世界各国のリアルタイムなデータ・インテリジェンスで皆様をお手伝い

通信用途向け人工知能:ネットワークオペレーションモニタリング&管理、カスタマーサービス&マーケティングVDA、インテリジェントCMR(顧客関係管理)システム、カスタマーエクスペリエンス管理、サイバー犯罪、不正緩和、その他の使用事例:市場分析と予測

Artificial Intelligence for Telecommunications Applications

Network Operations Monitoring & Management, Customer Service & Marketing VDAs, Intelligent CRM Systems, Customer Experience Management, Cybersecurity, Fraud Mitigation, and Other Use Cases: Market Analysis and Forecasts

 

出版社 出版年月電子版価格 ページ数図表数
Tractica
トラクティカ
2018年4月US$4,500
1-5ユーザライセンス
76 44

サマリー

このレポートは通信AIの市場と使用事例に関連する技術課題を分析しています。機械学習、深層学習(ディープラーニング)、自然言語処理、機械推論などの技術をカバーしています。産業エコシステムの主要企業のプロフィールや、2016-2025年の世界の通信AI向けソフトウェア、ハードウェア、サービスの市場予測を地域と使用事例別に掲載しています。

主な掲載内容  ※目次より抜粋

  1. エグゼクティブサマリー
  2. 市場課題
    1. 市場促進要因
    2. 市場阻害要因
  3. 使用事例
    1. ネットワークオペレーションモニタリングと管理
    2. 予測管理
    3. 不正緩和
    4. サイバーセキュリティ
    5. カスタマーサービスとマーケティングのバーチャルデジタルアシスタンス
    6. 顧客体験管理の向上
  4. 技術課題
    1. 機械学習(マシンラーニング)
    2. 深層学習(ディープラーニング)
    3. セキュアードデータ VS 非セキュアードデータ
    4. 教師あり学習 VS 教師なし学習
    5. 自然言語処理
    6. 自然言語生成
    7. ハードウェアインフラストラクチャ
  5. 産業の主要企業
  6. 市場予測
    1. 通信AIソフトウェア収益:使用事例毎、地域毎、セグメント毎
    2. 通信AIサービス収益:サービスタイプ毎
    3. 通信AIハードウェア収益:ハードウェアタイプ毎

Report Details

The telecommunication service provider industry is one of the biggest businesses in the world. It also has historically been a capital-intensive industry with high fixed costs, which has put pressure on telecom operators to control their variable costs, particularly human capital. This tension surrounding profitability is intensifying. Many telecom operators crossed the point where revenue per bit is lower than cost per bit in 2017. Telecom operators are threatened by fast and highly-efficient web-scale companies and are straining under the challenge posed by digital transformation. On top of all that, telecom operators must solve how to profitably manage and operate the dizzyingly complex next-generation 5G/Internet of Things (IoT) networks.

It is an industry ripe for artificial intelligence (AI)-driven solutions, with their promise of lowering costs and boosting efficiencies through automation. Many telecom operators have begun to experiment and deploy AI-driven solutions in both customer-facing and internal organizations. Tractica has identified seven key telecom AI use cases: network operations monitoring and management, predictive maintenance, fraud mitigation, cybersecurity, customer service and marketing virtual digital assistants (VDAs), intelligent customer relationship management (CRM) systems, and customer experience management (CEM). This report details the market drivers and barriers, technologies, key players, and forecasts for these seven telecom AI use cases.

This Tractica report examines the market and technology issues surrounding telecom AI use cases. The technologies covered include machine learning, deep learning, natural language processing, and machine reasoning. It presents profiles for key industry players throughout the ecosystem. The report also includes global software, hardware, and services market forecasts for telecom AI, segmented by region and use case, covering the period from 2016 through 2025.

Key Questions Addressed:

  • What is the current state of the market for telecom AI and how will it develop over the next decade?
  • What are the key use cases that will drive greater telecom AI adoption?
  • What are the key drivers of market growth, and the key challenges faced by telecom AI, in each world region?
  • Who are the key players in the market, what is their competitive positioning, and which ones are poised for greatest success in the years ahead?
  • What is the size of the telecom AI market opportunity?

Who Needs This Report?

  • Telecom network operators
  • Telecom hardware and software providers
  • AI hardware and software companies
  • Network operations solution providers
  • Customer experience-focused solution providers
  • Cybersecurity and fraud management solution providers
  • Government agencies
  • Investor community


