熱門時事分享：「AI 客服」為何讓人火大？人味才是新奢侈｜20260428
目錄
2026 年 04 月 28 日｜HOT 用英文聊時事｜S1 EP24
歡迎收聽《HOT 用英文聊時事》，你有沒有過這種經驗：對著 AI 客服鬼打牆，最後只想大喊「叫真人出來」？這真的不是你的錯。根據最新數據，有高達五分之一的用戶都認為，現在的 AI 客服根本是在幫倒忙。為什麼 AI 這麼雷？其實，往往不是它不夠聰明，而是它「讀錯書」了。今天這集，我們不只要談矽谷最新的技術，更要先揪出那個讓 AI 頻頻失靈的幕後黑手——也就是「資料品質」。（參考來源：Qualtrics）
Welcome to “Hot English Topics.” Have you ever had this experience: going in circles with an AI customer service agent, until you just want to yell, “Give me a real person”? This is really not your fault. According to the latest data, up to one-fifth of users think that current AI customer service is actually “causing more trouble.” Why is AI so unreliable? Actually, it is often not because it isn’t smart enough, but because it “reads the wrong books.” In today’s episode, we’re not only talking about the latest tech from Silicon Valley—we’re also uncovering the real reason AI keeps failing: data quality.
這類 AI 客服表現不佳的核心原因在於「資料品質」。成功的 AI 客服高度依賴系統整合能力，但現實中，許多企業的客服紀錄、知識庫與產品手冊往往分散在不同部門或外包廠商手中，導致資料格式分歧且不夠結構化。即使目前許多企業導入了新的 RAG 技術，簡單說就是強制 AI 先「翻書找答案」再開口，讓 AI 先從內部知識庫抓取正確資訊再回答，以減少「AI 幻覺」或亂編答案的風險，但如果企業內部的基礎資料品質欠佳，AI 依然無法給出精準的回覆。（參考來源：經濟日報）
Indeed, the core reason for this poor performance lies squarely in “data quality.” Successful AI customer service relies heavily on system integration; however, in reality, many companies’ records, knowledge bases, and product manuals are scattered across different departments or outsourced vendor s, leading to inconsistent and unstructured data formats. Even though many companies have introduced new RAG technology—which simply means forcing the AI to “look up the answer in a book” before speaking, allowing it to grab correct information from an internal database to reduce the risk of “AI hallucinations” or making things up—if the company’s basic data quality is poor, the AI still cannot give accurate responses.
除了技術層面的資料瓶頸，企業內部也面臨著深層的信任挑戰。第一線客服人員雖然期待 AI 能減輕工作量，但初期往往需要投入更多時間進行校正與訓練。澳洲聯邦銀行就發生過一個極具爭議的案例，當時，銀行指派了一位資深行員協助訓練內部的 AI 客服系統，原本是為了優化服務，但專案結束後，該名員工卻隨即遭到裁員。這種做法立刻在內部引發了強烈的焦慮，導致其他員工對 AI 專案採取消極態度，不願貢獻核心經驗。最終，這套系統上線後，因為缺乏人類專家的細膩引導，無法處理複雜的客戶情緒，反而導致客訴電話量不減反增。最終，銀行不得不公開道歉並撤回裁員決定。（參考來源：經濟日報、今周刊）
Beyond technical data bottlenecks, companies also face deep challenges regarding trust. Although frontline customer service staff hope AI can reduce their workload, they often need to spend more time correcting and training it initially. A highly controversial case at the Commonwealth Bank of Australia illustrates this perfectly. The bank assigned a senior employee to help train their internal AI system to improve service. However, the moment the project ended, that employee was laid off. This action triggered immediate anxiety internally, causing other employees to withhold their core expertise. Ultimately, because the system lacked the detailed guidance of human experts, it failed to handle complex emotions, and complaint calls actually increased. The bank eventually had to publicly apologize and reverse the layoff decision.
