This study examines AI-driven solutions for agricultural data interoperability through a combined empirical–theoretical approach, integrating stakeholder input with peer-reviewed research comprising two stakeholder workshops, one involving 28 participants and another involving 50 participants (encompassing agri-tech, farming professionals, policymakers, researchers, professors and industry experts), alongside a literature review of 50 peer-reviewed publications. The workshop revealed that structural incompatibility, semantic mismatches, and reliance on manual processes are major operational bottlenecks. Using thematic analysis of workshop discussions and literature synthesis, we identify five critical interoperability stages: Data Ingestion, Standardization, Ontology Alignment, Integration, and Access/Sharing. By systematically reviewing literature, we identified AI techniques, such as anomaly detection, semantic mapping, and rule-based automation, that directly address these stakeholder-validated challenges, forming the foundation of a sequential AI-driven framework. Findings highlight the need for adaptive AI tools to automate processes like schema alignment and ontology mapping, to reduce preprocessing time. This work bridges theoretical frameworks with practical needs, offering actionable insights for scalable smart farming solutions.