A Human-Centered Approach to the Implementation of Artificial Intelligence for Data-Driven Decision Optimization in Digital Transformation

Joko Iskandar; Kartika Yulianto²; Laras Fadillah

Authors

Joko Iskandar Institut Teknologi Sepuluh Nopember Author
Kartika Yulianto² Institut Teknologi Sepuluh Nopember Author
Laras Fadillah Universitas Indonesia Author

Keywords:

Artificial intelligence, Human-centered ai, Data-driven decision-making, Digital transformation, Decision optimization

Abstract

This study aims to explore a human-centered approach to the implementation of artificial intelligence for data-driven decision optimization in digital transformation. The study addresses the growing use of AI, data analytics, dashboards, and decision support systems in organizations, while highlighting the need to understand how humans interpret, trust, and negotiate AI recommendations in real decision-making processes. This research used a qualitative case study design involving 18 to 24 organizational actors, including leaders, digital transformation managers, information technology staff, data analysts, operational decision-makers, and direct users of AI systems. Data were collected through semi-structured interviews, limited participant observation, and document analysis. The data were analyzed using reflexive thematic analysis. The findings reveal five main themes: AI as a decision support instrument rather than a human replacement, trust and transparency in AI recommendations, human competence and adaptation, organizational culture toward data-driven decisions, and ethical control in AI-supported decision-making. These findings show that AI can improve decision quality when supported by human interpretation, data literacy, participatory culture, and clear accountability mechanisms. The study contributes to human-centered AI and digital transformation literature by emphasizing that successful AI implementation requires technical capability, human readiness, and ethical governance. The findings also suggest that organizations should strengthen explainability, user training, data governance, and human oversight in AI-supported decision-making.

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