目次

Table of Contents

  1. Executive Summary
    1. Introduction
    2. Market Overview
    3. Market Drivers
      1. Communications Service Providers Reinvent Themselves as Digital Service Providers
      2. Complexity of Service Offerings Require Automation
      3. The Promise of Autonomous Networks
    4. Market Barriers
      1. Digital Transformation
        1. Slow Rollout of Software-Defined Networks and Network Function Virtualization
      2. Feuding Standards
      3. Challenging Abstraction Layers for Telecom Data
      4. Desire for Centralized AI within Communications Service Providers
    5. Use Cases
      1. Network Operations Monitoring and Management
      2. Predictive Maintenance
      3. Fraud Mitigation
      4. Cybersecurity
      5. Customer Service and Marketing Virtual Digital Assistants
      6. Intelligent Customer Relationship Management Systems
      7. Improve Customer Experience Management
    6. Market Forecast Highlights
      1. Telecom AI Software Revenue by Use Case
      2. Telecom AI Software Revenue by Region
      3. Telecom AI Total Revenue by Segment
    7. Conclusions and Recommendations
  2. Market Issues
    1. Introduction
    2. Market Drivers
      1. Communications Service Providers Reinvent Themselves as Digital Service Providers
      2. Complexity of Service Offerings Require Automation
      3. The Promise of Autonomous Networks
      4. Alignment of Technological Capabilities
    3. Market Barriers
      1. Digital Transformation
        1. Slow Rollout of Software-Defined Networks and Network Function Virtualization
      2. Feuding Standards
      3. Challenging Abstraction Layers for Telecom Data
      4. Desire for Centralized AI within Communications Service Providers
      5. Lack of Experienced Talent
      6. Change Management Issues
  3. Use Cases
    1. Introduction
    2. Network Operations Monitoring and Management
      1. Apstra
      2. EnterpriseWeb
      3. Aria Networks
      4. Huawei
      5. Juniper Networks
      6. Nokia
    3. Predictive Maintenance
    4. Fraud Mitigation
    5. Cybersecurity
      1. Darktrace
    6. Customer Service and Marketing Virtual Digital Assistants
      1. Virtual Digital Assistant Market Drivers
      2. Creative Virtual
      3. Nuance
    7. Intelligent Customer Relationship Management Systems
      1. Automation Anywhere
      2. CallidusCloud (SAP)
      3. Conversica
    8. Improve Customer Experience Management
      1. DeviceBits
      2. Guavus (Thales)
    9. Sandvine
  4. Technology Issues
    1. Introduction
    2. Definition of AI
    3. Machine Learning
    4. Deep Learning
    5. The Difference between Machine Learning and Deep Learning
    6. Structured Data versus Unstructured Data
    7. Supervised versus Unsupervised Learning
    8. Natural Language Processing
      1. Legacy Natural Language Processing Gives Way to Hybrid Natural Language Processing
      2. Importance of Machine and Deep Learning to Natural Language Processing
      3. Understanding Natural Language: Word Maps and Language Models
    9. Natural Language Generation
    10. Hardware Infrastructure
      1. Hardware Considerations: Chipsets, Power, and Performance
      2. Server Landscape
        1. AI Workstations
        2. Training versus Inference Products
      3. Cloud Infrastructure
      4. Big Data AI Applications: Technology Challenges
        1. Volume of Big Data
        2. High Variety of Data
        3. Data Velocity
  5. Key Industry Players
    1. Introduction
    2. Amdocs
    3. Apstra
    4. Aria Networks
    5. AT&T
    6. Automation Anywhere
    7. CallidusCloud
    8. Conversica
    9. Creative Virtual
    10. Darktrace
    11. DeviceBits
    12. EnterpriseWeb
    13. Ericsson
    14. Guavus
    15. Huawei
    16. Juniper Networks
    17. Nokia
    18. Nuance
    19. Sandvine
  6. Market Forecasts
    1. Forecast Methodology
    2. Telecom AI Software Revenue
      1. Telecom AI Software Revenue by Use Case
      2. Telecom AI Software Revenue by Region
      3. Telecom AI Total Revenue by Segment
    3. Telecom AI Services Revenue
      1. Telecom AI-Driven Installation Services Revenue
      2. Telecom AI-Driven Training Services Revenue
      3. Telecom AI-Driven Customization Services Revenue
      4. Telecom AI-Driven Application Integration Services Revenue
      5. Telecom AI-Driven Support and Maintenance Services Revenue
      6. Telecom AI-Driven Cloud Services Revenue
    4. Telecom AI-Driven Hardware Revenue
      1. Telecom AI-Driven GPU Revenue
      2. Telecom AI-Driven CPU, ASIC, FPGA Revenue
      3. Telecom AI-Driven Networks Products Revenue
      4. Telecom AI-Driven Storage Device Revenue
    5. Conclusions and Recommendations
  7. Company Directory
  8. Acronym and Abbreviation List
  9. Table of Contents
  10. Table of Charts and Figures
  11. Scope of Study, Sources and Methodology, Notes

 

List of Charts, Figures, and Tables

Charts
  • Telecom AI Software Revenue by Use Case, World Markets: 2016-2025
  • Telecom AI Software Revenue by Region, World Markets: 2016-2025
  • Telecom AI Total Revenue by Segment, World Markets: 2016-2025
  • Telecom AI Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Installation Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Training Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Customization Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Application Integration Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Support and Maintenance Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Cloud Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Hardware Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Hardware Revenue by Product Category, World Markets: 2016-2025
  • Telecom AI-Driven GPU Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven CPU, ASIC, FPGA Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Network Products Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Storage Device Revenue by Region, World Markets: 2016-2025
Figures
  • Digital Transformation Moves Slowly
  • AI Encompasses Numerous Technologies
  • Schematic Representation of a Deep Neural Network
  • Examples of Word Embeddings
  • Progression of Natural Language Generation
Tables
  • Telecom AI Software Revenue by Region, World Markets: 2016-2025
  • Telecom AI Total Revenue by Segment, World Markets: 2016-2025
  • Telecom AI Hardware Revenue by Region, World Markets: 2016-2025
  • Telecom AI Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI Total Software, Service, and Hardware Revenue by Region, World Markets: 2016-2025
  • Telecom AI Software Revenue by Use Case, World Markets: 2016-2025
  • Telecom AI-Driven Hardware Revenue by Product Category, World Markets: 2016-2025
  • Telecom AI-Driven Cloud Services Revenue, World Markets: 2016-2025
  • Telecom AI Cloud Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven CPU, ASIC, FPGA Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven GPU Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Network Products Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Storage Devices Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Services Revenue by Service Category: 2016-2025
  • Telecom AI-Driven Installation Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Training Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Customization Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Application Integration Services Revenue by Region, World Markets: 2016-2025
  • Telecom AI-Driven Support and Maintenance Services Revenue by Region, World Markets: 2016-2025
  • Additional Industry Participants

 

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