The integration of artificial intelligence (AI) into business models is a transformative force that enhances operational efficiency, decision-making, and innovation. This review explores the diverse applications of AI, including machine learning, natural language processing, and predictive analytics, and their impact on various industries such as finance, healthcare, retail, and manufacturing. It emphasizes the importance of robust data governance, the acquisition of AI talent, and fostering a culture of innovation and adaptability. The paper also addresses the challenges and risks associated with AI adoption, including ethical considerations, data privacy, bias mitigation, and cybersecurity vulnerabilities. The implementation of AI in business models is driven by objectives aimed at enhancing organizational performance, competitiveness, and adaptability. Key strategies include assessing business needs and goals, identifying AI opportunities, building a data infrastructure, selecting appropriate AI technologies, implementing AI solutions, and ensuring employee training and change management. Continuous monitoring and improvement are essential to ensure AI solutions remain effective and aligned with organizational goals. Collaboration and partnerships with AI service providers and industry peers are crucial for leveraging external expertise and staying abreast of best practices. The future of AI in business models is shaped by trends such as the demand for transparency in AI decision-making, the integration of AI to address environmental and social challenges, the deployment of AI models on edge devices, and the emphasis on ethical considerations. Organizations must foster a culture of continuous innovation and adaptability, embrace an agile mindset, and actively engage in collaborations and knowledge-sharing to stay competitive in the evolving landscape of AI-driven business transformation.The integration of artificial intelligence (AI) into business models is a transformative force that enhances operational efficiency, decision-making, and innovation. This review explores the diverse applications of AI, including machine learning, natural language processing, and predictive analytics, and their impact on various industries such as finance, healthcare, retail, and manufacturing. It emphasizes the importance of robust data governance, the acquisition of AI talent, and fostering a culture of innovation and adaptability. The paper also addresses the challenges and risks associated with AI adoption, including ethical considerations, data privacy, bias mitigation, and cybersecurity vulnerabilities. The implementation of AI in business models is driven by objectives aimed at enhancing organizational performance, competitiveness, and adaptability. Key strategies include assessing business needs and goals, identifying AI opportunities, building a data infrastructure, selecting appropriate AI technologies, implementing AI solutions, and ensuring employee training and change management. Continuous monitoring and improvement are essential to ensure AI solutions remain effective and aligned with organizational goals. Collaboration and partnerships with AI service providers and industry peers are crucial for leveraging external expertise and staying abreast of best practices. The future of AI in business models is shaped by trends such as the demand for transparency in AI decision-making, the integration of AI to address environmental and social challenges, the deployment of AI models on edge devices, and the emphasis on ethical considerations. Organizations must foster a culture of continuous innovation and adaptability, embrace an agile mindset, and actively engage in collaborations and knowledge-sharing to stay competitive in the evolving landscape of AI-driven business transformation.