Peering Through AI’s Veil: Task-Technology Fit and Its Impact on AI Adoption in Data Centers

This study examines how artificial intelligence (AI) generates operational value in data centers through the Task-Technology Fit (TTF) framework. Using survey data from data center professionals, the study applies covariance-based structural equation modeling (CB-SEM) to test relationships among task characteristics, technology characteristics, managerial competence, organizational support, TTF, AI adoption, and operational efficiency. The findings indicate that task, technology, and managerial factors strengthen TTF, which serves as the key mechanism driving AI adoption. Organizational support also facilitates adoption by creating conditions that enable effective technology integration. AI adoption, in turn, enhances operational efficiency. The study contributes to AI adoption research by showing that AI value depends not only on technological capability but also on alignment with task demands, managerial expertise, and supportive organizational contexts. It offers practical insights for improving AI implementation in complex data center environments.