Leverage Large Language Models for Your Business: 10 ideas, 30 use cases

AI • Aug 01, 2023

Businesses in the digital space can’t afford to ignore the power of large language models.

With the emergence of advanced artificial intelligence models, businesses and brands have an unprecedented opportunity to automate manual processes, maximise operational efficiency and deliver exceptional customer experiences.

In this blog post, we explore 10 ways your business can harness these models to unlock the full potential of AI-powered communication. Get inspired by the use cases you are about to see.

Contents of this blog post

What are large language models, and what can businesses gain from them?
10 ways businesses can leverage Large Language Models (with use case examples)

Text Analytics
Content Generation and Automation
Language Translation and Cross-Lingual Communication
Personalization and Recommendation Systems
Voice Assistants and Interactive Chatbots
Information Retrieval and Knowledge Management
Sentiment Analysis and Opinion Mining
Conversational AI and Contextual Response Generation
Virtual Training and Simulation
Troubleshooting and Triage

What are large language models, and what can businesses gain from them?

Large language models (LLMs) are sophisticated artificial intelligence systems with extensive training on massive volumes of text data to achieve an impressive capability: understanding and generating human-like language.

These models (exemplified by OpenAI's ChatGPT-3, for instance) are constructed with deep neural networks of billions of parameters.

By processing diverse text sources during training, these models develop the ability to recognise intricate linguistic patterns, comprehend contextual nuances, and generate coherent and contextually relevant text.

Artificial intelligence systems allow businesses to streamline their operations and cultivate deeper customer connections while staying at the competitive edge of technology.

10 ways businesses can leverage large language models (with use case examples)

There are certainly more than 10 ways for businesses to exploit the immense power of large language models. Here are some of the most popular areas.

1. Text Analytics

Natural Language Processing (NLP) based on large language models focuses on understanding and interpreting textual data, enabling tasks such as preference analysis, named entity recognition, and language understanding.
Text Analytics enables us to extract valuable insights and patterns from textual data, encompassing tasks like sentiment analysis, topic modelling, and information extraction.

USE CASE EXAMPLES:

• Intelligent Fraud Detection: By applying NLP and Text Analytics techniques to analyse textual data such as financial transactions, customer communications, and insurance claims, businesses can identify patterns and anomalies indicative of fraudulent activities. They enhance their fraud detection mechanisms, mitigate risks, safeguard their operations, protect their financial well-being and secure the environment for their customers.

• Brand Reputation Management: NLP and Text Analytics can assist businesses in monitoring and managing their brand reputation by analysing textual data from social media, customer reviews, and news articles. Through sentiment analysis, topic extraction and opinion mining, businesses can get real-time insights into public perception. They can identify potential issues or crises, maintain a positive brand image and increase customer trust.

• Healthcare Data Analysis: Leveraging NLP and Text Analytics, businesses in the healthcare industry can analyse vast amounts of medical literature, patient records, and clinical notes to extract valuable insights. This analysis can assist in medical research, identifying trends, improving diagnoses, predicting patient outcomes, optimising treatment plans and improving patient care.

2. Content Generation and Automation

Many businesses leverage LLMs to automatically create engaging and relevant textual content, such as blogs, articles, product descriptions, and social media posts.
Automation enables businesses to streamline workflows, reduce manual effort, and ensure consistency in content production by streamlining tasks such as content scheduling, updates and adaptation for different platforms.

USE CASE EXAMPLES:

• Social Media Management: Large language models can be employed to automate the generation of social media content, including posts, captions, and hashtags. By analysing trending topics, user engagement patterns and brand preferences, businesses can automate the creation of compelling and relevant social media content, ensure a consistent presence and drive audience engagement across platforms.

• Email Marketing Campaigns: Businesses can utilise large language models to automate the generation of personalised email content. These models can generate tailored email sequences by leveraging customer data (such as demographics, purchase history and preferences). They can also deliver targeted and engaging messages to customers at scale, streamline the email marketing process and enhance customer engagement.

• News and Blog Content Creation: Large language models can assist businesses in automating the creation of news articles, blog posts, and other written content. By analysing relevant data, such as industry trends, user preferences, and search engine optimisation (SEO) insights, these models can generate high-quality SEO-friendly content, save time and resources for businesses and help maintain consistency and relevance. It is important to check and modify content from tools like ChatGPT, to avoid the risk of misinformation.

3. Language Translation and Cross-Lingual Communication

Language Translation leverages large language models' advanced language understanding and generation capabilities to automatically and accurately translate textual content from one language to another.
Cross-Lingual Communication involves utilising these models to bridge language gaps and enable seamless communication between individuals or businesses speaking different languages.

