The integration of artificial intelligence into diabetes care stands at a critical juncture where technological promise meets regulatory uncertainty. As diabetes cases surge toward a projected 20-year increase, healthcare systems face mounting pressure to optimize treatment decisions while minimizing the therapeutic delays that currently plague patient outcomes. The European Diabetes Forum assembled clinical experts to address a fundamental paradox: AI systems designed to learn and adapt may outgrow the static regulatory frameworks originally used to approve them. Their consensus roadmap reveals how dynamic machine learning algorithms could transform diabetes management from reactive to predictive care, but only if oversight mechanisms evolve alongside the technology. The working group identified primary care physicians as the crucial gatekeepers for AI adoption, since they manage the majority of diabetes patients yet often lack specialized endocrine expertise. Current clinical decision support systems rely on rigid protocols, while AI-driven alternatives could personalize treatment recommendations based on continuous glucose monitoring data, medication adherence patterns, and individual metabolic responses. However, the adaptive nature of these systems creates unprecedented regulatory challenges. Unlike traditional medical devices with fixed performance characteristics, AI algorithms may drift from their original validated state as they process new patient data. The experts emphasize that successful implementation requires not just technological sophistication but fundamental changes to how medical AI is evaluated, monitored, and updated throughout its lifecycle. This roadmap represents an early attempt to bridge the gap between AI innovation and practical diabetes care delivery.