Top 8 Large Language Model Enterprise Use Cases

Large language models have drawn significant market attention across industries in a very short span of time. If the upward trend continues, research estimates the generative AI industry could grow at a CAGR of 42% and become a $1.3 trillion market by 2032. Even in its infancy, LLMs already have a multitude of enterprise applications — including content production, translation, transcription, market research, customer support, etc.

Moreover, other than natural language processing (NLP) applications, there are many other enterprise use cases for large language models across customer service, marketing and advertising, sales, product research and development, and a lot more.

Top 8 LLM Enterprise Use Cases

While large language models have numerous applications across different business functions, here are key use cases.

1. Customer Service

Generative AI, especially LLMs, holds tremendous potential to transform customer support systems as a virtual agent, leading to improvement in customer satisfaction and higher productivity. According to McKinsey, the incorporation of LLM into customer support can lead to a 45% increase in productivity.

For example, large language models can be trained on companies’ customer data, customer chats, and domain-specific questions and answers to automate interactions with customers. Well-trained virtual assistants can analyze customer queries, and interact with customers in a natural way. As a result, businesses can provide 24/7 customer service without hiring additional human agents.

2. Marketing and advertising

So far, large language models, such as ChatGPT vs Bard, Cohere, etc., have demonstrated exemplary ability to create human-like marketing content, such as emails, messages, ad copies, social media posts, blog posts, articles, etc. Businesses can utilize such content for marketing and advertising requirements. This can help businesses to streamline communications with customers.

Moreover, businesses can use LLMs to evaluate the outputs of marketing campaigns by analyzing customer data on ad campaigns. Accordingly, they can plan future marketing campaigns for better results.

3. Market research

These tools can be used to analyze and draw inferences from large datasets, making them suitable for conducting market research for a better understanding of various elements, such as insights into customers’ persona, competition, market gaps, and other information required for business growth.

4. Sales

LLMs can be employed to automate or sharpen sentiment analysis, especially when dealing with the content available in social media discussions. compared to focused sentiment analysis tools, LLMs are more capable of understanding complex and nuanced customer sentiments. Businesses can leverage LLMs for market research, extracting valuable insights from text data to get a deep understanding of consumer behavior and preferences.

In addition, LLMs can be used to automate various sales activities, such as moving the leads down the sales funnel, analyzing lead quality, and predicting the potential future sales in terms of number and amount.

5. Product research and development

LLM-powered tools have shown immense potential for generating product ideas and facilitating brainstorming. They can perform several tasks, from creating outlines for research projects and expediting interdisciplinary research to retaining research content for easy retrieval.

Moreover, researchers can utilize the technology for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, facilitating improved research outcomes.

6. Code development

Large language models, like Code Llama, not only create human-like content but can be used to write programming codes in several languages like C#, Java, Python, PHP, etc. This helps non-technical users to create basic code snippets, and professionals to save time and resources, allowing them to focus on more complex tasks.

LLM-powered applications can assist in debugging existing code and even generate documentation related to it, saving users the manual effort and time required for documentation writing.

7. Transcription

LLM has demonstrated its ability to accurately convert audio or video files into text. Many companies have been using generative AI models to create transcripts from audio and video files. This eliminates the need for time and effort by humans to transcribe audio/ video files manually.

LLMs perform better than traditional transcription tools because of their natural language processing (NLP) capabilities, which enable them to infer the context and meaning of the spoken content when transcribing the audio into written text.

8. Search

Many people use large language models like ChatGPT and Bard as alternative search tools to access information by asking them questions in natural language. However, LLMs generate outputs based on the data they were trained on. Hence, they have limitations and might not have real-time information sometimes to generate updated information.

Moreover, language models are prone to hallucination and tend to invent facts and figures. Hence, users must cross-check responses produced by LLMs so as not to be misled.

Wrapping up

Despite being a nascent technology, large language models are bringing about transformation in different business functions, leading to improved efficiency, cost savings, and a competitive edge in the marketplace. LLMs can come in handy when you want to summarize or extract insights from large datasets. As the technology develops further, many other groundbreaking innovations and increased efficiency are expected.