Behind the Algorithm: International Insights into Data-Driven AI Model Development

Artificial intelligence (AI) is increasingly embedded within organizational infrastructures, yet the foundational role of data in shaping AI outcomes remains underexplored. This study positions data at the center of complexity, uncertainty, and strategic decision-making in AI development, aligning with the emerging paradigm of data-centric AI (DCAI). Based on in-depth interviews with 74 senior AI and data professionals, the research examines how experts conceptualize and operationalize data throughout the AI lifecycle. A thematic analysis reveals five interconnected domains reflecting sociotechnical and organizational challenges—such as data quality, governance, contextualization, and alignment with business objectives. The study proposes a conceptual model depicting data as a dynamic infrastructure underpinning all AI phases, from collection to deployment and monitoring. Findings indicate that data-related issues, more than model sophistication, are the primary bottlenecks undermining system reliability, fairness, and accountability. Practically, this research advocates for increased investment in the development of intelligent systems designed to ensure high-quality data management. Theoretically, it reframes data as a site of labor and negotiation, challenging dominant model-centric narratives. By integrating empirical insights with normative concerns, this study contributes to the design of more trustworthy and ethically grounded AI systems within the DCAI framework.