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通信の運用におけるAI:市場機会と障害

AI in Telecom Operations: Opportunities & Obstacles

発行 Heavy Reading 商品コード 696432
出版日 ページ情報 英文 48 Pages
納期: 即日から翌営業日
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通信の運用におけるAI:市場機会と障害 AI in Telecom Operations: Opportunities & Obstacles
出版日: 2018年09月11日 ページ情報: 英文 48 Pages
概要

通信ネットワークの複雑性は、SD-WAN(Software Defined-WAN)などの新サービスや、ネットワーク機能仮想化といった新技術のパラダイムの導入で拡大を続けています。膨らみ続ける顧客の期待に応えるため、通信サービスプロバイダ(CSP)は自社のネットワーク運用や計画、最適化を高度に発展させる必要があります。

当レポートでは、AI/MLの概要や主な通信業界での利用事例、現在のCSPにおける導入レベルの定量化、ネットワークドメインへのAI/ML導入における課題の分析、AI/MLを利用する10のCSPの分析や学術・規格機関・コンソーシアム・オープンソースプロジェクトにおけるAIイニシアティブの概要などについて取り上げています。

第1章 エグゼクティブサマリー

  • 主な調査結果
  • 調査対象企業

第2章 イントロダクション

  • 機械学習のカテゴリ
  • AI/MLへの関心が復活した理由

第3章 通信産業における潜在的なAI/MLの使用事例

  • ネットワーク運用監視と管理
  • 予測的メンテナンス
  • 不正の緩和
  • サイバーセキュリティ
  • 顧客サービス・マーケティングの仮想デジタルアシスタンス
  • 高度CRMシステム
  • CEM

第4章 AIのCSP導入

  • 多くのCSPはすでにIT/ネットワークにAI/MLを導入しているとのTCSの調査結果
  • より慎重なAI/MLの導入を提唱するTMフォーラム
  • AI/MLの主な活性因子となる顧客エクスペリエンス
  • ネットワーク管理におけるAI/ML

第5章 現実世界のCSP事例

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

第6章 ネットワークへのAI/ML導入に対する課題

  • 不純、不可用的、アクセス困難なデータ
  • データサイエンスの人材不足
  • 答えられる明確な疑問の不足
  • ツールの制限

第7章 学術・SDO・コンソーシアム・OSイニシアティブ

  • 学術 - 知識が定義するネットワーク
  • 規格開発機構
  • 業界のコンソーシアム - 通信インフラプロジェクト
  • オープンソース - Acumos

第8章 ベンダーのプロファイル

  • 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

目次

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.
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