This viewpoint paper, at the intersection of computer science, information science, and public administration, analyzes the motivations and barriers government agencies encounter when adopting algorithms. Drawing on global examples from literature and published reports, it illustrates how algorithms aid agencies in overcoming human and organizational limitations in processing big data, enabling them to make more informed and responsive decisions for citizens. The primary motivations for algorithm adoption are rooted in external drivers—such as rising data complexity and evolving citizen demands—as well as internal factors like cognitive and organizational constraints. However, agencies face significant barriers: challenges with data stewardship and sharing, resource and policy limitations, the need for operational transparency, staff resistance, and insufficient regulatory frameworks or public support. The paper highlights how algorithms with features such as scalability, pattern recognition, accuracy, alignment with human values, and efficient resource use create public value. These insights provide actionable guidelines for government organizations and their IT partners, emphasizing the importance of thoughtfully designed algorithm-based solutions to enhance digital governance and public service delivery.