Companies Use AI to Get Better Results
Companies Use AI to Get Better Results
企業利用 AI 獲得更好的成效
Introduction
Many companies now use AI to help their customers. They want the AI to solve real problems.
許多公司現在使用 AI 來協助客戶。他們希望 AI 能解決實際問題。
Main Body
More companies use AI now. In 2025, 39% used it. In 2026, 66% used it. Many companies use AI in five or more places. Some people still help the AI to keep customers happy. Companies also need new workers to manage the AI data.
現在有更多公司使用 AI。在 2025 年，有 39% 的公司使用；到 2026 年，則增加至 66%。許多公司在五個或更多領域應用 AI。有些人仍會協助 AI 以確保客戶滿意。公司也需要新員工來管理 AI 數據。
AI costs a lot of money. Before, companies used AI for everything. Now, they are careful. They do not want to waste money on small tasks. AI companies must show that their tools really work.
AI 的成本很高。以前公司將 AI 應用在所有方面，但現在他們變得謹慎。他們不想在微小的任務上浪費金錢。AI 公司必須證明其工具確實有效。
Some companies now pay only when the AI solves a problem. For example, Salesforce has a new plan. You pay when the AI fixes the issue. Now, AI solves 40% of problems alone. This makes work faster and customers happier.
部分公司現在採取僅在 AI 解決問題後才付款的模式。例如，Salesforce 推出了新方案，只有在 AI 修復問題時才支付費用。現在 AI 能獨立解決 40% 的問題，這使得工作更快速，客戶也更滿意。
Conclusion
AI is changing. Companies do not just try it now. They want to see a clear profit.
AI 正在改變。公司現在不再僅僅是嘗試，而是希望看到明確的獲利。
Vocabulary Learning
💡 The 'Comparing Now and Then' Pattern
In this text, we see how companies changed their habits. To reach A2, you need to show the difference between the past and the present.
Look at these shifts:
- Before companies used AI for everything.
- Now they are careful.
How to use this in your speaking: Use 'Before' for the old way and 'Now' for the new way. It is the simplest way to tell a story about change.
🛠️ Word Power: Action-Result Pairs
Notice how the text connects an action to a feeling. This is a great way to build longer sentences.
| Action | Result | Sentence |
|---|---|---|
| Fixes the issue | Happy | AI fixes the issue customers are happier. |
| Solve problems | Faster | AI solves problems work is faster. |
Tip: Use words like 'makes' to connect them. (Example: "This makes work faster")
Vocabulary Learning
The Shift in Enterprise AI: Moving from Usage Volume to Business Results
企業 AI 的轉型：從追求使用量轉向業務成果
Introduction
Global service companies are increasingly using agentic AI in their customer operations. Instead of simply adopting the technology, they are now focusing on achieving measurable business results.
全球服務公司在客戶營運中越來越多地採用 agentic AI。他們現在不再僅僅是導入技術，而是專注於達成可衡量的業務成果。
Main Body
The use of agentic AI in customer service is growing rapidly, with adoption expected to rise from 39% in 2025 to 88% by the end of 2026. Most organizations are using AI across five or more different channels; however, 77% still keep humans involved to maintain customer trust. Consequently, companies are changing how they manage their staff. There is a growing demand for experts in data management and AI architecture, and companies are emphasizing the need for employees to improve their strategic problem-solving skills.
在客戶服務中使用 agentic AI 的速度正快速增長，預計採納率將從 2025 年的 39% 上升至 2026 年底的 88%。大多數組織在五個或更多不同的通路中使用 AI；然而，仍有 77% 的組織保留人工參與以維持客戶信任。因此，公司正在改變管理員工的方式。對於數據管理與 AI 架构專家的需求日益增加，且公司強調員工需要提升策略性解決問題的能力。
At the same time, companies are changing how they spend money on AI. In the past, some firms tried to maximize their AI usage, but they are now limiting 'tokens' to control costs. For example, Accenture has introduced internal limits to stop wasting resources on unimportant tasks. Because of these high costs, AI providers must now prove that their tools provide real value to satisfy company executives.
