Top 30+ NLP Use Cases with Real-life Examples

The NLP market will hit $53.42 billion this year. By 2031? We’re looking at $201.49 billion.¹

6. Automatic text summarization

Text summarization moved past just pulling key sentences. Modern systems generate new text that captures essence without copying phrases.

Extractive methods grab important sentences directly. Abstractive approaches write fresh summaries. Hybrid systems do both, choosing the best approach per document type.

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.²

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

Example
Morgan Stanley feeds OpenAI thousands of research reports. Financial advisors get instant answers pulling from their entire knowledge base – no more searching through PDFs.

8. Cross-Language, Cross-Domain Intelligence

Modern NLP handles medical terminology in Mandarin, legal concepts in Portuguese, engineering specs in Arabic. The EU’s eTranslation service processes documents across 24 languages while maintaining technical precision and legal consistency.

Example

Knowledge transfers from high-resource languages (English, Spanish) to low-resource ones (Swahili, Icelandic). Domain expertise crosses linguistic boundaries.

Retail & E-commerce

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 bot understands style preferences. Sephora’s Virtual Artist knows the difference between “natural look” and “no-makeup makeup.” Each interaction teaches the system customer preferences.

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.

Example

Unilever tracks product launches through social sentiment. When customers complain about packaging before mentioning product quality, they know to fix the box first. NLP spots trending complaints before they become PR disasters.

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 knows “lightweight summer dress under $50” means breathable fabrics, not just keywords. The system interprets shopping intent – someone searching “interview outfit” gets different results than “wedding guest dress” even if both queries could return the same products.

Healthcare use cases

12. Doctors Dictate, NLP Types

Physicians spend 6 of their 11.4-hour workdays on EHR data entry. Voice-to-text changes that equation. Doctors dictate notes, NLP converts speech to structured text, automatically codes procedures, and populates patient records.

13. Medical Documentation Without the Paperwork

Nuance’s Dragon Medical One serves 550,000 physicians with 99% accuracy. Doctors save 2 hours daily. The system understands medical terminology, drug names, and clinical abbreviations that would confuse general speech recognition.

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. AI 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 achieved 90% accuracy in cancer treatment recommendations at MD Anderson. But it struggled with physician handwriting and confused “ALL” (Acute Lymphoblastic Leukemia) with “ALL” (allergy). The lesson: NLP needs context, not just pattern matching.

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’s LOXM platform processes news, social media, and economic reports. Trading efficiency improved 40%. The system extracts insights from earnings calls, evaluates loan documentation, detects sentiment shifts in market commentary.

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’s NLP system analyzed quarterly reports from a tech company, spotting unusual language patterns. The bank reduced exposure before problems went public. When the company restated financials, stock dropped 47%. Wells Fargo had already moved on.

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’s COIN platform analyzes 12,000 commercial loan agreements yearly. Work that took lawyers 360,000 hours now takes seconds. Errors dropped 66%. The system extracts key data, identifies anomalies, generates reports.

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 reduced claim processing from 58 minutes to 5 minutes – a 90% decrease. Accuracy improved 25%. The NLP system extracts information from various documents, categorizes claims, routes them appropriately, identifies potential fraud.

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 processes 1.5 million resumes annually through NLP. The system analyzes 50+ data points, improves candidate matching, saves recruiters 70% of their time. Diversity increased 17%. Interview match rates jumped 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’s “Mya” chatbot screens marketing candidates, schedules interviews, answers questions. Time-to-hire dropped 40%. Candidate satisfaction rose from 78% to 92%. Application completion increased 53% because candidates get immediate responses.

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 uses HireVue’s NLP for campus recruiting. The system analyzes video responses for communication patterns, competency signals, unconscious bias. Hiring timeline: 4 months to 4 weeks. Diversity up 16%. New hire retention improved 25%.

