Strategic Decision Framework for Enterprise LLM Adoption
Michael Trusov, Minha Hwang, Zainab Jamal, Swarup Chandra

TL;DR
This paper introduces a systematic six-step decision framework to guide organizations in adopting and implementing Large Language Models effectively and securely across diverse business contexts.
Contribution
It presents a novel, practical framework based on interviews and analysis to assist organizations in making informed LLM adoption decisions.
Findings
Framework covers application selection to deployment
Provides guidance on security, infrastructure, and compliance
Includes real-world examples from multiple industries
Abstract
Organizations are rapidly adopting Large Language Models (LLMs) to transform their operations, yet they lack clear guidance on key decisions for adoption and implementation. While LLMs offer powerful capabilities in content generation, assisted coding, and process automation, businesses face critical challenges in data security, LLM solution development approach, infrastructure requirements, and deployment strategies. Healthcare providers must protect patient data while leveraging LLMs for medical analysis, financial institutions need to balance automated customer service with regulatory compliance, and software companies seek to enhance development productivity while maintaining code security. This article presents a systematic six-step decision framework for LLM adoption, helping organizations navigate from initial application selection to final deployment. Based on extensive…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Electronic Health Records Systems · Robotic Process Automation Applications
