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市場調查報告書
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2044047

面向通訊領域的生成式人工智慧應用：市場佔有率分析、產業趨勢與統計數據、成長預測（2026-2031 年）

Telecom Generative AI Applications - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 - 2031)
出版日期: | 出版商: Mordor Intelligence | 英文 151 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2025 年，用於通訊的生成式人工智慧應用市場價值 6.3 億美元，預計到 2031 年將達到 35.3 億美元，而 2026 年為 8.4 億美元，預測期（2026-2031 年）的複合年成長率為 33.38%。

電信生成式人工智慧應用市場-IMG1

生成式人工智慧正從小規模聊天機器人發展成為生產級平台，實現網路編配、詐欺偵測和預測性維護的自動化，取代曾經主導營運的手動工作流程。北美通訊業者正主導成本最佳化工作，例如，AT&T 在實施多模型路由架構後，推理成本降低了 90%，該架構將查詢分配給滿足準確率閾值的最便宜模型。基礎設施供應商現在正將人工智慧作為原生層進行整合。愛立信和Google雲端的「5G 核心即服務」產品清晰地展現了這一趨勢，它們將即時策略調整功能整合到核心軟體中，而不是作為附加模組出售。設備製造商和超大規模資料中心業者正在競相爭取早期採用者的契約，因此，通訊領域生成式人工智慧應用的市場正從實驗階段邁向全面資本投資階段。

人工智慧生成應用在通訊領域的全球趨勢與洞察。

利用世代人工智慧實現網路自動化

通訊業者正在對多智慧體系統進行現場測試，這些系統能夠在幾秒鐘內自動完成無線電參數調整、流量重路由和網路切片配置，取代了以往需要數小時的手動操作。德國電信的概念驗證(PoC) 將 5 萬個基地台的手動配置工作量減少了 40%，使工程師能夠專注於策略規劃。在 2025 年世界行動通訊大會上，諾基亞和 AWS 重現了一個現場演示，其中虛擬助理以自然語言協商服務品質 (QoS) 目標，證明無需專門的配置入口網站。成熟市場的人手不足進一步提高了投資報酬率，因為人工智慧推理的成本低於射頻工程師的總人事費用。然而，真正的即時控制需要高密度邊緣運算節點。缺乏這一層的通訊業者將遭受延遲損失，並被迫在部署人工智慧的同時啟動網路現代化計畫。

高度個人化的客戶體驗解決方案

第一代聊天機器人只能提供靜態的常見問題解答，而生成式人工智慧現在能夠根據每位用戶的設備、位置和歷史記錄來最佳化優惠和故障排除步驟，在試點項目中實現了 20-30% 的轉換率提升。 Verizon 將Google的 Gemini 模型整合到其支援系統中，平均處理時間縮短了 18%，這一具體指標受到了銷售團隊財務部門的重視。 Salesforce 透過簡訊推送人工智慧產生的推薦訊息，追加提升銷售率提高了 25%，這表明交付管道和模型輸出都必須不斷發展。預付市場見效最快，因為通訊業者可以在幾分鐘內更新優惠信息，但歐洲和加州的隱私法要求對行為分析必須獲得明確同意，這延長了高價值地區的普及藍圖。

傳統核心網路中較高的推理成本

許多通訊業者仍依賴傳統的封包核心網路硬體，這些硬體並非為生成模型等運算密集型工作負載而設計。當這些老舊的交換器和EPC平台嘗試進行即時推理時，晶片瓶頸會導致公共端點的每次查詢查詢飆升至0.002美元。這相當於使用針對人工智慧最佳化的現代核心網路成本的20倍。 ARPU值較低的地區的通訊業者感受到的壓力最大，因為即使是小規模的人工智慧部署也會進一步壓縮他們本已微薄的營運利潤。因此，非洲、拉丁美洲和東南亞部分地區的經營團隊正在擱置客戶用例，轉而將有限的資源分配給詐欺偵測和其他投資回報更明確的後勤部門營運。

細分市場分析

到了2025年，軟體在通訊領域生成式人工智慧應用的市場佔有率中佔48.72%。這主要得益於通訊業者可取得的、針對基礎設施模型最佳化的API。同時，隨著推理最佳化晶片的每瓦效能提升十倍，硬體出貨量有所放緩，通訊業者得以減少每個資料中心的加速器安裝數量。另一方面，業務收益的複合年成長率達到了35.40%，反映出通訊業者傾向於將微調和合規性等管理任務外包。隨著託管服務供應商（MSP）引入基於結果的定價模式，通訊領域生成式人工智慧應用的服務規模預計將從2026年的2.6億美元成長到2031年的13.8億美元。

