia in the right lower lobe”) within unstructured data, filtering out irrelevant information, checking for abbreviations or synonyms, and matching the keywords to underlying events previously defined by established rules.

Example

For example, researchers at Vanderbilt University Medical Center used NLP to analyze 2.8 million clinical notes. Their efforts successfully identified previously unrecognized phenotype correlations, leading to improved diagnostic accuracy for complex medical conditions.

16. Clinical diagnosis

Natural Language Processing (NLP) is utilized to develop medical models that can identify disease criteria based on standard clinical terminology and medical language usage.

Example

IBM Watson, a cognitive NLP solution, has been employed at the MD Anderson Cancer Center to analyze patients’ electronic health record (EHR) documents and provide treatment recommendations with an impressive accuracy rate of 90%.

However, Watson encountered difficulties when interpreting physicians’ handwriting, leading to incorrect responses due to misunderstandings of shorthand. According to project leaders, Watson struggled to differentiate between the acronym for Acute Lymphoblastic Leukemia, “ALL,” and the shorthand for allergy, which is also “ALL.”

17. Virtual therapists

Virtual therapists powered by Natural Language Processing (NLP) offer accessible mental health support through various methods, including:

• Cognitive Behavioral Therapy (CBT) exercises
• Mood tracking and analysis
• Guided meditation and stress reduction techniques
• Early intervention for identifying concerning patterns

Example

Woebot, an NLP-based therapeutic chatbot, has shown effectiveness in alleviating symptoms of depression and anxiety. This is achieved through daily check-ins and structured therapeutic interventions, as reported in peer-reviewed research published in JMIR Mental Health.

Financial services use cases

18. Risk assessment

Financial institutions use Natural Language Processing (NLP) to extract valuable insights from large volumes of unstructured data for risk assessment purposes. Examples of this include:

• Analyzing earnings calls and financial news to predict stock prices
• Evaluating loan documentation for credit scoring
• Detecting shifts in sentiment from market commentary
• Identifying emerging market risks from research reports

Example

JPMorgan Chase’s LOXM platform leverages NLP to assess financial news, social media, and economic reports, which helps to inform trading strategies. This approach has reportedly improved trade execution efficiency by 40%.

19. Fraud detection

NLP improves fraud detection by analyzing language in financial communications, identifying suspicious transaction descriptions, detecting anomalies in payment documentation, and recognizing patterns related to known fraud schemes.

Example

Wells Fargo implemented a natural language processing (NLP) system to analyze corporate financial reports and earnings call transcripts for potential fraud indicators. In 2023, the system flagged unusual language patterns in the quarterly reports of a mid-sized technology company. As a result, the bank reduced its exposure before the issues became public, avoiding significant losses when the company later restated its financials and its stock price dropped by 47%.

20. Automated regulatory compliance

Financial institutions face the challenge of navigating complex and ever-changing regulatory requirements. Natural Language Processing (NLP) tools can assist in this process by:

• Monitoring regulatory publications for relevant updates
• Extracting compliance requirements from legal documents
• Screening communications for potential compliance violations
• Generating compliance reports and documentation

Example

HSBC has implemented NLP systems to review and classify over 100 million transactions daily for compliance purposes. This has resulted in a 20% reduction in false positives, allowing compliance teams to focus on genuine risks.

21. Financial reporting

Natural Language Processing (NLP) and machine learning are transforming financial reporting by:

• Extracting critical data from unstructured financial statements
• Processing invoices, contracts, and payment documentation
• Feeding structured data into automation tools like RPA bots
• Generating comprehensive reports with minimal human input
• Detecting anomalies that may signal financial irregularities

The result is faster, more accurate reports and deeper insights with less manual work.

Example

JP Morgan Chase’s Contract Intelligence (COIN) platform uses NLP to analyze 12,000 commercial loan agreements annually. It swiftly extracts key data from complex documents, completing in seconds what once took 360,000 hours of lawyer time. This implementation not only saved costs but also reduced errors by 66%, significantly speeding up document processing.

Insurance use cases

22. Insurance claims management

NLP and OCR are transforming insurance claims management by automating information extraction, understanding context, categorizing claims, and detecting fraud indicators.

Example

Zurich Insurance’s NLP-powered system processes various claim documents, extracts key details, and routes claims efficiently. This innovation reduced processing time from 58 minutes to 5 minutes (90% decrease) and improved accuracy by 25%, enhancing customer satisfaction. The system quickly identifies details in auto accident claims within seconds of submission.

HR use cases

23. Resume evaluation

Natural Language Processing (NLP) is transforming the way resumes are evaluated by:

• Automatically extracting key qualifications, skills, and experiences.
• Matching candidate profiles with specific job requirements.
• Generating concise summaries of relevant qualifications.
• Identifying candidates with transferable skills that keyword matching might overlook.
• – Reducing bias through consistent evaluation criteria.

Example

Johnson & Johnson’s NLP-powered resume screening system processes over 1.5 million resumes annually, analyzing 50+ data points and improving candidate matching. It saves recruiters 70% time, increases candidate diversity by 17%, and raises the rate of well-matched interview candidates from 62% to 85%.

