AI Selection in Organizations: A Decision Framework for Large Language Model and Generative AI Deployments

Artificial intelligence selection has emerged as one of the most impactful decision areas facing modern organizations. As AI adoption has accelerated from experimental use to broad enterprise deployment, the central challenge is no longer whether organizations should adopt AI, but how leaders and technology professionals can make informed, defensible decisions about which AI systems to select, how to govern them, and where they can create sustainable organizational value. The urgency of this challenge is heightened by rapid vendor proliferation, uneven executive technical literacy, growing regulatory expectations, and the increasing operational risks associated with poorly matched AI solutions, including wasted investment, vendor lock-in, unreliable outputs, and uncontrolled shadow AI adoption. This paper addresses that problem by providing a practical and conceptually grounded framework for AI selection in organizations. It first explains how current large language models (LLMs) work, including transformer architecture, training, alignment, fine-tuning, and key limitations, while translating these concepts into plain language for executive audiences. It then examines why technical literacy has become essential for organizational decision-makers responsible for AI strategy, procurement, and governance. Building on that foundation, the paper presents a structured selection framework that evaluates AI systems across eight dimensions: capability assessment, alignment and safety, integration fit, data governance, total cost of ownership, vendor risk and lock-in, governance and compliance, and lifecycle management, aligned with the NIST AI Risk Management Framework (AI RMF 1.0) and ISO/IEC 42001:2023. The paper further introduces an executive decision model for the build, configure, or buy choice, and concludes with strategies for preventing extreme AI proliferation through governance controls, technical safeguards, and workforce education. The contribution of this paper is both strategic and practical. For organizational leaders, it offers a clearer basis for aligning AI decisions with business goals, risk tolerance, compliance obligations, and long-term operating models. For technology professionals, it provides a structured approach to evaluating AI capabilities, deployment options, and lifecycle implications in a way that supports sound architecture and governance decisions. By integrating academic literature with current industry evidence, this paper equips decision-makers with a more informed foundation for selecting AI systems that are not only