現今，服務業的競爭差異化主要集中在管治。 Amdocs 和 IBM 提供整合控制平台，可處理版本控制、提示日誌記錄和合規性稽核追蹤。 NVIDIA 等硬體供應商正與諾基亞合作，透過將加速器預先整合到基地台中來模糊硬體和軟體之間的界限。這使得通訊業者能夠協商捆綁協議而非單獨的許可證，從而縮短採購週期並增強供應商的議價能力。

到2025年，客戶服務自動化將維持27.81%的市場佔有率，聊天機器人將負責處理第一線諮詢。然而，預測性維護預計將實現最快的成長。面向通訊領域的生成式人工智慧應用市場，特別是專注於預測性維護的市場，預計將以37.01%的複合年成長率成長，人工智慧代理透過提前72小時預防故障來贏得市場佔有率。諾基亞在15個網路中的部署，減少了現場服務次數，並將平均維修時間縮短至2小時，為每個通訊業者節省了5000萬美元的成本。

隨著攻擊者產生合成語音和偽造流量，詐欺偵測和安全相關的工作負載也在同步增加。 Pindrop 的平台已幫助一家北美通訊業者將帳戶盜用率降低了 40%。網路最佳化正在利用生成模型對擁塞情況下的數位雙胞胎模型進行壓力測試，而行銷個人化雖然目前仍處於起步階段，但在預付市場中，其預算正在不斷成長，該市場年解約率超過 30%。各種應用場景的融合有利於整合平台的發展，這些平台能夠整合遙測資料並重新訓練共用的嵌入向量，從而降低冗餘的運算成本。

區域分析

2025年，北美市場將維持35.88%的市佔率。這主要得益於FCC可解釋性法規對易於審計平台的需求增加，以及由於位置超大規模資料中心業者而加快的整合速度。 AT&T大幅降低推理成本，顯示該地區專注於最佳化營運支出（OPEX），而加拿大的資訊揭露要求減緩了前台人工智慧的普及，從而增強了客戶信心。墨西哥的指導方針草案透過將合規負擔轉移到能夠承擔法律成本的大型公司，鞏固了其市場佔有率。

亞太地區預計將以36.72%的複合年成長率成為全球成長最快的地區，這主要得益於中國移動的100億通通話詳細記錄（CDR）模型和Reliance Jio的AI應用「MyJio」（每天處理5000萬查詢）。日本NTT Docomo正在提供互動式網路切片服務，而SK Telecom的客戶流失預測模型已將解約率降低了1.2個百分點。澳洲受嚴格的責任法約束，限制了人工智慧在後勤部門運作中的應用。

在歐洲，由於歐盟人工智慧法案將人工智慧列為「高風險」領域，成長速度有所放緩，但Telia符合GDPR標準的切片配置器表明，合規是一條切實可行的道路。德國電信透過減少40%的人工工作量，證明了生產力提升與監管可以並存。在中東，人們大力投資原生人工智慧5G，以推動智慧城市建設。 du的雙語聊天機器人是區域在地化的一個很好的例子。在拉丁美洲，人工智慧的部署主要集中在巴西的詐欺偵測專案上，但在阿根廷，由於宏觀經濟波動，部署進度落後。在非洲，雲端環境尚不完善，但南非和奈及利亞正在試行邊緣人工智慧進行在地化最佳化，凸顯了其潛力。

其他好處：

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章：引言

  • 研究假設和市場定義
  • 調查範圍

第2章：調查方法

第3章執行摘要

第4章 市場狀況

  • 市場概覽
  • 市場促進因素
    • 利用世代人工智慧實現網路自動化
    • 高度個人化的客戶體驗解決方案
    • AI原生5G獨立組網部署的快速成長
    • 透過最佳化大規模語言模型降低成本
    • 促進開放式無線存取網路（ORAN）和ORAN相容人工智慧工具包的生態系統發展
    • 面向通訊業者的平台模式與垂直整合API
  • 市場限制因素
    • 幻覺導致違規風險
    • 缺乏具有通訊級註釋的資料資產
    • 傳統核心網路中較高的推理成本
    • 區域標準和規範日益碎片化。
  • 產業價值鏈分析
  • 監理情勢
  • 技術展望
  • 宏觀經濟因素的影響
  • 波特五力分析
    • 新進入者的威脅
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 競爭公司之間的競爭

