ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR BUSINESS

Abstract

artificial intelligence (AI) and machine learning (ML) have become general-purpose technologies that are reshaping how organisations make decisions, serve customers, design products and run operations. This chapter provides a management-oriented treatment of the principal AI and ML techniques and of the organisational conditions under which they create business value. It first positions AI as a business capability that lowers the cost of prediction and enables both automation and augmentation of human work. It then explains the fundamental concepts of machine learning, including features and labels, training and testing, generalisation, overfitting and the bias–variance trade-off, before examining the major families of algorithms: supervised learning methods such as regression, decision trees, support vector machines and ensemble models; unsupervised methods such as clustering, association rule mining and dimensionality reduction; and reinforcement learning. Deep learning architectures and generative AI, including large language models and retrieval-augmented generation, are discussed with reference to emerging empirical evidence on productivity. The chapter sets out the ML project lifecycle, model evaluation metrics and MLOps practices, and examines explainability and fairness as conditions for responsible deployment. It then addresses AI strategy, including use-case prioritisation, build-versus-buy decisions, adoption theories and operating models, and discusses human–AI collaboration and the Indian business context. The chapter argues that sustained value arises when appropriate techniques are matched to well-framed business problems and embedded in processes, skills and governance.