20世紀初，管理顧問泰勒透過觀察與記錄，試圖將工廠工人的傳統經驗與隱性知識系統化。如今，企業在將人工智慧導入白領階級的工作時，也面臨著類似的挑戰，因為許多對AI模型至關重要的領域知識與判斷力，仍鎖在員工的腦海中。

由於許多複雜任務難以被明確定義與編碼，通用型AI模型在處理需要專業判斷的工作時表現不佳，甚至透過改良提示詞也無法解決問題。因此，許多企業開始尋求員工的協助，利用企業內部的專業知識與數據來微調專屬的AI模型，從而大幅提升模型的準確度與實用性。

然而，這種要求員工協助訓練AI的過程引發了信任與利益的考驗。員工可能會擔心交出專業知識後被AI取代，或失去工作上的自主權；因此，在接下來的AI發展階段中，那些能建立互信文化、並讓員工相信能從AI發展中獲益的企業，才更有機會在競爭中脫穎而出。

In the early 20th century, management consultant Frederick Winslow Taylor attempted to systematize the traditional and tacit knowledge of factory workers through observation and recording. Today, companies face a similar challenge when introducing artificial intelligence into white-collar workplaces, as much of the domain knowledge and judgment crucial for AI models remains locked inside employees' heads.

Because many complex tasks are difficult to explicitly define and codify, general-purpose AI models often underperform in jobs requiring professional judgment, a problem that even improved prompting cannot solve. Consequently, many businesses are enlisting their employees' help to fine-tune bespoke AI models using internal expertise and data, thereby significantly improving the accuracy and utility of the models.

However, this process of asking employees to help train AI tests workplace trust and interests. Workers might fear being replaced by AI after sharing their expertise, or losing their autonomy at work; therefore, in the next phase of AI development, companies that foster a culture of mutual trust and convince employees they will share in AI-driven gains will be more likely to succeed in the competition.