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ネットワークオペレーターのためのAI戦略:主な利用例 & 収益化モデル (2020-2024年)

AI Strategies for Network Operators: Key Use Cases & Monetisation Models 2020-2024

発行 Juniper Research Ltd 商品コード 923634
出版日 ページ情報 英文
納期: 即日から翌営業日
価格
本日の銀行送金レート: 1GBP=137.85円で換算しております。
ネットワークオペレーターのためのAI戦略:主な利用例 & 収益化モデル (2020-2024年) AI Strategies for Network Operators: Key Use Cases & Monetisation Models 2020-2024
出版日: 2020年02月04日 ページ情報: 英文
概要

当レポートでは、サービスプロバイダーの領域における新しい・既存のAI利用例について調査し、世界の主要8地域におけるネットワークオペレーターの主な収益化モデル・戦略などを分析しており、5Gなどの新しい技術がオペレーターのAI導入戦略へ及ぼす影響、AIが実現するオペレーターの収益機会、オペレーターによるAIソリューションへの支出額、ネットワークオペレーターによるAIソリューションへの投資額が最も成長すると見込まれる地域などについて考察しています。

第1章 要点 & 戦略提言

第2章 ネットワークオペレーターのためのAI戦略:将来の市場展望

  • イントロダクション
    • AI (人工知能) の定義
    • 収益減少の課題
    • OTTとの競合
    • AI投資情勢
    • 加入者数の増加 & 高密度化の課題
    • ネットワーク & 技術

第3章 ネットワークオペレーターのためのAI戦略:利用例の分析

  • ネットワークオペレーター分野におけるAIの役割
    • AI導入の現況:オペレーター別
  • ネットワークオペレーターにとってのAIの利用例
    • イントロダクション
    • 不正検出 & 軽減
    • 予知保全
    • ネットワーク最適化 & 仮想化
    • 顧客対応技術
    • 販売 & マーケティングパフォーマンス
    • ワークフォース生産性の学習 & 開発
  • AI導入の制限
    • 価格に関連した障壁
    • 企業文化に関連した課題
    • 規制の課題

第4章 ネットワークオペレーターのためのAI戦略:市場予測 & 要点

  • イントロダクション
    • イントロダクション & 調査手法
    • AIを活用するモバイルネットワークに加入しているユーザー
    • ネットワークオペレーターのAIオペレーション支出総額
    • モバイル加入者1人あたりのオペレーターのAI平均支出額
目次

Overview

Juniper Research's latest ‘AI Strategies for Network Operators ’ report evaluates new and existing AI use cases for the service provider space. It assesses key monetisation models and strategies for network operators across 8 global regions.

This must-read research provides comprehensive quantitative and qualitative analysis into the key drivers behind operator-lead AI adoption globally, and presents 4-year forecasts for operator average and total spend on AI solutions by 2024.

This research suite includes:

  • Market Trends & Opportunities (PDF)
  • 4-Year Deep Dive Data and Forecasting (PDF & Excel)

Key Features

  • Network Operator Landscape: Evaluation of the state of the network operator industry globally, identifying key challenges, business models and key opportunities for technologies, including:
    • 5G & Edge Computing
    • Serverless Infrastructure
    • AIaaS (AI as a Service)
    • Omnichannel Communication & Chatbots
  • AI for Network Operators Use Case Analysis: Evaluating how network operators will leverage AI solutions in the future, with comprehensive focus on 6 key use cases that will drive revenue gains and cost efficiencies, including:
    • Fraud Detection & Mitigation
    • Predictive Maintenance
    • Network Optimisation & Virtualisation
    • Customer-facing Technologies
    • Sales & Marketing Performance
    • Learning & Development and Workforce Productivity
  • Interviews: 3 case studies of market-leading AI analytics vendors; assessing how the largest network operators today leverage AI for network optimisation, sales performance, customer experience improvement and other revenue-boosting use cases. These include:
    • Hitachi Vantara
    • Avora
    • Groundhog Technologies
  • Benchmark Industry Forecasts: Provided for global operator spend on AI solutions across 8 key regions, and aligned with a detailed analysis of future development.

Key Questions

  • 1. How will emerging technologies, such as 5G, affect operators' strategies for AI adoption?
  • 2. What is the AI-enabled revenue opportunity for operators over the next 4 years?
  • 3. What will the value be of operators' spend on AI solutions by 2024?
  • 4. How are leading AI vendors globally serving network operators?
  • 5. Which geographic regions are expected to have highest growth rate for network operator investment in AI solutions by 2024?
  • 6. What will the most prevalent AI-driven monetisation use cases for operators be in emerging and developed markets in 4 years' time?

Companies Referenced

  • Interviewed: Avora, Groundhog Technologies, Hitachi Vantara, Infobip, Interop Technologies.
  • Case Studied: Avora, Groundhog Technologies, Hitachi Vantara.
  • Mentioned: Amazon, Apple, BEREC (The Body of European Regulators for Electronic Communications, Bharti Airtel, BSNL, China Mobile, Chunghwa Telecom, Deloitte, Deutsche Telekom, Ericsson, Etisalat, EY, Facebook, FCC (Federal Communications Commission), Google, Haptik, Huawei, Hulu, Indosat, Line, Microsoft, MIT (the Massachusetts Institute of Technology), Netflix, NTT Docomo, O2, Ooredoo, Optus, Orange, Rakuten, Reliance Jio, Robi, Salt Mobile, SingTel, Skype, Sprint, STC, Telefonica, Telkomsel, T-Mobile, Viettel, Vocord, Vodafone, WeChat, WhatsApp, YouTube.

