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Enterprise Generative AI: The Growing Adoption of LLMs in Production - Blogs


Enterprise Generative AI: The Growing Adoption of LLMs in Production

In the past few years, Large Language Models (LLMs) have evolved significantly, from simple rule-based engines to complex systems like GPT-4 and BERT. Today, these are powerful tools with unimaginable potential, revolutionizing various industries, driving innovation and efficiency. The sophisticated AI models, trained on vast amounts of text data, transcend mere language understanding.

In this blog, we will delve into the enterprise use cases of LLMs, highlighting how companies like Walmart, Stellantis, and Commvault leverage this technology to improve customer experiences, streamline processes, and democratize content creation.

Are there any disadvantages to this revolutionary model? Yes, let’s discuss the stumbling blocks that hinder the widespread adoption of LLMs, and the strategies employed by companies to mitigate them.

Who is Using LLMs in Production?

Gartner’s survey in October 2023 revealed that 55% of corporations were either piloting or releasing LLM projects. This number is expected to grow rapidly.

Large Language Models, ever since becoming the potential game-changers of the modern technology era, are widely employed by innovative tech firms. Explore the importance of modern tech stack, large language models.

Here are several use cases where enterprises are leveraging Large Language Models in their day-to-day operations:

Walmart has implemented an automated voice ordering system that utilizes a combination of cutting-edge AI models. This innovative system enables customers to effortlessly place orders at drive-throughs, improving the overall ordering experience.

Stellantis, on the other hand, focuses on enhancing customer personalization by comprehending and responding to their needs based on the specific context of their car-buying journey. By tailoring its approach, Stellantis aims to provide a seamless and customized experience for each customer.

Commvault has taken a unique approach to content creation by democratizing the process. Their tool allows users, regardless of their technical writing skills, to co-write blogs and prospecting emails for Salesforce. This empowers individuals to produce content that adheres to the company's style guidelines. Additionally, Commvault's tool synthesizes information from various sources such as interviews and case studies, enabling users to succinctly summarize customer pain points, market perspectives, product details, and competitor analysis. Overall, these companies have embraced innovative strategies to enhance their respective industries and provide exceptional customer experiences.

What's the Reason Behind Increased LLM Adoption among Enterprises?

  1. Enhanced Data Accessibility and Interpretation: LLMs democratize data access within organizations. Employees from diverse backgrounds can interact with and extract meaningful insights from enterprise data using simple, natural language queries. This inclusive approach breaks down information silos, enabling more informed decision-making across departments.
  2. Streamlined Customer Interactions and Services: Faster response times and accurate answers lead to happier customers. LLM-powered chatbots provide personalized interactions, improving customer service. They track orders, troubleshoot issues, and enhance overall customer experiences.
  3. Accelerated Innovation and Creativity: LLMs generate creative content, from marketing copy to technical documentation. Writers can focus on strategy while LLMs handle the heavy lifting. It helps enterprises save time and maintain consistent messaging across channels.
  4. Advancing Business Innovation and Agility: Strategic decisions become data-driven and agile as LLMs analyze market trends, financial reports, and customer feedback. Businesses gain a competitive edge by leveraging LLMs for insights and adaptability.

Interesting Generative AI First Solutions

When it comes to digital communication, Generative AI-first solutions have gained substantial prominence in recent times. With the advent of advanced technology below are a few examples of utilizing them:

GPT for Numbers is a powerful tool that simplifies data analysis by providing a user-friendly interface in the form of a “Text Box”. This interface allows users to connect various data sources and ask questions related to the data. The tool generates a response in HTML format, encompassing charts, and explanations, and prompts the user for further questions based on the provided response.

Kognitos, another impressive tool, leverages Natural Language Processing to automate business processes. While the automation scripts may not be entirely in Natural Language, the interface allows users to incorporate natural language and syntax that closely resembles natural language to create automation. For instance, users can utilize natural language to read invoices and extract contents while defining rules for when human intervention may be required in the process.

Ideas to design, commonly referred to as ideation, generates a wide range of delightful ideas. The editable UI is generated from text descriptions.

What are Some of the Main Stumbling Blocks for Adoption?


Reliability is a significant concern for practitioners and companies utilizing Language Model Models (LLMs) in their production processes. LLMs tend to hallucinate, as acknowledged by Nick Frosst, co-founder of Cohere, who quotes, “All it does is hallucinate, it is just amazing how it gets anything right.” The non-deterministic nature of all ML models, coupled with the hallucination potential of LLMs, makes it challenging to ensure their accuracy. While companies like offer tools to detect LLM hallucinations, this remains an area of ongoing research. As a result, incorporating human-in-the-loop solutions and co-pilots that assist in verifying LLM outputs is a promising approach for deploying LLM-powered use cases in production environments.

Privacy Attacks

Privacy attacks have become an ever-present concern in today's digital age. The LLM Model has the potential for generating content that can inadvertently lead to privacy attacks. Imagine where someone maliciously obtains your private data and uses it to train such a model. Asking a query and gaining access to your personal information becomes effortless, breaching the boundaries of privacy.

Bias and Discrimination

A significant concern is the development of large language models. Many LLMs are trained on extensive datasets derived from web content created by humans, which inherently introduces biases into the models themselves. To address this issue, it is crucial to train LLMs on diverse datasets, closely monitor and identify biases in their outputs, and make necessary adjustments. Failure to handle biases appropriately can potentially damage your brand's reputation.


LLMs have the potential to spread misinformation. To prevent this, we must ensure the provision of clean and accurate data, adjust the temperature settings to respond only to the given context, and develop robust foundational models. Implementing these measures can help mitigate the potential for LLMs to disseminate false or misleading information.


The cost of inference and training the models remains considerably high and can be expected to decrease in the near future. However, companies must still substantiate the return on investment (ROI) that this technology will provide.


Despite the challenges, LLMs are capable of driving innovation and improving business outcomes across industries. Ongoing research and integrating human-in-the-loop solutions are crucial to ensure the accuracy and security of LLM-powered applications.

iOPEX Technologies helps businesses leverage LLM to provide cutting-edge cognitive solutions by scaling our AI capabilities across industries, including Telecom, Hi-tech, Manufacturing, Retail Media, Healthcare, and Life Sciences.

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