No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size
Ashok Urlana, Charaka Vinayak Kumar, Bala Mallikarjunarao Garlapati, Ajeet Kumar Singh, Rahul Mishra
TL;DR
The paper investigates how the challenges of deploying large language models (LLMs) differ with organization size, arguing that one-size-fits-all solutions are ineffective. It combines an industrial case study, a scoping survey of industry publications, and a practical pilot-guide to map scale-specific barriers and propose actionable remedies. Key contributions include categorizing challenges into data confidentiality, reliability, infrastructure, domain adaptation, synthetic data, and ethics, and offering concrete mitigations such as privacy-preserving techniques, retrieval-augmented generation, adapters, and guardrails. The study provides a size-aware roadmap for maximizing LLM utility across small, medium, and large industries, emphasizing open-source options, governance, and cross-sector collaboration to enable responsible and effective industrial deployment. Overall, it advances practical understanding of industrial LLM adoption and informs practitioners and researchers about prioritized directions for future work.
Abstract
Large language models (LLMs) are playing a pivotal role in deploying strategic use cases across a range of organizations, from large pan-continental companies to emerging startups. The issues and challenges involved in the successful utilization of LLMs can vary significantly depending on the size of the organization. It is important to study and discuss these pertinent issues of LLM adaptation with a focus on the scale of the industrial concerns and brainstorm possible solutions and prospective directions. Such a study has not been prominently featured in the current research literature. In this study, we adopt a threefold strategy: first, we conduct a case study with industry practitioners to formulate the key research questions; second, we examine existing industrial publications to address these questions; and finally, we provide a practical guide for industries to utilize LLMs more efficiently. We release the GitHub\footnote{\url{https://github.com/vinayakcse/IndustrialLLMsPapers}} repository with the most recent papers in the field.