Shokran, Md and Akter, Fahmida and Bhuiyan, Mohd Salman Hossain and Nafees, Muhammad Farhan (2026) Governing AI Adoption: A Business Management Framework for Risk and Accountability in U.S. Enterprises. American Journal of Economics and Business Management, 9 (10). ISSN 2576-5973

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Abstract

The wide-spread use of Generative Artificial Intelligence (GenAI) has revolutionized company decision making, improving forecasting, fraud detection, workforce planning and performance analytics. Yet in spite of these advantages, a lot of organizations experience severe governance issues in regards to accountability, risk possession, compliance and value realization. Past AI governance studies tend to focus on technical, algorithmic, or regulatory aspects, and provide little actionable advice for business managers, project managers, and business analysts who oversee AI implementation. This study poses a management-oriented framework called Governing AI Adoption: A Business Management Framework for Risk and Accountability in U.S. Enterprises that will work to facilitate responsible implementation of AI without the development of a new technical model. This study draws on a systematic review of the literature on governance of AI, as well as an empirical analysis of the >100,000 enterprise records available in the Enterprise GenAI Adoption & Workforce Impact Dataset. The dataset is used to understand trends in AI adoption, the impact on workforce, employee learning, productivity gains and employee attitudes toward AI within US companies, giving practical insights to companies' outcomes around implementing GenAI. The findings are used to build a Five-Layer Business Governance Framework: Strategic Ownership, Business Risk Classification, Oversight and Escalation Processes, Financial Accountability, Governance Maturity and Continuous Review. The framework utilizes common management practices such as RACI matrices, AI-specific risk registers, stage-gate governance reviews, KPI and ROI dashboards, and governance maturity assessments to help guide decisions through a structured process and enable organizations to take ownership. This study not only serves as a practical governance model for managing AI's strategic impact but also connects the theoretical study of technical AI governance with its real-world implications for business management. The proposed framework sees both academic and managerial contributions by defining a governance approach that is actionable and can facilitate responsible, sustainable and value-based AI usage in modern enterprises.

Item Type:	Article
Uncontrolled Keywords:	Generative Artificial Intelligence (GenAI), AI Governance, Business Management, Enterprise AI Adoption, Risk Management, Organizational Accountability
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	16 Oct 2026 14:49
Last Modified:	16 Oct 2026 14:49
URI:	http://eprints.umsida.ac.id/id/eprint/17211

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