Agent 都这么厉害了，「AI 员工」为什么今天还没有真正出现？

Core Viewpoint - The article discusses the challenges and limitations of implementing AI digital employees in the workplace, questioning whether the pursuit of such technology is truly worthwhile [2][20]. Group 1: Historical Context and Current Limitations - The concept of "digital employees" originated from the RPA (Robotic Process Automation) era, where the goal was to automate processes to mimic human tasks [3]. - Early automation tools, such as chatbots and intelligent calling systems, are often misrepresented as "AI employees," but they lack true autonomy and are merely automation tools [4]. - High maintenance costs associated with AI systems, including constant updates and process configurations, can make managing them more cumbersome than managing human employees [5]. Group 2: Challenges with Large Models - The evolution of AI has introduced new possibilities, yet significant issues remain that prevent AI from functioning as true employees [6]. - AI's reasoning speed is slower than that of humans, which can disrupt user experience in high-paced environments like sales [8]. - Most AI applications still rely on pre-defined scenarios and workflows, making it difficult for them to handle edge cases that humans can easily navigate [10]. Group 3: Limitations in Understanding and Adaptability - AI struggles with clarifying user intent, as real users often express themselves imprecisely, requiring a more nuanced understanding [13]. - The knowledge update process for AI is often slow and inconsistent, as models lack memory and rely on human input for updates, leading to outdated information [18]. - AI systems currently lack the ability to assess the implications of their decisions, which is crucial for building trust in their capabilities [19]. Group 4: Future Directions for AI Employees - The demand for AI employees is high, but the pursuit of complete human-like replacements may overlook the complexities and costs involved [20]. - A more feasible approach is to focus on partial replacements, identifying specific tasks where AI can effectively collaborate with humans [20]. - The recommendation is to allow AI to function in a "trainee" capacity within real scenarios, enabling iterative improvements and assessments [23].