USE CASE EXAMPLES:

• Global Customer Support: Large language models can provide real-time language translation in customer support interactions, enabling businesses to communicate with customers across different geographies. They can enhance customer experience, resolve issues more efficiently and ensure seamless communication with a diverse customer base.

• Multilingual Content Localization: Large language models can assist businesses in localising their content for different target markets. By automatically translating website content, product descriptions, and marketing materials, businesses can reach a broader audience, engage customers in their native language, expand their global presence and connect with customers on a deeper level.

• International Collaboration and Communication: Large language models enable effective cross-lingual communication and collaboration between teams and partners who speak different languages. By providing real-time translation services in meetings, conferences and written communication, businesses can break language barriers, foster collaboration and facilitate knowledge exchange across diverse international teams.

4. Personalization and Recommendation Systems

Personalisation powered by large language models can help tailor user experiences based on individual preferences, behaviour and historical data, enabling businesses to deliver customised content.
Recommendation Systems utilise large language models to analyse user data, identify patterns, and provide personalised recommendations.

USE CASE EXAMPLES:

• E-commerce Product Recommendations: By employing large language models, businesses can personalise the shopping experience for customers by analysing their browsing behaviour, purchase history and preferences. They can generate tailored product recommendations, increase the likelihood of conversions, enhance customer satisfaction, drive sales and foster long-term customer loyalty.

• Content Streaming Platforms: Large language models can power recommendation systems on streaming platforms by analysing user preferences, viewing history, and content interactions. This enables the delivery of personalised recommendations to users for movies, TV shows, or music. Users can be helped to discover new content, prolong their engagement on the platform and enjoy their entertainment experience.

• News and Content Aggregation: Businesses can leverage large language models to curate personalised news feeds and content recommendations for users. Tailored news articles, blog posts, or other relevant content can be provided by analysing user interests, reading habits, and content preferences. This can ensure a customised information experience, improve user engagement and establish the business as a trusted source of information.

5. Voice Assistants and Interactive Chatbots

Voice Assistants that use large language models enable hands-free, voice-based interactions with users, allowing them to perform tasks, answer questions and provide personalised assistance.
Interactive Chatbots engage in text-based conversations, simulating human-like interactions to assist users and deliver relevant information, advice or guidance.

USE CASE EXAMPLES:

• Customer Support and Service: Voice Assistants and Interactive Chatbots can be deployed to handle customer queries, provide product information, and offer personalised assistance. Businesses can offer 24/7 support, respond to real-time customer inquiries, and resolve common issues efficiently. All this enhances customer satisfaction, reduces response times, and improves customer support experiences.

• Virtual Sales and E-commerce Assistance: Voice Assistants and Interactive Chatbots can guide customers through the sales process, answer product-related questions, and provide recommendations based on customer preferences. Businesses can offer personalised product suggestions, handle customer inquiries, and facilitate seamless purchases to improve conversions and shopping experiences.

• Appointment Scheduling and Booking: Voice Assistants and Interactive Chatbots can streamline scheduling appointments and making reservations. Businesses can provide automated assistance in booking services, checking availability, and managing appointment calendars. This saves business and customers time, enhances convenience, and improves overall efficiency in scheduling.

6. Information Retrieval and Knowledge Management

Information Retrieval using large language models facilitates the extraction of relevant information from diverse sources, enabling quick and accurate access to specific data or documents.
Knowledge Management leverages large language models to organise and structure knowledge repositories, facilitating effective knowledge sharing and discovery within organisations.

USE CASE EXAMPLES:

• Enterprise Search and Information Access: Large language models can power advanced search capabilities within organisations, allowing employees to quickly retrieve relevant information from vast repositories of data, documents and knowledge bases. This enhances productivity, reduces time spent on information retrieval and enables employees to make informed decisions based on comprehensive and up-to-date information.

• Knowledge Base Creation and Management: Large language models can assist businesses in building and managing knowledge bases, enabling effective knowledge sharing and collaboration. By leveraging these models, businesses can automatically extract relevant information from various sources, structure knowledge meaningfully, and provide easy access to internal knowledge resources. This fosters knowledge discovery, improves problem-solving, and promotes organisational innovation.

• Expertise Sharing and Collaboration: Large language models can facilitate expertise identification and collaboration by analysing internal documents, communication channels, and employee profiles. Businesses can match employees with relevant expertise to specific projects or inquiries, foster collaboration and promote knowledge exchange. Organisational performance on complex projects gets enhanced.

7. Sentiment Analysis and Opinion Mining

Sentiment Analysis using large language models can help identify and classify emotions, attitudes, and opinions, enabling businesses to understand public perception and sentiment towards their products, services or brand.