對於消費者而言，AI 客服的「人情味缺失」是一大痛點。研究指出，高達一半的消費者只要遭遇一次糟糕的 AI 體驗，就會減少在該品牌的消費。雖然 AI 能處理簡單的重複性問題，但在面對情緒安撫或複雜判斷時，人類的溫度依然不可替代。台灣的金融與電商產業也面臨類似狀況，如果 AI 只是被視為「減少人力預算」的節流工具，而非「提升服務體驗」的引擎，企業將很難留住注重隱私與掌控權的現代消費者。（參考來源：Qualtrics、今周刊）
For consumers, the “lack of human touch” in AI customer service is another major pain point. Research points out that up to half of consumers will spend less on a brand after just one bad AI experience. Although AI can handle simple, repetitive problems, the warmth of a human is still irreplaceable when it comes to comforting emotions or making complex judgments. Taiwan’s financial and e-commerce industries face a similar situation; if AI is only seen as a cost-cutting tool to “reduce labor budgets” rather than an engine to “improve service experience,” companies will find it hard to keep modern consumers who value privacy and control.
面對這種兩難，國際標竿企業如美國威訊無線與友邦保險，已經示範了最佳解方——那就是「人機協作」。他們不讓 AI 完全取代人類，而是讓 AI 擔任「副駕駛」的角色，負責自動生成摘要、即時檢索數據並提供建議回覆，再由人工客服進行最終的決策與情緒處理。這種方式能有效縮短通話時間，並顯著提升應答效率。（參考來源：經濟日報、今周刊）
Facing this dilemma, international benchmark companies like Verizon and AIA have already demonstrated the best solution—which is “human-machine collaboration.” They do not let AI completely replace humans, but instead let AI act as a “co-pilot,” responsible for automatically generating summaries, searching for data in real-time, and suggesting replies, while live customer service handles the final decisions and emotions. This method can effectively shorten call times and significantly improve response efficiency.
長遠來看，2026 年的客服轉型關鍵在於平衡科技的速度與人類的溫度。正如《今周刊》提到的，傳統客服將升格為「數位旅程設計師」；而《ETtoday 新聞雲》也指出，企業正積極招募所謂的「AI 戰略師」，專注於經營客戶關係與判斷倫理風險。企業必須意識到，AI 應該是推動轉型的引擎，而非單純的替代品。唯有建立透明的資料治理機制，並讓機器負責效率、人類守護溫暖與意義，才有機會在 AI 時代真正贏得消費者的長期忠誠。（參考來源：今周刊、ETtoday新聞雲）
In the long run, the key to customer service transformation in 2026 lies in balancing the speed of technology with the warmth of humans. As mentioned by Business Today, traditional customer service will be upgraded to “Digital Journey Designers”; meanwhile, ETtoday also points out that companies are actively recruiting so-called “AI Strategists.” These roles focus on managing customer relationships and judging ethical risks. Companies must realize that AI should be an engine driving transformation, not just a simple replacement. To win long-term customer loyalty in the AI era, we need transparent data governance. Let machines do the fast work—and let humans protect warmth and meaning.
本集節目由 CLN 製作播出，若你喜歡這種主題與雙語內容，歡迎追蹤我們、給我們五顆星，並分享給對 AI 趨勢、數位轉型或學英文有興趣的朋友。也告訴我們下次想聽的主題吧！我們下次見！
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本節目由【CLN (Corporate Language Network)｜外語服務與培訓領導品牌】製作播出。
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CLN (Corporate Language Network) 創辦於 2014 年，是亞洲企業外語服務和培訓的領導品牌，旨在解決企業因外語所衍伸的相關問題，協助客戶成為具有跨文化溝通和國際合作能力的專業人士。我們提供一流的企業教育訓練、AI 學習工具、隨選隨上家教平台、文件翻譯、會議口譯、師資訓練等專業服務。這些年來，我們的合作廠商包含 Google、Yahoo、IBM、IKEA、Mercedes-Benz、台積電、聯發科等多家國際品牌。
Since 2014, CLN (Corporate Language Network) has delivered language training and cross-cultural communication services for companies across Asia, including brands such as Google, IKEA, TSMC and MediaTek.