[论文解读] Strategic AI adoption in SMEs: A Prescriptive Framework

Ahmar Kamal Hussain, Rana Rizwan|arXiv (Cornell University)|Aug 5, 2024

Big Data and Business Intelligence被引用 6

一句话总结

提出分阶段、规定性框架，帮助中小企业逐步采用AI，解决由认知到内部生成式与判别式AI的成本、技能和接受度等障碍。

ABSTRACT

Artificial Intelligence (AI) is increasingly acknowledged as a vital component for the advancement and competitiveness of modern organizations, including small and medium enterprises (SMEs). However, the adoption of AI technologies in SMEs faces significant barriers, primarily related to cost, lack of technical skills, and employee acceptance. This study proposes a comprehensive, phased framework designed to facilitate the effective adoption of AI in SMEs by systematically addressing these barriers. The framework begins with raising awareness and securing commitment from leadership, followed by the adoption of low-cost, general-purpose AI tools to build technical competence and foster a positive attitude towards AI. As familiarity with AI technologies increases, the framework advocates for the integration of task-specific AI tools to enhance efficiency and productivity. Subsequently, it guides organizations towards the in-house development of generative AI tools, providing greater customization and control. Finally, the framework addresses the development of discriminative AI models to meet highly specific and precision-oriented tasks. By providing a structured and incremental approach, this framework ensures that SMEs can navigate the complexities of AI integration effectively, driving innovation, efficiency, and competitive advantage. This study contributes to the field by offering a practical, prescriptive framework tailored to the unique needs of SMEs, facilitating the successful adoption of AI technologies and positioning these organizations for sustained growth in a competitive landscape.

研究动机与目标

• 激发中小企业对 AI 的采用需求并识别常见障碍（成本、技能、员工接受度）。
• 提出一个结构化、分阶段的框架，以引导中小企业进行AI采用。
• 使中小企业能够通过逐步提升能力的工具来建立在AI方面的能力和信心。
• 提供从通用工具到任务特定及内部AI解决方案的实际路径。
• 展示规定性框架如何推动中小企业的创新与竞争优势。

提出的方法

• 概述一个分阶段框架，先从领导层的认知和承诺开始。
• 建议以低成本、通用的AI工具为起点，建立技术能力和对AI的积极态度。
• 随着熟悉度提升，建议整合面向任务的AI工具以提高效率。
• 指南逐步在内部开发生成式AI工具，以实现定制与控制。
• 处理针对高度具体、精确任务的判别式AI模型开发。

实验结果

研究问题

• RQ1 中小企业在采用AI方面存在哪些障碍，如何有系统地解决？
• RQ2 中小企业如何在有结构的方式下从基本认知发展到高级AI能力？
• RQ3 哪种AI工具与能力的序列最有利于中小企业能力建设和竞争优势？

主要发现

• 提出一个切合中小企业独特需求的实用、规定性框架。
• 描述从认知到内部生成式AI和判别式模型的分阶段路径。
• 强调渐进式能力建设以应对AI采用的复杂性。
• 论证有结构的进展可以促进创新、效率和竞争优势。