This Forbes Technology Council roundup presents 20 real-world use cases for large language models (LLMs) across diverse industries, contributed by technology executives and leaders. The examples span healthcare, cybersecurity, customer service, software development, pharmaceuticals, marketing, and more. Notable cases include OpenAI's collaboration with a healthcare provider that reduced diagnostic errors by 20%, GitHub Copilot enhancing developer productivity with real-time coding suggestions, Ricoh cutting technician onboarding from 10 weeks to two using a specialized LLM, and a pharmaceutical company saving days to weeks of manual work by automating scientific literature curation.

A recurring theme across the contributions is that success with LLMs depends not just on choosing the right model, but on rigorous data management, domain-specific fine-tuning, and thoughtful workflow integration. Several contributors emphasize that LLMs work best when they augment human capabilities rather than replace them — freeing teams to focus on higher-impact, strategic work. The article also highlights that organizations of all sizes can benefit, as many of these use cases leverage publicly available models rather than requiring massive proprietary training datasets. From automating claims processing to generating test data, the examples collectively illustrate that LLMs deliver the most value when applied to well-defined, labor-intensive processes where language understanding and data synthesis are key bottlenecks.

MIT Sloan professor Rama Ramakrishnan outlines a three-step framework for identifying the right generative AI use cases in business. The approach begins with breaking jobs into discrete tasks, since not all tasks within a role are equally suited for LLM automation — preparing course materials can be partially automated, while moderating discussions cannot. Next, organizations should apply a generative AI cost equation that accounts not just for licensing fees but also the less obvious costs of achieving the required accuracy, building error-detection mechanisms, and managing reputational risk. If the total cost of AI automation is lower than the status quo, the third step is to build, launch, and rigorously evaluate pilots, choosing from vendor solutions, commercial models like GPT-4, or open-source options like Llama 3.

Ramakrishnan also offers several best practices for successful implementation: maintain a robust error-checking process with a human in the loop, consider narrow and targeted use cases that require smaller and cheaper models, cultivate AI talent outside traditional data science teams, and prioritize low-stakes, high-volume use cases for quick ROI and organizational learning. He emphasizes that tasks failing the cost equation today should be revisited periodically, as rapidly improving LLM capabilities are steadily driving down adoption costs. The overarching advice is to avoid paralysis by starting with easy wins and scaling from there.

This article from Evidently AI compiles 55 real-world examples of how major companies are deploying large language models (LLMs) in production, organized across industries including e-commerce, fintech, tech, delivery, social media, and B2C. Each example includes a brief description and a link to the original source material. The use cases span a wide range of applications: Instacart builds internal AI assistants and generates food product images, Walmart extracts product attributes from PDFs, Grab uses LLMs for data classification and automated reporting, GitHub Copilot provides code suggestions, DoorDash extracts product details and builds support chatbots, and LinkedIn automates hiring tasks with an agent-based assistant.

The examples reveal several recurring patterns in enterprise LLM adoption. Many companies use RAG (Retrieval-Augmented Generation) for knowledge retrieval and customer support — including Thomson Reuters, Uber, and RBC. Content moderation is another common theme, with Yelp, Zillow, and Whatnot leveraging LLMs to detect inappropriate, discriminatory, or fraudulent content. Search relevance improvements appear across Leboncoin, Faire, Picnic, and Grab, while incident management and software testing use cases feature prominently at Google, Microsoft, Meta, and Uber. The collection serves as a practical reference for teams exploring LLM integration, highlighting that while the technology is powerful, building reliable production systems requires careful engineering, evaluation, and human oversight.