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市場調查報告書
商品編碼
696432

通訊的運用的AI:市場機會及障礙

AI in Telecom Operations: Opportunities & Obstacles

出版日期: | 出版商: Heavy Reading | 英文 48 Pages | 商品交期: 最快1-2個工作天內

價格
  • 全貌
  • 簡介
  • 目錄
簡介

通訊網路的複雜性,SD-WAN(Software Defined-WAN)等新服務,及網路功能虛擬化的新技術的範例引進持續擴大。為了回應持續擴大的客戶的期待,通訊服務供應商(CSP)需要高度發展自家公司的網路運用和計劃、最佳化。

在本報告提供AI/ML概要和主要的通訊業的利用案例,目前CSP的引進等級的定量化,AI/ML引進網路網域的課題分析,利用AI/ML的10個CSP分析和學術、規格機關、聯盟、開放原始碼計劃的AI主張概要等。

第1章 摘要整理

  • 主要調查結果
  • 調查對象企業

第2章 簡介

  • 機器學習的範疇
  • AI/ML的關注復活的理由

第3章 通訊產業上潛在的AI/ML的使用案例

  • 網路運用監視和管理
  • 預測的維修
  • 詐欺的緩和
  • 網路安全
  • 客戶服務、行銷的虛擬數位援助
  • 高度CRM系統
  • CEM

第4章 AI的CSP引進

  • TCS調查結果中許多CSP已經將AI/ML引進IT/網路
  • TM論壇提倡更慎重的AI/ML引進
  • 成為AI/ML的主要促進要素的客戶體驗
  • 網路管理上AI/ML

第5章 現實全球CSP案例

  • AT&T
  • COLT
  • Deutsche Telekom
  • Globe Telecom
  • KDDI
  • KT
  • SK Telecom
  • Swisscom
  • Telefonica
  • Vodafone

第6章 AI/ML引進網路的課題

  • 不純,不可用的,存取困難的資料
  • 資料科學的人力資源不足
  • 能回答的明確疑問不足
  • 工具的限制

第7章 學術、SDO、聯盟、OS主張

  • 學術 - 知識定義的網路
  • 規格開發機構
  • 產業的聯盟 - 通訊基礎設施計劃
  • 開放原始碼 - Acumos

第8章 供應商的簡介

  • Afiniti
  • AIBrain
  • Anodot
  • Arago
  • Aria Networks
  • Avaamo
  • B.Yond
  • Cardinality
  • Guavus
  • Intent HQ
  • IPsoft
  • Nuance Communications
  • Skymind
  • Subtonomy
  • Tupl
  • Wise Athena

第9章 結論

目錄

The complexity of communications networks seems to increase inexorably with the deployment of new services, such as software-defined wide-area networking (SD-WAN), and new technology paradigms, such as network functions virtualization (NFV). To meet ever-rising customer expectations, communications service providers (CSPs) need to increase the intelligence of their network operations, planning and optimization.

Heavy Reading believes that artificial intelligence (AI) and machine learning (ML) will be key to automating network operations and enhancing the customer experience. Although "big data" analytics is already widespread in the telecom industry, it is typically conducted in batch, after the fact, and used to manually update rules and policies. In order to move to real-time closed-loop automation, CSPs need systems that are capable of learning autonomously. That is only possible with AI/ML.

Researchers in communication networks are tapping into AI/ML techniques to optimize network architecture, control and management, and to enable more autonomous operations. Meanwhile, practitioners are involved in initiatives such as the Telecom Infra Project's (TIP) Artificial Intelligence and Applied Machine Learning Group. AI/ML techniques are beginning to emerge in the networking domain to address the challenges of virtualization and cloud computing. Network automation platforms such as the Open Networking Automation Platform (ONAP) will need to incorporate AI techniques to deliver efficient, timely and reliable operations.

However, we must not let ourselves get carried away by the breathless hype surrounding AI/ ML. Many so-called AI/ML systems today are mainly composed of "big data" tools, statistical analysis and a healthy dose of marketing. As our sister market intelligence firm Tractica surmises in its report Artificial Intelligence for Telecommunications Applications: "An immature ecosystem for telecom AI use cases has formed, made up of legacy telecom network and business support system (BSS)/operations support system (OSS) vendors; broad-based automated customer service specialists; CRM providers; open-source communities and organizations; established cybersecurity vendors; and a small but impressive number of startups."

