AI-First Development Explained: Core Concepts That Underpin a New Paradigm for Modern Software Development

This IDC Market Perspective discusses the significance of AI-first development. This paradigm embeds intelligence into applications through machine learning frameworks, natural language processing (NLP), predictive analytics, and foundation models, allowing developers to deliver and modify intelligent capabilities effectively. By breaking down applications into smaller, independent services, known as microservices, AI-first development promotes modularity and scalability, facilitating easier AI integration. It emphasizes data-centric insights, enabling developers to manage training data for machine learning models and valuing both structured and unstructured data sets. In addition, it leverages DevOps, MLOps, and other tools to automate technology updates, ensuring applications can quickly adapt to environmental changes. Continuous learning is another critical aspect, allowing applications to evolve by learning from new data, user feedback, and real-world interactions, thereby improving performance over time. "AI-first development is a transformative paradigm that integrates intelligence as a core attribute of applications from the outset, ensuring they are smart, adaptable, and scalable," notes Arnal Dayaratna, research vice president, Software Development at IDC.