Artificial intelligence (AI) is increasingly recognized for its capacity to transform medicine. While publications applying AI in allergy and immunology have increased in number, clinical implementation substantially lags behind other specialties. By mid-2024, over 1,000 US Food and Drug Administration–approved AI-enabled medical devices existed, but none specifically addressed allergy and immunology. This gap partly reflects the field’s limited reliance on imaging, which facilitated early AI breakthroughs in radiology and pathology. This narrative review examines recent AI developments, including large language models and AI agents, evaluating their applicability to allergy and immunology practice. We analyze current and potential applications, emphasizing those demonstrating clinical value while identifying implementation barriers amplified by allergic diseases’ unique complexities, including data privacy concerns, bias, reliability constraints, and evolving regulatory frameworks. To bridge the persistent research-to-implementation gap, we propose a 6-point road map: (1) prioritize impactful applications, (2) define clinically relevant benchmarks, (3) enforce rigorous governance, (4) transition to operationalization, (5) promote clinical adoption through trustworthy AI, and (6) establish life cycle management. This road map builds on established implementation frameworks while incorporating critical field-specific considerations unique to allergy and immunology. Through this approach, we provide a perspective for advancing AI in allergy and immunology from academic promise to tangible clinical benefit.