Artificial Intelligence Application: A Multi‑Sectoral Review

Abstract

Artificial Intelligence (AI) has become a disruptive general-purpose technology that is changing institutional, organizational, and domestic settings. The multi-sectoral review is a synthesis of peer-reviewed literature on AI applications in seven areas, namely research, healthcare, restaurant and food services, personal-care services, education, corporate offices, and households. The review combines the empirical evidence, methodological trends, innovations in the sector, and cross-cutting ethical and governance issues. There has been evidence that AI increases efficiency, predictive accuracy, personalization, and decision support, especially when implemented as an addition to human expertise. In studies, AI-based screening and evidence synthesis can greatly decrease the workload, but it needs human verification to prevent bias and false negatives. In the field of healthcare, AI has expert-level performance on small diagnostic tasks, but mass prospective validation, explainability, and regulation are essential. Forecasting, automation, and customer personalization are beneficial to service industries, but the cost of integration and trust remains. There are slight gains in learning outcomes in educational applications with teacher-led instruction. Corporate settings use AI to automate and analyze data, and this leads to the issue of algorithmic bias and workforce management transparency. Smart systems and assistive technologies are the focus of household adoption, and the key factors are privacy, cost, and ethical implementation. Themes that have been replicated across industries are data governance, transparency, fairness, workforce implications, and the need to have human-in-the-loop structures. The review concludes that the impact of AI on society is massive and growing, but to make the implementation of AI fair and sustainable, responsible, explainable, and context-specific governance models are necessary. The future studies ought to focus on longitudinal assessment, cross-cultural examination, and strict validation to enhance evidence-based AI implementation.