Data & Interactive Forecast

Juniper Research's ‘AI Strategies for Network Operators ’ forecast suite includes:

  • 4-year benchmark forecasts for key metrics by 8 key regions:
    • North America
    • Latin America
    • West Europe
    • Central & East Europe
    • Far East & China
    • Indian Subcontinent
    • Rest of Asia Pacific
    • Africa & Middle East
  • Country-level splits including:
    • Canada
    • Germany
    • UK
    • US
  • Forecasts for network operators spend on AI services including:
    • Average Network Operator Spend on AI per Mobile Subscriber per Annum ($)
    • Total Network Operator Spend on AI per Annum ($m)
  • Access to the full set of forecast data of 6 tables and over 540 datapoints.
  • Interactive Excel Scenario tool allowing users the ability to manipulate Juniper Research's data for 5 different metrics.

Juniper Research's highly granular interactive Excels enable clients to manipulate Juniper Research's forecast data and charts to test their own assumptions using the Interactive Scenario Tool and compare select markets side by side in customised charts and tables. IFxls greatly increase clients' ability to both understand a particular market and to integrate their own views into the model.

Table of Contents

1. Key Takeaways & Strategic Recommendations

  • 1.1. Key Takeaways
  • 1.2. Strategic Recommendations
  • 1.3. Research Aims & Objectives

2. AI Strategies for Network Operators: Future Market Outlook

  • 2.1. Introduction
    • 2.1.1. Definition of AI (Artificial Intelligence)
    • 2.1.2. The Declining Revenues Challenge
    • 2.1.3. OTT Competition
      • Figure 2.2: Enablers of OTT Players' Business Models
      • Figure 2.3: Total Number of OTT Users vs Mobile Subscribers (m), 2016 & 2019
    • 2.1.4. The AI Investment Landscape
      • i. Global AI Investment Landscape
    • 2.2.3. Increasing Number of Subscribers & Densification Issues
      • i. High Levels of Handset Penetration & Evolving Data Usage
      • ii. Network Operator's Investment in AI
        • Table 2.4: Mobile Penetration as a Proportion of Global Population (%), Split by 8 Key Regions, 2019-2024
        • Table 2.5: Total Data Traffic Generated by Mobile Handsets per Annum (PB) Split by Data Categories, 2019-2024
        • Table 2.6: Total Data Traffic Carried Via Cellular Networks Per Annum (PB) Split by Originating Device Type, 2019-2024
      • ii. Juniper's View
    • 2.2.4. Networks & Technology
      • i. The Rise of 5G

3. AI Strategies for Network Operators: Use Case Analysis

  • Figure 2.7: Global Mobile 5G Active Connections (m) Split by 8 Key Regions 2020 & 2025
  • Figure 2.8: The Evolution of Wireless Networks
  • Figure 2.9: Shift to Centralised & Decentralised Computing, 1970-2030
  • 3.1. AI's Role in the Network Operator Field
    • 3.1.1. Current State of AI Adoption by Operators
      • i. Innovations in Customer Service
  • 3.2. AI Use Cases for Network Operators
    • 3.2.1. Introduction
    • 3.2.2. Fraud Detection & Mitigation
      • Case Study: Hitachi Vantara
      • i. Hitachi Vantara's Analytics Solution
    • 3.2.3. Predictive Maintenance
    • 3.2.4. Network Optimisation & Virtualisation
      • Case Study: Groundhog Technologies
      • i. Groundhog's Solutions
        • Case Study: Avora
      • i. Avora's Solutions
      • ii.Operator Case Studies
    • 3.2.5. Customer-facing Technologies
    • 3.2.6. Sales & Marketing Performance
    • 3.2.7. Learning & Development for Workforce Productivity
  • 3.3. AI Adoption Limitations
    • 3.3.1. Price-related Barriers
      • Figure 3.2: Total Network Operator Spend on AI CAGR ($m) Split by 8 Key Regions 2019-2024
    • 3.3.2. Challenges Related to Organisational Culture
    • 3.3.3. Regulatory Challenges

4. AI Strategies for Network Operators: Market Forecasts & Key Takeaways

  • 4.1. Introduction
    • 4.1.1. Introduction & Methodology
      • Figure 4.1: Methodology for AI Operator Spend Forecasts
    • 4.1.2. Users that Subscribe for Mobile Networks that Leverage AI
      • Figure & Table 4.2: Total Number of Subscribers that Subscribe to Operator Networks Leveraging AI (m) Split by 8 Key Regions, 2019-2024
    • 4.1.3. Total Network Operator Spend on AI Solutions
      • Figure & Table 4.3: Total Network Operator Spend on AI per Annum ($m) Split by 8 Key Regions, 2019-2024
    • 4.1.4. Average Operator AI spend per Mobile Subscriber
      • Figure & Table 4.4: Average Network Operator Spend on AI per Mobile Subscriber per Annum ($) Split by 8 Key Regions, 2019-2024