This report looks at the move toward collaboration between humans and AI. It analyzes what organizations need to do to use this technology successfully and how it is changing the way companies manage their staff.

本報告探討人類與 AI 協作的趨勢。它分析了組織若要成功應用此技術需要採取哪些行動，以及這如何改變公司的員工管理方式。

AI is no longer seen as just a tool, but as a digital colleague. Because of this, experts emphasize that businesses must create formal rules to manage these interactions without treating AI like a human. A major concern is that autonomous AI agents can make mistakes very quickly. Consequently, companies need 'anarchy protection'—a set of safety rules similar to traffic laws—to keep the systems under control. Furthermore, for AI to work well, human users must be able to explain their own decision-making process to improve the AI's results.

AI 不再被視為僅僅是一種工具，而是一個數位同事。因此，專家強調企業必須制定正式規則來管理這些互動，而不能將 AI 視為人類。一個主要擔憂是自主 AI 代理可能會迅速犯錯。因此，公司需要「無政府狀態保護」——一套類似交通法規的安全規則——以將系統控制在範圍內。此外，為了讓 AI 運作良好，人類用戶必須能夠解釋自己的決策過程，以改善 AI 的結果。

From an operational view, there is an 'AI velocity gap,' meaning that AI technology is advancing much faster than companies can change their internal structures. While many firms have invested heavily in AI, many report a lack of real business results. This is often caused by poor system design rather than problems with the AI model itself. To succeed, organizations should focus on better data management and follow the '12 Rules of Agentic AI.' These rules assert that trust is built through fairness, preventing AI errors, and using strict legal and safety guardrails.

從營運角度來看，存在一個「AI 速度差距」，意味著 AI 技術的進步速度遠快於公司改變內部結構的速度。雖然許多公司在 AI 方面投入巨資，但許多公司報告缺乏真正的業務成果。這通常是由於系統設計不佳，而非 AI 模型本身的問題。為了取得成功，組織應專注於更好的數據管理，並遵循「代理式 AI 12 條規則」。這些規則主張，信任是透過公平、防止 AI 錯誤以及使用嚴格的法律與安全防護欄而建立的。

Finally, the job market is changing significantly. Some executives believe in a strict approach, replacing employees who lack AI skills with new, tech-savvy talent. Roles in marketing and HR may decrease as AI takes over routine tasks. However, there is a growing demand for technical experts who can manage these systems. In universities, a 'Lift-Lighten-Learn-Lead' method is suggested. This ensures that AI supports students' learning rather than replacing it, keeping human judgment as the final authority.

最後，就業市場正發生顯著變化。一些高管相信應採取強硬做法，用精通技術的新人才取代缺乏 AI 技能的員工。隨著 AI 接管例行任務，行銷與人力資源（HR）的職位可能會減少。然而，對能夠管理這些系統的技術專家的需求日益增加。在大學中，建議採用「Lift-Lighten-Learn-Lead」方法。這能確保 AI 是支持學生的學習而非取代學習，並將人類的判斷保留作為最終權威。

Currently, there is a clear difference between companies that simply use AI and those that build complete systems to create real value.

目前，僅僅使用 AI 的公司與建立完整系統以創造真實價值的公司之間，存在明顯差異。