The AI and Machine Learning Advances Journal works towards becoming a leading journal for AI/ ML research findings. In this way, it performs a function of connecting academic, industrial, top machine learning algorithms and governmental researchers to exchange know-how and innovations that are shaping the development of intelligent systems at the present time.
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This research proposes an API-led connectivity framework to address challenges in digital transformation, particularly scalability, performance, and governance. Traditional integration methods, such as ESB and microservices, struggled with these issues. The study implemented a three-tier architecture consisting of System APIs, Process APIs, and Experience APIs, tested within Enterprise X, which faced performance and scalability issues with legacy integration methods. The framework significantly improved order processing time (73% reduction), API response time (74% reduction), and peak throughput (4.2x increase). It also managed up to 500 concurrent users and a 3.2x increase in order volume without outages. Operational efficiency saw a 35% reduction in maintenance costs, and partner onboarding was faster. The framework provided a scalable, flexible, and secure solution, driving digital transformation and supporting future business growth.
API-Led Connectivity, Enterprise Integration, Scalability, Performance, Modular Architecture, Governance;, Digital Transformation