As battery technologies become central to the global energy transition, optimizing their performance during first-life use is essential for maximizing value and enabling circular economy pathways. First-life electric vehicle (EV) battery operations—including deployment, usage, maintenance, and early-stage diagnostics—are increasingly influenced by advanced digital technologies, data management practices, and artificial intelligence (AI). Despite rapid technological advances, significant research and implementation gaps remain in integrating data-driven approaches and AI models into operational decision-making and lifecycle optimization. This paper addresses these challenges through an exploratory qualitative study, drawing insights from three expert workshops involving battery ecosystem actors. Our analysis identifies four key thematic areas: (1) battery lifecycle optimization, (2) risk and responsibility distribution, (3) data ownership and interoperability, and (4) AI deployment and cybersecurity. The findings highlight tensions between short-term operational cost-efficiency and long-term battery health, the fragmentation of risk management responsibilities, and growing concerns around data sovereignty and AI system integrity. Based on these insights, we propose a guiding framework for smarter first-life EV battery operations, structured around four pillars and supported by four cross-cutting enablers. This study contributes to the emerging discourse on battery circularity by advancing the understanding of strategies for smarter first-life battery operations.