On-device artificial intelligence (AI) represents a transformative paradigm shift that brings computational intelligence directly to edge devices, eliminating the need for cloud connectivity and enabling real-time, privacy-preserving AI processing. This introductory chapter intends to explore on-devices examining its fundamental concepts, software and hardware dependencies, modernized algorithmic development approaches and notable benefits using real-world use cases. The chapter begins with the definition of on-device AI, which helps in establishing its key differences from a traditional cloud-based approach, especially its critical role in modern computing systems. It further mentions unique algorithmic approaches, current hardware challenges including memory limitations, computational constraints, energy efficiency requirements and hardware heterogeneity issues that developers must navigate through. Further in this chapter, a case study of on-device AI development for mobile phones is presented, with critical considerations highlighted for real-world deployment. Mobile AI models require optimization to counter compute and memory limitations, along with minimizing battery impact. Along with a generic optimization workflow, a brief introduction of Qualcomm, MediaTek and Apple’s platform specific workflows is laid out. Key pointers involved in performance analysis of AI pipelines deployed on mobile are also discussed. The chapter concludes with a forward-looking analysis of how on-device AI could evolve in near future, examining hardware roadmaps, algorithmic advancements, and emerging application domains. With detailed examples and practical case studies, this chapter provides readers with both theoretical understanding and practical insights into the potential of on-device AI, thus presenting complex technical concepts as accessible topics to newcomers while offering valuable perspectives for experienced professionals.