AI Solutions

This chapter provides a comprehensive overview of AI and its applications in an organizational context. It begins by explaining the basic principles of AI and the distinction between strong and weak AI. To illustrate this distinction, we use Searle’s “Chinese Room Argument”, a thought experiment that helps us understand the difference between a system that merely processes information (weak AI) and one that truly understands it (strong AI). The chapter covers the main branches of AI in detail, including ML, deep learning (DL), natural language processing (NLP), and computer vision. ML is divided into four main categories: supervised, unsupervised, semi-supervised, and reinforcement learning, which explain how each category processes data differently. The chapter emphasizes the crucial role of structured and unstructured data in ML applications, ensuring the audience knows the key factors in AI development. Particular attention is paid to emerging technologies such as generative AI (GAI), which refers to AI systems that can create new content, and Foundation Models (FMs), especially Large Language Models (LLMs) and Vision Foundation Models (VFMs) (Awais et al., 2025). These Foundation Models are large-scale AI models that serve as the basis for various AI applications. The chapter explains how these technologies will revolutionize various industries through their ability to understand and generate content. The chapter concludes with essential considerations about AI reliability, addressing three critical challenges: bias in AI systems, hallucination (generating false information), and explainability (understanding AI decision processes). The chapter also provides practical examples of AI applications in various sectors, such as healthcare, finance, and logistics. These examples provide knowledge about AI’s practical applications and reassure the audience about its real-world relevance and potential in different industries.