同時，公司正在改變在 AI 上的支出方式。過去，部分公司試圖將 AI 使用量最大化，但現在他們透過限制「token」來控制成本。例如，Accenture 引入了內部限制，以防止在不重要的任務上浪費資源。由於成本高昂，AI 供應商現在必須證明其工具能提供實際價值，才能滿足公司高層的要求。
To address these financial pressures, some companies are moving toward outcome-based pricing. Salesforce, for instance, has introduced a 'pay-per-resolution' model, meaning they only pay when the AI successfully solves a problem. Data shows that 40% of cases are now handled entirely by AI, which could reduce resolution time by 20%. Therefore, success is no longer measured by how much AI is used, but by how much customer satisfaction and employee productivity improve.
為了應對這些財務壓力，部分公司正轉向以成果為基礎的定價模式。例如，Salesforce 引入了「按解決方案付費」的模式，這意味著只有在 AI 成功解決問題時才支付費用。數據顯示，目前 40% 的案例完全由 AI 處理，這可將解決時間縮短 20%。因此，成功的衡量標準不再是 AI 的使用量，而是客戶滿意度與員工生產力的提升程度。
Conclusion
The AI market is moving away from experimental use and toward a professional system focused on measurable returns and efficiency.
AI 市場正從實驗性使用，轉向一個專注於可衡量回報與效率的專業系統。
Vocabulary Learning
🚀 The 'Cause & Effect' Connection
To move from A2 to B2, you must stop using only and or but. B2 speakers connect ideas to show why something happened. Look at these three 'Bridge Words' from the text:
-
Consequently Used to show a direct result.
- Text: "77% keep humans involved... Consequently, companies are changing how they manage staff."
- A2 style: "They keep humans. So, they change staff."
- B2 style: "They keep humans; consequently, their management style is evolving."
-
Therefore Used to reach a logical conclusion.
- Text: "...could reduce resolution time by 20%. Therefore, success is no longer measured by usage."
- Quick Tip: Use this when you are presenting a fact and then a decision based on that fact.
-
Because of Used to introduce a reason (followed by a noun, not a full sentence).
- Text: "Because of these high costs..."
- The Trap: A2 students say "Because these costs are high." B2 students use "Because of [Noun Phrase]" to sound more professional.
💡 Vocabulary Pivot: From 'Simple' to 'Strategic'
Instead of using basic words, try these B2 replacements found in the article:
| A2 Word (Basic) | B2 Word (Advanced) | Context from Text |
|---|---|---|
| Use / Start | Adoption | "...adoption expected to rise..." |
| Goal / Result | Measurable returns | "...focused on measurable returns..." |
| Change / Move | Shift | "The Shift in Enterprise AI..." |
Coach's Note: Notice how "Shift" is stronger than "Change." It implies a movement in direction, not just a random difference. Use this in your next presentation!
Vocabulary Learning
The Transition of Enterprise AI Integration from Volume-Based Consumption to Outcome-Oriented Valuation
企業 AI 整合從量化消費轉型至成果導向估值
Introduction
Global service organizations are increasingly integrating agentic AI into customer operations, shifting their focus from mere adoption to the realization of quantifiable business outcomes.
全球服務組織正日益將 Agentic AI 整合至客戶營運中，將重心從單純的採用轉向實現可量化的業務成果。
Main Body
The proliferation of agentic AI within customer service sectors is evidenced by a rise in adoption from 39% in 2025 to 66% in 2026, with projections suggesting a further increase to 88% by year-end 2026. This integration is characterized by a multi-channel deployment strategy, where 83% of organizations utilize AI across five or more interfaces. To mitigate risks to consumer trust, 77% of these entities maintain human intervention capabilities. The operationalization of this technology has necessitated a reconfiguration of human capital; there is a documented expansion in roles pertaining to data management, AI architecture, and prompt specialization, alongside a systemic emphasis on upskilling staff in complex problem-solving and strategic oversight.