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

Microsoft’s “Employee Voice” detected engineering team frustration about work-life balance. HR implemented targeted changes. Satisfaction improved 24% in one quarter. Attrition among top performers dropped 15%, saving $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.³

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’s NLP security system detected classified information hidden in DNS queries. Traditional tools saw normal network traffic. NLP spotted linguistic anomalies, prevented multi-million dollar IP theft.

Media & publishing use cases

29. Content recommendation engines

Natural Language Processing (NLP) is revolutionizing content discovery by analyzing user preferences and document semantics to deliver 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 “Project Feels” increased subscriber retention 31%. The system analyzes topics, emotional tone, engagement patterns. Climate articles? It knows who wants technical analysis over human-interest stories. Session duration up 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.

Example

Allen & Overy reviewed 10,000 contracts for a major acquisition using NLP. Review time down 70%. Accuracy up 30%. The firm saved $2.5 million in billable hours, completed due diligence three weeks faster.

The system classified documents, extracted provisions, flagged non-standard clauses for attorney review.

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

University of Michigan’s NLP writing system provides instant feedback on essays. Students receiving NLP feedback improved writing scores 28%. Course completion up 17%. The system identified conceptual gaps, enabling curriculum improvements.

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.

FAQs

Further reading

• Chatbot applications
• Conversational Banking

Reference Links

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https://www.statista.com/outlook/tmo/artificial-intelligence/natural-language-processing/worldwide[/efn_note] But here’s what those numbers mean for actual businesses: companies are finally figuring out which NLP applications deliver results versus which ones just sound impressive in vendor demos. After analyzing implementations across industries, we’ve identified 30 use cases where NLP delivers measurable value, not theoretical potential. General applications 1. Translation systems Georgetown and IBM showed off machine translation in the 1950s with 60 Russian sentences. Today’s systems handle context, not just words. DeepL catches nuances Google misses – try translating German technical documentation and you’ll see the difference. Microsoft’s system knows when “bank” means financial institution versus riverbank based on surrounding text. The real breakthrough isn’t accuracy percentages. It’s that modern translation understands industry jargon. Legal translations preserve specific terminology. Medical translations maintain clinical precision. 2. Autocorrect Forget basic spellcheck. Current autocorrect runs three parallel processes: First, rule engines catch grammatical structures that break standard patterns. Then ML models trained on millions of documents spot contextual errors rules miss. Finally, hybrid systems merge both approaches, learning from your specific writing patterns over time. 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 Modern autocomplete goes way beyond smartphone keyboards. Systems like GPT analyze partial sentences and generate complete paragraphs, maintaining your tone. Google’s Smart Reply reads entire email threads and suggests responses that match both the content and the communication style. Example Jasper turns bullet points into full marketing copy. Legal teams use similar tools to expand case notes into formal briefs. The technology combines RNNs with latent semantic analysis to predict not just words but entire thought patterns. 4. Conversational AI Chatbots save businesses $8 billion annually, according to Juniper Research – but only when they work correctly. The difference between a chatbot that frustrates customers and one that resolves issues comes down to three capabilities: Intent recognition that understands what customers want, not just what they say. Entity extraction that pulls relevant details from messy human speech. Response generation that sounds natural, not scripted. Example Intercom’s bots handle order processing and basic troubleshooting, then seamlessly transfer complex cases to humans with full context. No more “I didn’t understand that” loops. How do chatbots work YouTube video explaining the logic behind the chatbots. 5. Voice recognition Automatic speech recognition (ASR) converts acoustic sound waves into digital text through a complex process: Splitting audio into individual sounds (tokens) Analyzing each sound’s acoustic properties Applying NLP and a variation of advanced algorithms to identify the most probable word matches Converting processed sounds into accurate text Example Alexa processes billions of daily commands across accents, background noise, and mumbled speech. The system learns individual speaking patterns – after a week, it understands your specific pronunciation quirks. Figure 2. Speech recognition process1https://analyticsindiamag.com/ai-trends/using-kubeflow-to-solve-natural-language-processing-nlp-problems/

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Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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