Natural Language Processing (NLP) technology is regarded as one of the key ways of digital transformation. Among the benefits of NLP for business are obtaining valuable insights, automating routine tasks, etc., which brings higher operational efficiency and better financial outcomes.
Back in 2022, NLP market revenue worldwide amounted to $24 billion and is predicted to surge to over $43 billion by the end of 2025. The trend reflects the rising infiltration of businesses in various industries with NLP-based technologies.
From this post, you’ll discover applications of NLP, the categories of the technology, its benefits, and potential pitfalls with its implementation to be aware of.
Why is NLP important for businesses?
NLP is the branch of AI that encompasses machine tasks related to understanding human language. It powers up an entire set of software solutions, which are provided in the below sections. The versatility of NLP in handling human language makes NLP essential across diverse business domains, including:
- Manufacturing. Here, NLP aids in production by interpreting machine communication for predictive maintenance (predictive maintenance domain overview), synthesizing workers’ insights, and analyzing technical documents
- Insurance. NLP in insurance automates claims processing, analyzes customer feedback, and identifies fraudulent activities by extracting key patterns and information.
- Digital marketing. In this field, NLP enables customer targeting through social media analysis, optimizes content for SEO, and personalizes engagement through automated responses.
- Retail. Businesses in this domain leverage NLP to analyze contexts of customer reviews, drive virtual assistants, spot trends in social media, and more.
- Healthcare. Here NLP for business can assist by analyzing patient records and physician’s notes and extracting vital information from them.
- Legal. In the legal field, NLP enables cross-referencing, document analysis, and information extraction, and ensures compliance with relevant laws and regulations.
Probably, there is always one or another way in which NLP can come of great usefulness to any business.
Which are the major categories of NLP technology?
Since natural language processing in artificial intelligence is a rather complex matter, it can be differentiated into several major categories, which are the following:
Natural Language Understanding (NLU)
NLU involves the comprehension and interpretation of human language by machines, which allows for:
- Interpretation of human language
- Context understanding
- Sentiment analysis
- Intent recognition
Basically, it’s the ability of systems to understand context, sentiment, and intent of written and spoken language.
Natural Language Generation (NLG)
NLG is the process of transforming structured data into natural language, which can be used for:
- Creation of human-like text
- Automated report writing
- Creation of chatbots and virtual assistants
- Data-to-text conversion
NLG enables machines to produce human-like content for various needs and applications.
Optical Character Recognition (OCR)
OCR technology works by scanning and analyzing printed or handwritten characters in an image and converting them into machine-encoded text that can be edited and searched. It allows for:
- Conversion of scanned documents
- Text recognition from images
- Editable text creation
- Searchable automated data extraction solutions
OCR is widely used to convert scanned paper documents or PDFs into data in easily scannable digital format.
The benefits of NLP in business
Now, let’s review the advantages of Natural Language Processing usage. We’ll see what value an NLP solution can bring for the investment needed:
- Improved customer satisfaction. Through timely and relevant interactions, businesses can enhance the customer experience, fostering brand loyalty.
- Cost savings. Automation of processes like customer support and data analysis with the help of NLP can significantly reduce operational expenses.
- Competitive advantage. Leveraging NLP gives businesses an edge by tapping into deeper insights and more efficient processes comparing to competitors that are not using the technology.
- Enhanced productivity. Employees can focus on higher-value tasks by offloading repetitive or data-heavy tasks to NLP-driven solutions, boosting overall organizational efficiency.
- Risk mitigation. By monitoring communications and transactions, NLP can identify anomalies that may indicate fraud or other risks, allowing businesses to act proactively.
- Predictive lead scoring. Utilizing NLP for predictive lead scoring allows businesses to more accurately prioritize and engage potential customers, significantly boosting marketing efficiency.
The benefits of implementing natural language processing for business are numerous. The big idea here is to identify what pressing business needs NLP can address and utilize its capabilities accordingly, so you can maximize the value of your investment.
You may additionally discover a related to NLP matter — machine learning-based intelligent underwriting in another article based on the experience of our ML team.
What are business applications of Natural Language Processing?
As of now, NLP technology is used extensively in multiple industries and ways with the prospect of optimizing business performance and driving customer satisfaction. Possible business uses of natural language processing include the following:
- Email filtering. NLP algorithms categorize and sort emails, reduce spam, and highlight important messages, thus improving productivity.
- Language translation. This capability offers real-time language translation services for seamless cross-border communication.
- Patient voice analysis. It enables healthcare providers to interpret patients’ voice-based symptom descriptions, fostering more accurate and efficient diagnoses.
- Electronic Health Records (EHRs) processing. This application of NLP in business enables us to extract meaningful insights from EHRs to enhance patient care and fuel medical research.
For example, IBM Watson — an NLP-based cognitive computer system by IBM, the technology corporation, is used to analyze clinical information, research material, medical evidence from cancer centers, and the Personal Health Information (PHI) of patients. The technology is extensively used by Humana, a leading US provider of Medicare supplements and health insurance. Watsons’ conversational AI exploit by Humana recieve more than 7,000 call from 120 providers per business day.
