Core Insights - The rapid development of AI large models has revolutionized capabilities, yet over 95% of enterprises fail in pilot applications of AI, indicating significant challenges in leveraging AI effectively [2][3] - The article focuses on the micro risks associated with deploying AI large models in enterprises, including issues like poor business outcomes, customer experience degradation, brand reputation damage, data security threats, intellectual property erosion, and legal compliance problems [3][5] Micro Risks of AI - The phenomenon of "hallucination" in large models leads to the generation of content that appears logical but is actually incorrect or fabricated, posing a significant challenge in high-precision operational scenarios [5][6] - Output safety and value alignment challenges arise from the model's training data, which may include biases and harmful information, potentially damaging brand reputation and public trust [5][6] - Privacy and data compliance risks are present when sensitive information is input into third-party AI services, which may lead to unintentional data leaks [6][11] - The lack of explainability in decision-making processes of large models creates challenges in high-risk sectors, as the "black box" nature of these models makes it difficult to audit and trust their outputs [6][12] Strategies to Mitigate Risks - Companies can enhance model performance through technical improvements, such as reducing hallucination rates and ensuring better value alignment [7][8] - Enterprises should implement governance measures at the application level, utilizing tools like prompt engineering, retrieval-augmented generation (RAG), content filters, and explainable AI (XAI) to manage risks effectively [7][9] - Training and operational protocols for AI should mirror those for human employees, including setting clear guidelines and conducting regular audits to minimize errors [9][10] Accountability in AI Deployment - Responsibility for errors made by AI models ultimately lies with human operators, necessitating clear accountability frameworks within organizations [15] - Companies must adapt their organizational processes to leverage the strengths of both AI and human employees, ensuring a collaborative approach to maximize efficiency and minimize risks [15][16]