Journal of Advances in Mathematics and Computer Science

This research investigates the transformative role of artificial intelligence (AI) and generative AI in revolutionizing application integration and API development, addressing limitations of traditional methods and fostering intelligent, adaptive workflows. The study analyzes challenges in conventional integration such as data complexity, rigid workflows, skills shortages—and evaluates the applicability of AI technologies, including machine learning and natural language processing (NLP), while assessing organizational outcomes. A comprehensive literature review highlights AI’s evolution from monolithic to adaptive architectures, emphasizing advancements in middleware optimization and API lifecycle management, while noting gaps in model reliability and ethics. The AI-Enabled Integration Capability Framework (AICF) maps AI technologies to integration challenges, using thematic exploration to align with objectives. Findings show AI significantly reduces manual effort in schema mapping, enhances anomaly detection accuracy, and improves system throughput, enabling citizen development and reducing reliance on specialized skills. However, challenges like data quality and cultural resistance necessitate robust governance and training. The research concludes that AI shifts integration to dynamic, learning-enabled systems, delivering faster implementation, higher compliance, and greater agility. Recommendations advocate adopting the AICF, emphasizing advanced NLP for conversational interfaces, federated learning for privacy-preserving integration, and edge AI for low-latency scenarios. Investments in explainable AI and green optimization are crucial for sustainable, scalable solutions, ensuring organizations remain competitive in a rapidly evolving digital landscape.