第5章 市場規模與成長預測

  • 按組件
    • 硬體
    • 軟體
    • 服務
  • 透過使用
    • 客戶服務自動化
    • 網路最佳化
    • 詐欺偵測與安全
    • 預測性保護
    • 行銷個人化
  • 按部署模式
    • 雲
    • 現場
    • 邊緣
  • 按通訊業者類型分類
    • 行動網路營運商
    • 固定線路通訊業者
    • 網際服務供應商
    • 虛擬行動服務業者
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 南美洲
      • 巴西
      • 阿根廷
      • 南美洲其他地區
    • 歐洲
      • 德國
      • 英國
      • 法國
      • 俄羅斯
      • 其他歐洲地區
    • 亞太地區
      • 中國
      • 日本
      • 印度
      • 韓國
      • 亞太其他地區
    • 中東和非洲
      • 中東
        • 沙烏地阿拉伯
        • 阿拉伯聯合大公國
        • 其他中東國家
      • 非洲
        • 南非
        • 奈及利亞
        • 其他非洲地區

第6章 競爭情勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • OpenAI LP
    • Cohere Technologies Inc.
    • Anthropic PBC
    • NVIDIA Corporation
    • Google LLC
    • Microsoft Corporation
    • Amazon Web Services Inc.
    • IBM Corporation
    • Huawei Technologies Co. Ltd.
    • Ericsson AB
    • Nokia Corporation
    • Amdocs Limited
    • Juniper Networks Inc.
    • Ciena Corporation
    • Rakuten Symphony Inc.
    • Salesforce Inc.
    • ServiceNow Inc.
    • Alteryx Inc.
    • Telia Company AB
    • AT&T Inc.

第7章 市場機會與未來展望

簡介目錄
Product Code: 97834

The Telecom Generative AI Applications Market size was valued at USD 0.63 billion in 2025 and is estimated to grow from USD 0.84 billion in 2026 to reach USD 3.53 billion by 2031, at a CAGR of 33.38% during the forecast period (2026-2031).

Telecom Generative AI Applications - Market - IMG1

Generative AI is shifting from small-scale chatbots to production-grade platforms that automate network orchestration, fraud detection, and predictive maintenance, replacing manual workflows that once dominated operations. North American operators are leading cost-optimization efforts, exemplified by AT&T's 90% drop in inference costs after deploying a multi-model routing fabric that allocates queries to the cheapest model that meets accuracy thresholds. Infrastructure vendors now embed AI as a native layer. Ericsson and Google Cloud's 5G core-as-a-service offering speaks directly to this trend, bundling real-time policy tuning into the core software rather than selling it as an extra module. Equipment makers and hyperscalers are racing to lock in early adopter contracts, so the Telecom generative AI applications market is moving from experimentation to mainstream capital budgeting.

Global Telecom Generative AI Applications Market Trends and Insights

Generative-AI Powered Network Automation

Operators are field-testing multi-agent systems that auto-adjust radio parameters, reroute traffic, and provision network slices in seconds, replacing hours-long manual tasks. Deutsche Telekom's proof-of-concept reduced human configuration work by 40% across 50,000 cell sites, freeing engineers for strategic planning. Nokia and AWS reenacted a live demo at Mobile World Congress 2025 in which virtual assistants negotiated quality-of-service targets in natural language, removing the need for dedicated provisioning portals. Labor shortages in mature markets magnify ROI because AI inference costs undercut the fully loaded cost of radio-frequency engineers. The caveat is that true real-time control depends on dense edge-compute nodes; operators missing that layer will incur latency penalties, pushing them to launch network modernization programs in parallel with AI rollouts.

Hyper-Personalized Customer Experience Solutions

First-generation chatbots delivered static FAQ responses, but generative AI now tailors offers and troubleshooting steps to each subscriber's device, location, and history, yielding 20-30% higher conversion in pilot runs. Verizon blended Google's Gemini model into its support stack and shortened average handle time by 18%, a hard metric that finance teams recognize. Salesforce observed a 25% upsell lift when AI-curated recommendations were pushed via SMS, underlining that delivery channel and model output must co-evolve. Prepaid markets reap the fastest gains because operators iterate offers within minutes, but privacy statutes in Europe and California require explicit consent for behavioral analytics, stretching deployment roadmaps in high-value regions.