Figure 4. How NLP evaluates resumes.

24. Recruiting chatbot

Recruiting chatbots utilize Natural Language Processing to enhance the hiring process by:

• Engaging candidates in natural conversations throughout their recruitment journey.
• Screening resumes and matching candidates with specific job requirements.
• Automating interview scheduling while accommodating the availability of recruiters.
• Providing instant answers to candidate questions with accurate and personalized information.
• Streamlining the onboarding process by guiding the collection of necessary documents.

Example

L’Oréal introduced the NLP-powered chatbot “Mya” to improve its global talent acquisition. The chatbot engages candidates applying for marketing positions by asking qualifying questions about their experience in digital marketing and social media.

For qualified candidates, Mya schedules interviews by syncing with recruiters’ calendars and answers role-specific inquiries using the company’s knowledge base. This implementation reduced time-to-hire by 40% and raised candidate satisfaction from 78% to 92%. HR teams saved about 20 hours weekly on administrative tasks, and application completion rates increased by 53% due to immediate engagement.

25. Interview assessment

Natural Language Processing technology transforms virtual interview platforms by analyzing candidate responses more deeply than just relying on keyword matching. NLP systems assess sentiment patterns, extract key qualifications from uploaded documents, and offer comprehensive evaluation metrics that human recruiters might overlook, especially in high-volume hiring situations.

Example

Unilever revamped its campus recruitment for the Future Leaders Program using HireVue’s NLP-powered platform. Candidates record video responses to standardized questions, which the system analyzes for communication patterns, sentiment, competency signals, and unconscious biases. This tool transcribes interviews, extracts insights, and provides standardized assessment scores. As a result, Unilever reduced their hiring timeline from 4 months to 4 weeks, increased diversity by 16%, and saved recruiters around 100,000 hours annually. Retention rates for new hires improved by 25% compared to traditional methods.

26. Employee sentiment analysis

NLP is transforming HR analytics by revealing hidden patterns in employee communications. Advanced NLP algorithms analyze text from various sources to determine satisfaction levels, identify potential conflicts, and highlight training needs. This provides actionable intelligence that allows for proactive workplace improvements.

Example

One example is Microsoft’s NLP-powered “Employee Voice” system, which analyzes anonymous feedback, public reviews, and internal communications from its global workforce. When the system detected rising frustration regarding work-life balance in the engineering division, HR responded swiftly by implementing targeted interventions. As a result, satisfaction scores improved by 24% within just one quarter, and attrition rates among high-performing engineers decreased by 15%, preventing approximately $3.2 million in replacement costs.

Cybersecurity use cases

27. Spam detection

Natural Language Processing (NLP) is changing spam detection by analyzing content patterns and contextual signals to identify unwanted messages. Unlike basic keyword matching, modern NLP understands message intent through advanced text analysis. The spam detection process typically includes:

1. Data Cleaning: Removing filler and stop words.
2. Tokenization: Splitting text into smaller units, like sentences.
3. Part-of-Speech (PoS) Tagging: Assigning tags to words based on their context.

Finally, the processed data is classified using algorithms such as decision trees or K-nearest neighbors to determine if an email is spam or non-spam.

Figure 4. Machine learning for email spam filtering: review, approaches and open research problems.⁴

Example

Google’s Gmail uses advanced natural language processing (NLP) techniques to filter over 100 million spam messages each day. The system analyzes message content, examines linguistic patterns, and evaluates sender behavior to identify spam accurately.

When Gmail implemented its NLP-powered TensorFlow model, it successfully blocked an additional 100 million spam messages that traditional filters had missed. This improvement led to a 60% reduction in user-reported spam and a 35% decrease in false positives, significantly enhancing both security and user experience for Gmail’s 1.8 billion users worldwide.

28. Data exfiltration prevention

Natural Language Processing (NLP) is enhancing cybersecurity by analyzing text patterns in communications and network traffic to detect data exfiltration attempts. Attackers often use techniques like Domain Name System (DNS) tunneling, which manipulates DNS queries, and phishing emails that trick users into revealing personal information. Modern NLP systems can identify suspicious language patterns and unusual queries that traditional security measures might miss.

Example

Raytheon has implemented an NLP-powered security system in its defense contracting operations that analyzes network traffic and communications. It successfully detected a sophisticated data exfiltration attempt where sensitive information was concealed in normal DNS queries.

By identifying unusual linguistic patterns, the system blocked the transfer of classified information, preventing a major data breach that traditional security tools missed, and safeguarded intellectual property worth millions of dollars while ensuring compliance with defense contracts.

