Successful Real-World Use Cases For LLMs (And Lessons They Teach)

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.