USE CASE EXAMPLES:

• Brand Reputation Management: By employing large language models, businesses can analyse public sentiment and opinions on various platforms, such as social media, review sites, and forums. This enables them to monitor and manage their brand reputation effectively, identify potential issues and address customer concerns promptly. Businesses can enhance their brand image, improve customer satisfaction and build stronger customer relationships.

• Market Research and Product Development: Large language models can be utilised to analyse customer feedback and opinions about existing products or services. By extracting valuable insights from customer reviews, surveys, and social media discussions, businesses can identify emerging trends, understand customer preferences and gather feedback for future product development and innovation.

• Customer Experience Enhancement: Sentiment analysis and opinion mining can be leveraged to assess customer sentiment and satisfaction throughout the customer journey. By analysing customer feedback and interactions, businesses can identify pain points, areas of improvement, and opportunities for enhancing the overall customer experience. This enables businesses to proactively address customer concerns, personalise their offerings, and deliver exceptional customer service.

8. Conversational AI and Contextual Response Generation

Conversational AI involves using large language models to deploy intelligent chatbots and virtual assistants that can understand and respond to user queries and requests in a conversational manner.
Contextual Response Generation focuses on generating responses that are not only accurate but also contextually appropriate, taking into account the ongoing conversation and user intent.

USE CASE EXAMPLES:

• Customer Support and Assistance: By employing Conversational AI and Contextual Response Generation, businesses can enhance their customer support capabilities by providing intelligent and interactive chatbot-based assistance. These chatbots can understand user queries, provide relevant information and assist with common issues, offering quick and personalised responses. This reduces support costs and enables round-the-clock support.

• Virtual Sales and Product Recommendations: Conversational AI and Contextual Response Generation can create virtual sales assistants that interact with customers, understand their preferences, and provide tailored product recommendations. These assistants can engage in natural language conversations, ask relevant questions, and suggest suitable products based on customer needs. They can help boost sales and improves customer engagement.

• Interactive Survey and Feedback Collection: Conversational AI and Contextual Response Generation can be leveraged to create interactive chatbots for conducting surveys and collecting feedback. These survey chatbots can ask targeted questions, gather valuable customer insights and logically organise contextually relevant responses. They can also help analyse the feedback received and assist with post-survey data-driven decisions.

9. Virtual Training and Simulation

Virtual Training based on large language models involves using virtual environments and simulations to provide hands-on training and skill development in a simulated setting.
Simulation allows users to replicate real-world scenarios and practice decision-making in a safe and controlled environment.

USE CASE EXAMPLES:

• Healthcare Training and Medical Simulations: Large language models can power virtual training and simulation platforms that allow healthcare professionals to practice complex medical procedures, such as surgeries or emergency interventions, in a realistic virtual environment. By leveraging virtual training and simulation, businesses in the healthcare sector can reduce training costs, minimise risks and foster learning in a practical, engaging and experiential way.

• Safety and Emergency Response Training: Large language models can be used to develop virtual training and simulation programs for safety and emergency response scenarios. Businesses can train their employees on handling critical situations, such as fire emergencies, natural disasters or security breaches, in a simulated environment. By utilising virtual training and simulation, businesses can enhance employee preparedness, minimise risks, and improve safety protocols.

• Manufacturing and Industrial Training: Virtual training and simulation can be employed in the manufacturing and industrial sectors to provide hands-on training for operating complex machinery, equipment or processes. Large language models can power realistic simulations, allowing employees to practice tasks, identify potential risks and optimise operational efficiency in a virtual environment. This enables businesses to enhance employee skills, reduce training costs and improve productivity, while ensuring a safe working environment.

10. Troubleshooting and Triage

Troubleshooting using large language models can deliver automated and contextually relevant solutions, guiding users through troubleshooting steps or providing self-service options.
Triage involves prioritising and categorising user queries or requests based on urgency and severity, ensuring prompt attention to critical issues.

USE CASE EXAMPLES:

• Customer Support Ticket Triage: Large language models can automatically triage customer support tickets based on urgency and complexity. By analysing the content of the tickets and applying intelligent classification algorithms, businesses can prioritise and route tickets to the appropriate support teams or escalate critical issues for immediate attention. This streamlines the support process, ensures efficient handling of customer queries and improves response times.

• Automated Troubleshooting Guides: With the help of large language models, businesses can develop automated troubleshooting guides that provide step-by-step instructions to resolve common customer issues. By understanding the user query or problem description, the system can generate relevant and contextually appropriate solutions or suggest relevant resources. This reduces the need for human intervention and improves customer satisfaction.

• Knowledge Base Search and Self-Service: Large language models can power intelligent search systems that allow users to find answers to their questions or access relevant information from the company's knowledge base. Businesses can deliver accurate and contextually relevant results by understanding user queries in natural language and applying semantic search algorithms. This pares down customers’ reliance on support agents and improves the overall customer experience.