- Document conversion. Through OCR, different types of documents can be converted into editable and searchable data, improving information accessibility.
- Document analyzing. Understanding and categorizing large volumes of documents makes information management more efficient.
As another example of natural language processing business application, ROSS Intelligence — an NLP-powered legal research platform, is used worldwide to recognize and understand the context, syntax, and meaning of case law. It largely simplifies the document analysis process.
- Smart assistants. With the help of NLP, assistants like Siri or Alexa understand and respond to user commands, enhancing customer service and interaction.
- Online searching. NLP-driven search engines interpret user queries to provide relevant search results, optimizing the user experience.
It’s known that Google uses NLP to provide better search results, personalize advertisements, and more.
- Duplication detection. Duplicate content or plagiarism can be detected, ensuring content originality and legal compliance.
- Social media monitoring. Social media chatter about brands can be monitored, allowing for effective reputation management and customer engagement.
- Automatic insights provision. Using NLP-based algorithms, it’s possible to generate automatic insights from vast data sets, driving decision-making.
- Sentiment analysis. It enables companies to analyze customer reviews and feedback to gauge public sentiment about their products or services and guide strategic decision-making.
The number of applications of NLP in business is high and is gradually increasing as more ways to use smart technology in business operations arise.
Difficulties with implementing NLP for business purposes
As with any other technology, the implementation of NLP may have some pitfalls. Among the most critical you need to be aware of:
- Data privacy and security and regulation compliance. Implementing NLP may be involved with handling sensitive data such as patient medical history and physicians’ notes. Sensitive data must be protected to comply with privacy regulations including GDPR and HIPAA. Mismanagement can lead to legal issues and loss of trust.
- Resource insensitivity. Training and operating NLP models require substantial computational resources. The needed investment and potentially high energy consumption involved can be an entry barrier.
- Data collection and annotation. Gathering and annotating enough quality data for training and testing may be difficult and time-consuming. Using inadequate or biased data obtained in the process of business operations or from open sources can lead to inaccurate or unfair model performance.
- Language complexity and ambiguity. NLP must navigate the intricacies of human language, such as idioms and ambiguities. Understanding subtle meanings across contexts is complex, posing significant challenges for implementation.
- Evaluation and metrics in NLP. Assessing NLP involves dealing with multiple factors like task specificity, intricacies of human language, human judgment alignment, and trade-offs between metrics. Issues with the availability of ground truth data, robustness, generalization of metrics, interpretability, explainability, and scalability add layers of complexity to evaluation.
Additionally, sensitivity to changes, ethical considerations, and alignment with business goals further contribute to the non-trivial nature of selecting and applying the appropriate evaluation metrics in NLP.
Despite the complex nature of NLP developments, partnering with a trusted vendor of ML services will bring your project to success with a high chance.
Intelliarts experience
Here at Intelliarts, we specialize in AI and ML services and related technologies including NLP. Let’s take a look at two of the Natural Language Processing use cases our team had:
B2B job sourcing platform development
A challenge was to ensure high-quality job description parsing and matching candidate profiles with job requirements to streamline candidate sourcing on the platform. Additionally, the solution should incorporate Diversity, Equity, and Inclusion (DEI) principles.
The Intelliarts team built an ML solution that can compare candidate profiles from job boards or social media sites like LinkedIn with the descriptions of positions that companies intend to fill. The solution that includes a semantic search engine that supports multiple search filters, such as age, gender, racial origin, etc., was built with the aid of NLP for ease of searching and matching based on key phrases in textual descriptions. The model shows over 90% accuracy for gender and ethnicity detection.
Case study in federal criminal matters
A challenge was to create a solution capable of assessing how likely people will commit a crime. The assessment is based on the questionnaires examinees fill out in order to complete a security designation form intended to assess mental health stability.
The solution was a specialized ChatGPT prompt. Users can utilize the prompt through a developed application, by uploading filled questionnaires and assessment rules for questionnaires. This way businesses operating in legal fields or lawyers can run a quick and effective assessment of the probability of a particular person committing a crime.
Final take
NLP use cases in business are vast. From email filtering and virtual assistants to social media monitoring, smart technology can aid businesses from multiple domains in numerous ways bringing improved customer experience, cost-efficiency, and increased productivity. However, the complexities of handling NLP development make such projects demanding to qualifications of specialists working on it.
Trusting your NLP project to expert developers is always the right call. With substantial experience in NLP, AI, and ML, the Intelliarts team can assist you with AI development of any complexity or with technical AI consulting.
FAQ
The usage of NLP for business purposes should result in improved operational efficiency, enhanced customer experience, and increased efficiency in the generation of data-driven insights for decision-making.
While NLP facilitates human-computer interaction through the ability to understand context and sentiment, text analytics focuses on understanding language nuances in a text.
NLP can streamline customer interactions with business, conduct sentiment analysis, and process large volumes of data guaranteeing high efficiency of insight generation.