High Inference Cost on Legacy Core Networks

Many operators still lean on legacy packet-core hardware that was never designed for the compute-intensive workloads of generative models. When those aging switches and EPC platforms attempt real-time inference, the silicon bottlenecks push per-query charges up to USD 0.002 on public endpoints, 20 times the rate achieved on modern, AI-optimized cores. Carriers in low-ARPU regions feel the squeeze most acutely because even modest AI adoption can swamp thin operating margins. As a result, boards in parts of Africa, Latin America, and Southeast Asia are shelving customer-facing use cases and instead reserving scarce capacity for fraud detection and other back-office tasks that deliver a clearer return on spend.

Other drivers and restraints analyzed in the detailed report include:

  1. Surge in AI-Native 5G Stand-Alone Deployments
  2. Cost Deflation via Large Language Model Optimization
  3. Evolving Standards Fragmentation Across Regions

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software retained 48.72% of the Telecom generative AI applications market share in 2025, owing to telco-tuned foundation models delivered as consumable APIs. Hardware shipments decelerated as inference-optimized chips deliver 10X the performance per watt, enabling carriers to install fewer accelerators per data center. Conversely, services revenue is clocking a 35.40% CAGR, reflecting operator preference for managed fine-tuning and compliance outsourcing. The Telecom generative AI applications market size for services is projected to climb from USD 0.26 billion in 2026 to USD 1.38 billion by 2031 as MSPs introduce outcome-based pricing.

Competitive differentiation in services now pivots on governance. Amdocs and IBM position unified control planes that handle version tracking, prompt logging, and regulator-ready audit trails. Hardware vendors such as NVIDIA partner with Nokia to pre-integrate accelerators into base stations, collapsing the boundaries between boxes and code. Operators thus negotiate bundles instead of line-item licenses, compressing procurement cycles and magnifying vendor bargaining power.

Customer service automation maintained a 27.81% share in 2025, as chatbots deflected tier-1 queries. Yet predictive maintenance will command the fastest growth, as the Telecom generative AI applications market devoted to predictive maintenance is forecast to expand at a 37.01% CAGR, taking share as AI agents pre-empt failures 72 hours before they occur. Nokia's roll-out across 15 networks saved USD 50 million per carrier by cutting truck rolls and slashing mean time to repair to 2 hours.

Fraud detection and security workloads rise in tandem as adversaries generate synthetic voices and spoofed traffic; Pindrop's platform reduced account takeovers by 40% at North American telcos. Network optimization uses generative models to stress-test digital twins under congestion, while marketing personalization remains a smaller slice but earns budget in prepaid battlegrounds where churn tops 30% annually. Convergence across use cases favors unified platforms that pool telemetry and retrain shared embeddings, reducing redundant compute spend.

The Telecom Generative AI Applications Market Report is Segmented by Component (Hardware, Software, and More), Application (Customer Service Automation, Network Optimization, and More), Deployment Model (Cloud, On-Premise, and Edge), Telecom Operator Type (Mobile Network Operators, Fixed-Line Operators, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

North America retained 35.88% share in 2025 as FCC explainability rules sharpened demand for audit-friendly platforms and proximity to hyperscaler regions compressed integration timelines. AT&T's plunge in inference costs illustrates the region's focus on opex efficiency, while Canada's disclosure mandates slowed front-office AI but nurtured customer trust. Mexico's draft guidelines tilt the compliance burden toward larger players able to absorb legal costs, consolidating share.

Asia-Pacific will register a 36.72% CAGR, the highest worldwide, propelled by China Mobile's 10 billion Call Detail Record model and Reliance Jio's AI-enabled MyJio app handling 50 million daily queries. Japan's NTT DoCoMo offers conversational network slicing; SK Telecom's churn predictor reduced attrition by 1.2 points. Australia, burdened by strict liability laws, confines AI to back-office scenarios.

Europe is growing more slowly due to the EU AI Act's high-risk label, but Telia's GDPR-compliant slice configurator shows that compliance paths are viable. Deutsche Telekom's 40% cut in manual tasks shows that productivity gains can coexist with regulation. The Middle East invests aggressively in AI-native 5G to power smart-city agendas; du's bilingual chatbot exemplifies regional localization. Latin America's uptake centers on Brazilian fraud-detection projects, whereas Argentina delays due to macroeconomic volatility. Africa faces cloud scarcity, but South Africa and Nigeria test edge AI for rural optimization, highlighting latent potential.