‘AI in Telecom Operations: Opportunities & Obstacles’ provides an overview of AI/ML, outlines the key telecom use cases, quantifies the level of adoption in CSPs today, and discusses the challenges of applying AI/ML to the networking domain. The report also provides real-world examples from 10 CSPs using AI/ML and summarizes key AI initiatives taking place in academia (Knowledge-Defined Networking), standards organizations (ETSI and IEEE), industry consortia (TIP) and open source projects (Acumos).

The report profiles 16 vendors with AI-based offerings focused on the telecom industry. The use cases are typically in customer care, marketing and networking. Other use cases include IT operations, fraud and security. As shown in the excerpt below, of the 16 vendors profiled in this report, nine of them are applying AI to networking, nine to customer care, four to marketing/CRM, and four to fraud/security.

‘AI in Telecom Operations: Opportunities & Obstacles’ is published in PDF format.

Table of Contents

1. EXECUTIVE SUMMARY

  • 1.1. Key Findings
  • 1.2. Companies Covered

2. INTRODUCTION

  • 2.1. Machine Learning Categories
  • 2.2. Why the Resurgence of Interest in AI/ML?

3. POTENTIAL AI/ML USE CASES IN TELECOM

  • 3.1. Network Operations Monitoring & Management
  • 3.2. Predictive Maintenance
  • 3.3. Fraud Mitigation
  • 3.4. Cybersecurity
  • 3.5. Customer Service & Marketing Virtual Digital Assistants
  • 3.6. Intelligent CRM Systems
  • 3.7. CEM

4. CSP ADOPTION OF AI

  • 4.1. TCS Study Suggests Most CSPs Already Using AI/ML in IT/Networking
  • 4.2. TM Forum Study Suggests More Cautious Adoption of AI/ML
  • 4.3. Customer Experience the Key Driver for AI/ML
  • 4.4. AI/ML in Network Management

5. REAL-WORLD CSP EXAMPLES

  • 5.1. AT&T
  • 5.2. COLT
  • 5.3. Deutsche Telekom
  • 5.4. Globe Telecom
  • 5.5. KDDI
  • 5.6. KT
  • 5.7. SK Telecom
  • 5.8. Swisscom
  • 5.9. Telefónica
  • 5.10. Vodafone

6. CHALLENGES OF APPLYING AI/ML TO NETWORKING

  • 6.1. Data That Is Dirty, Unavailable or Difficult to Access
  • 6.2. Lack of Data Science Talent
  • 6.3. Lack of a Clear Question to Answer
  • 6.4. Limitations of Tools

7. ACADEMIC, SDO, CONSORTIA & OS INITIATIVES

  • 7.1. Academia - Knowledge-Defined Networking
  • 7.2. Standards Development Organizations
  • 7.3. Industry Consortium - Telecom Infra Project
  • 7.4. Open Source - Acumos

8. VENDOR PROFILES

  • 8.1. Afiniti
  • 8.2. AIBrain
  • 8.3. Anodot
  • 8.4. Arago
  • 8.5. Aria Networks
  • 8.6. Avaamo
  • 8.7. B.Yond
  • 8.8. Cardinality
  • 8.9. Guavus
  • 8.10. Intent HQ
  • 8.11. IPsoft
  • 8.12. Nuance Communications
  • 8.13. Skymind
  • 8.14. Subtonomy
  • 8.15. Tupl
  • 8.16. Wise Athena

9. CONCLUSIONS

TERMS OF USE

CSP CASE STUDIES PROFILED:

  • AT&T Inc. (NYSE: T)
  • Colt Technology Services Group Ltd. (LSE: COLT)
  • Deutsche Telekom AG (NYSE: DT)
  • Globe Telecom Inc. (PSE: GLO)
  • KDDI Corp. (TYO: 9433)
  • KT Corp. (NYSE: KTC)
  • SK Telecom Co. Ltd. (KRX: 017670; NYSE: SKM)
  • Swisscom AG (SIX: SCMN)
  • Telefónica S.A. (NYSE: TEF)
  • Vodafone Group plc (NYSE: VOD)

AI/ML VENDORS PROFILED:

  • Afiniti Inc.
  • AIBrain Inc.
  • Anodot Ltd.
  • Arago GmbH
  • Aria Networks Ltd.
  • Avaamo Inc.
  • B.Yond Inc.
  • Cardinality Ltd.
  • Guavus Inc.
  • Intent HQ Ltd.
  • IPsoft Inc. /
  • Nuance Communications Inc.
  • Skymind Inc.
  • Subtonomy AB
  • Tupl Inc.
  • Wise Athena Inc.