Implementing AI solutions offers many benefits, but without proper Risk Management for AI, these technologies can pose challenges to organizations across industries. Effective AI risk management is crucial to deploying safe, reliable, and compliant AI systems that drive innovation and maintain accountability. Industry standards and frameworks can help manage AI risks, including the NIST AI Risk Management Framework, which helps identify, assess, and mitigate risks associated with AI systems. The EU AI Act introduces a risk-based approach to regulating AI systems, categorizing applications based on their risk level. ISO and IEC have developed several standards for AI risk management, focusing on emerging areas such as AI governance, ethical considerations, and sustainability. Leading technology companies have established guidelines to ensure responsible AI development, which organizations can learn from. As AI technologies evolve, challenges such as integrating critical infrastructure, managing autonomous systems, and addressing security threats will shape the risk landscape in 2024 and 2025. Organizations must implement proactive measures to mitigate these risks, including training programs, data governance policies, and explainable AI techniques. By integrating strong risk management practices, encouraging collaboration across teams, and investing in skill development, organizations can confidently navigate the complexities of AI deployment.