  1. OpenAI LP
  2. Cohere Technologies Inc.
  3. Anthropic PBC
  4. NVIDIA Corporation
  5. Google LLC
  6. Microsoft Corporation
  7. Amazon Web Services Inc.
  8. IBM Corporation
  9. Huawei Technologies Co. Ltd.
  10. Ericsson AB
  11. Nokia Corporation
  12. Amdocs Limited
  13. Juniper Networks Inc.
  14. Ciena Corporation
  15. Rakuten Symphony Inc.
  16. Salesforce Inc.
  17. ServiceNow Inc.
  18. Alteryx Inc.
  19. Telia Company AB
  20. AT&T Inc.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Generative-AI Powered Network Automation
    • 4.2.2 Hyper-Personalized Customer Experience Solutions
    • 4.2.3 Surge in AI-Native 5G Stand-Alone Deployments
    • 4.2.4 Cost Deflation via Large Language Model Optimization
    • 4.2.5 Ecosystem Push for Open RAN and ORAN-Aligned AI Toolkits
    • 4.2.6 Telco-specific Foundation Models and Verticalized APIs
  • 4.3 Market Restraints
    • 4.3.1 Hallucination-Driven Regulatory Non-Compliance Risk
    • 4.3.2 Scarcity of Telecom-Grade Annotated Data Assets
    • 4.3.3 High Inference Cost on Legacy Core Networks
    • 4.3.4 Evolving Standards Fragmentation Across Regions
  • 4.4 Industry Value Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Impact of Macroeconomic Factors
  • 4.8 Porter's Five Forces Analysis
    • 4.8.1 Threat of New Entrants
    • 4.8.2 Bargaining Power of Suppliers
    • 4.8.3 Bargaining Power of Buyers
    • 4.8.4 Threat of Substitute Products
    • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Component
    • 5.1.1 Hardware
    • 5.1.2 Software
    • 5.1.3 Services
  • 5.2 By Application
    • 5.2.1 Customer Service Automation
    • 5.2.2 Network Optimization
    • 5.2.3 Fraud Detection and Security
    • 5.2.4 Predictive Maintenance
    • 5.2.5 Marketing Personalization
  • 5.3 By Deployment Model
    • 5.3.1 Cloud
    • 5.3.2 On-Premise
    • 5.3.3 Edge
  • 5.4 By Telecom Operator Type
    • 5.4.1 Mobile Network Operators
    • 5.4.2 Fixed-Line Operators
    • 5.4.3 Internet Service Providers
    • 5.4.4 Mobile Virtual Network Operators
  • 5.5 By Geography
    • 5.5.1 North America
      • 5.5.1.1 United States
      • 5.5.1.2 Canada
      • 5.5.1.3 Mexico
    • 5.5.2 South America
      • 5.5.2.1 Brazil
      • 5.5.2.2 Argentina
      • 5.5.2.3 Rest of South America
    • 5.5.3 Europe
      • 5.5.3.1 Germany
      • 5.5.3.2 United Kingdom
      • 5.5.3.3 France
      • 5.5.3.4 Russia
      • 5.5.3.5 Rest of Europe
    • 5.5.4 Asia-Pacific
      • 5.5.4.1 China
      • 5.5.4.2 Japan
      • 5.5.4.3 India
      • 5.5.4.4 South Korea
      • 5.5.4.5 Rest of Asia-Pacific
    • 5.5.5 Middle East and Africa
      • 5.5.5.1 Middle East
        • 5.5.5.1.1 Saudi Arabia
        • 5.5.5.1.2 United Arab Emirates
        • 5.5.5.1.3 Rest of the Middle East
      • 5.5.5.2 Africa
        • 5.5.5.2.1 South Africa
        • 5.5.5.2.2 Nigeria
        • 5.5.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)
    • 6.4.1 OpenAI LP
    • 6.4.2 Cohere Technologies Inc.
    • 6.4.3 Anthropic PBC
    • 6.4.4 NVIDIA Corporation
    • 6.4.5 Google LLC
    • 6.4.6 Microsoft Corporation
    • 6.4.7 Amazon Web Services Inc.
    • 6.4.8 IBM Corporation
    • 6.4.9 Huawei Technologies Co. Ltd.
    • 6.4.10 Ericsson AB
    • 6.4.11 Nokia Corporation
    • 6.4.12 Amdocs Limited
    • 6.4.13 Juniper Networks Inc.
    • 6.4.14 Ciena Corporation
    • 6.4.15 Rakuten Symphony Inc.
    • 6.4.16 Salesforce Inc.
    • 6.4.17 ServiceNow Inc.
    • 6.4.18 Alteryx Inc.
    • 6.4.19 Telia Company AB
    • 6.4.20 AT&T Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-Space and Unmet-Need Assessment
  • 7.2 Analyst Recommendations and Suggestions