Media & publishing use cases

29. Content recommendation engines

Natural Language Processing (NLP) is revolutionizing content discovery by analyzing user preferences and the semantics of documents to provide personalized recommendations. These systems surpass basic keyword matching by:

• Understanding thematic elements and writing styles across various content pieces
• Identifying relationships between seemingly unrelated topics based on semantic similarity
• Recognizing user consumption patterns and reading preferences
• Adapting recommendations based on time, context, and evolving interests

Example

The New York Times implemented “Project Feels,” an NLP system that analyzes articles’ topics, emotional tone, and reader engagement patterns. This system increased subscriber retention by 31% by recommending content that is contextually relevant and aligns with readers’ emotional states and interests.

For instance, when readers showed interest in climate-related topics, the system could differentiate between those seeking technical explanations and those interested in human interest stories, delivering appropriately matched content and increasing average session duration by 42%.

Legal industry use case

30. Contract analysis

Natural Language Processing (NLP) is transforming the review of legal documents by automating the extraction and analysis of essential information from contracts, leases, and legal agreements. Modern NLP-powered contract analysis systems can:

• Identify and categorize contractual clauses and obligations
• Highlight unusual terms or potential risks
• Compare documents against standard templates or previous agreements
• Extract key dates, parties, and financial terms for compliance tracking

Example

The law firm Allen & Overy implemented an NLP-based contract analysis platform for a major corporate acquisition involving over 10,000 contracts. The system automatically classified documents, extracted essential provisions, and identified non-standard clauses that required attorney review. As a result, document review time was reduced by 70%, and accuracy improved by 30%. The firm estimated a savings of $2.5 million in billable hours and completed due diligence three weeks faster than in comparable previous transactions, providing their client with a significant negotiating advantage.

Education Use Case

31. Automated assessment and feedback

NLP is transforming educational assessment by enabling the automated evaluation of essays, open-ended responses, and student writing. These systems offer several benefits:

• Assess the quality of content, structure, and adherence to assignment requirements.
• Provide immediate and specific feedback on writing strengths and weaknesses.
• Detect conceptual misunderstandings in student explanations.

Example

The University of Michigan has implemented an NLP-powered writing feedback system across its undergraduate composition courses. This system analyzes student essays for coherence, evidence usage, and the quality of argumentation, supplying instant, specific feedback that allows instructors to focus on higher-order concerns. Students who received NLP-generated feedback demonstrated a 28% improvement in writing scores compared to control groups, and course completion rates increased by 17%. Furthermore, the system identified common conceptual gaps among the student population, enabling curriculum adjustments that enhanced overall learning outcomes.

10 best practises of NLP

1. Multimodal enhancement: Integrate text analysis with other data types, such as document layout, images, and audio, for a richer understanding.
2. Domain-specific pre-training: Develop models specifically trained on content relevant to your industry, such as financial documents, legal texts, or medical records, rather than relying on general models.
3. Synthetic data augmentation: Create artificial examples of rare cases and challenging scenarios to enhance the model’s performance in uncommon but significant situations.
4. Multi-task learning: Design systems that can learn multiple related tasks simultaneously, thereby reducing development time and improving overall performance.
5. Human-AI collaboration: Establish workflows where AI manages routine cases while referring uncertain or high-risk situations to human experts.
6. Counterfactual explainability: Provide users with insights on how modifying specific inputs would change the AI’s decision, making the system’s reasoning more transparent and trustworthy.
7. Ethical AI and bias mitigation: Incorporate diverse training data, conduct regular bias audits, ensure capability transparency, and maintain human oversight for sensitive applications. Microsoft’s Office of Responsible AI offers tools for detecting and addressing bias before deployment.
8. Existing system integration: Integrate NLP capabilities with existing software systems, establish clear workflows for handling exceptions, and align metrics with business objectives. For instance, Salesforce’s Service Cloud incorporates NLP directly into CRM workflows without requiring users to switch systems.
9. Continuous learning systems: Implement feedback loops that capture user corrections, regularly retrain models with new data reflecting changes in language use, conduct A/B testing of different approaches, and monitor performance for any changes.
10. Federated learning: Allow models to learn collaboratively while keeping sensitive data localized, ensuring privacy and compliance.

Conclusion

Natural Language Processing (NLP) has transformed from a research curiosity into a fundamental technology that drives innovation across various industries. Organizations are utilizing NLP’s capabilities for applications such as real-time translation, intelligent chatbots, automated clinical documentation, and contract analysis. These applications help unlock efficiency, improve decision-making, and enhance customer experiences.

Successful adoption of NLP requires more than just implementing advanced models. It is crucial to ensure high-quality and representative training data, incorporate robust bias mitigation and interpretability measures, integrate NLP seamlessly into existing workflows, and maintain a human oversight process. By combining advanced NLP tools with domain expertise and strategic governance, enterprises can effectively leverage the transformative power of language AI while managing its associated risks.

FAQs

Further reading

• Chatbot applications
• Conversational Banking
• 4 Benefits of R2R (Record to Report) Automation

External Links

• 1. Natural Language Processing - Worldwide | Market Forecast. Statista
• 2. Using Kubeflow to solve natural language processing problems. Analytics India Magazine
• 3. Text Summarization Using TextRank Algorithm - Analytics Vidhya. Analytics Vidhya
• 4. ScienceDirect.