Agentic AI 在客戶服務領域的普及，體現於採用率從 2025 年的 39% 升至 2026 年的 66%，預計到 2026 年底將進一步增加至 88%。這種整合的特點在於多通路部署策略，83% 的組織在五個或更多介面使用 AI。為了降低對消費者信任的風險，77% 的實體維持了人工干預能力。該技術的營運使得人力資本必須重新配置；記錄顯示，數據管理、AI 架構和 Prompt 專業化相關的職位有所擴張，同時系統性地強調提升員工在複雜問題解決與策略監管方面的技能。
Parallel to these deployments, a divergence in fiscal strategies has emerged. While some organizations previously prioritized the maximization of token usage, there is an observable shift toward 'token rationing' as AI expenditures become material to corporate cost structures. This transition is exemplified by reports of internal restrictions at Accenture to prevent the depletion of token reserves on low-value tasks. Such fiscal volatility has contributed to a broader market correction, necessitating that AI providers demonstrate tangible utility to satisfy the requirements of chief financial and operating officers.
與這些部署平行的是，財務策略出現了分歧。雖然部分組織此前優先考慮 Token 使用量的最大化，但隨著 AI 支出對公司成本結構產生實質影響，目前可觀察到向「Token 配額管理」的轉移。Accenture 內部限制 Token 使用以防止低價值任務耗盡儲備的報告便是一個例證。這種財務波動導致了更廣泛的市場修正，要求 AI 供應商證明其實際效用，以滿足首席財務官與營運長的要求。
In response to these economic pressures, a rapprochement between technology deployment and value extraction is being pursued through outcome-based pricing. Salesforce has introduced a 'pay-per-resolution' model for its help agent, ensuring that costs are incurred only upon the autonomous resolution of an issue. This shift is supported by data indicating that 40% of case resolutions are now fully autonomous, contributing to a potential 20% reduction in resolution time. Consequently, the metric for success has transitioned from token consumption to the improvement of customer satisfaction and service representative productivity.
為了回應這些經濟壓力，業界正透過「基於成果的定價」來追求技術部署與價值提取之間的趨同。Salesforce 為其 AI 助手引入了「按解決方案付費」模式，確保僅在問題被自主解決後才產生費用。數據支持了這一轉變，顯示 40% 的案件目前已完全自主解決，有助於潛在減少 20% 的解決時間。因此，成功的衡量指標已從 Token 消耗量轉型為客戶滿意度與服務代表生產力的提升。
Conclusion
The AI landscape is currently evolving from an era of speculative experimentation toward a disciplined framework of measurable ROI and autonomous efficiency.
AI 領域目前正從投機實驗時代，演進至一個注重可衡量投資報酬率 (ROI) 與自主效率的嚴謹框架。
Vocabulary Learning
The Architecture of Nominalization and 'Density' in C2 Prose
To bridge the gap from B2 to C2, one must move beyond describing actions and begin conceptualizing processes. This article is a masterclass in Nominalization—the linguistic process of turning verbs (actions) and adjectives (qualities) into nouns.
⚡ The C2 Pivot: From Action to Concept
Observe the shift in the text. A B2 learner would write: "Organizations are integrating AI more and more, and they want to see real results."
The text instead uses: "The proliferation of agentic AI... is evidenced by a rise in adoption... to the realization of quantifiable business outcomes."
By transforming proliferate proliferation, adopt adoption, and realize realization, the author strips away the "human actor" and replaces it with a conceptual state. This creates a tone of objective authority and academic distance.
🔍 Deconstructing the 'High-Density' Phrase
Consider this segment:
"...a rapprochement between technology deployment and value extraction is being pursued through outcome-based pricing."
Why this is C2 level:
- Lexical Precision: Rapprochement (a restoration of harmonious relations) is used metaphorically here to describe the alignment of two disparate business goals. This is a "high-utility" word used in a nuanced, non-literal context.
- Noun-Heavy Clustering: Technology deployment and value extraction are complex noun phrases acting as single units of meaning. This allows the writer to pack an immense amount of information into a single sentence without needing multiple coordinating conjunctions (and/but/so).
🛠️ The 'C2 Strategy' for your Writing
To elevate your output, stop focusing on who is doing what. Instead, focus on what phenomenon is occurring.
- B2 (Verb-centric): "The company decided to limit tokens because they were spending too much money."
- C2 (Nominal-centric): "The implementation of token rationing was necessitated by the fact that AI expenditures had become material to corporate cost structures."
Key Takeaway: C2 mastery is not about using "big words," but about using nominal structures to create a dense, analytical, and impersonal narrative flow.