Graduation Year

2025

Document Type

Dissertation

Degree

D.B.A.

Degree Granting Department

Information Systems and Decision Sciences

Major Professor

Steven Currall, Ph.D.

Co-Major Professor

Hemant Merchant, Ph.D.

Committee Member

Clinton Daniel, Ph.D.

Committee Member

Michael Mondello, Ph.D.

Keywords

Change Management, Compliance, Governance, Innovation, Organizational Change, Technology Implementation

Abstract

Machine learning (ML) technologies have the potential to revolutionize regulated electric utilities by improving operational efficiency, enabling predictive maintenance, and optimizing energy management. Despite these advantages, the adoption of ML in this sector lags other industries due to technical, organizational, and regulatory barriers. This research, grounded in the Technology-Organization-Environment (TOE) framework, explores these barriers to uncover actionable solutions for integration. The study identifies key challenges, including explainability, cybersecurity, workforce resistance, and regulatory ambiguity to ML adoption in electric utilities. Utilizing an exploratory qualitative methodology, this approach integrates insights from the literature and industry interviews to rank barriers by frequency, severity, and ease of mitigation. Findings reveal that trust in ML systems, workforce readiness, and regulatory compliance remain critical issues. In this research, the TOE framework is extended to regulated industries, providing utility leaders with strategies for addressing barriers and policymakers with insights into adaptive regulatory reforms. By focusing on the unique challenges of regulated utilities, this study provides a roadmap for leveraging machine learning (ML) to modernize operations while maintaining compliance, security, and public trust.

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