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市場調查報告書

網路業者的AI策略:主要的利用案例 & 收益化模式 (2020-2024年)

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

出版商 Juniper Research Ltd 商品編碼 923634
出版日期 內容資訊 英文
商品交期: 最快1-2個工作天內
價格
網路業者的AI策略:主要的利用案例 & 收益化模式 (2020-2024年) AI Strategies for Network Operators: Key Use Cases & Monetisation Models 2020-2024
出版日期: 2020年02月04日內容資訊: 英文
簡介

本報告提供服務供應商的領域的新的、現有的AI利用案例的相關調查,全球主要八個區域的網路業者的主要收益化模式、策略等分析,5G等新技術帶給業者AI引進策略的影響,AI實現的業者的商機,業者的AI解決方案的支出額,網路業者的AI解決方案的投資額最為成長的地區等考察。

第1章 要點 & 策略建議

第2章 網路業者的AI策略:未來市場展望

  • 簡介
    • AI (人工智能)的定義
    • 收益減少的課題
    • OTT的競爭
    • AI投資形勢
    • 用戶數的增加 & 高密度化的課題
    • 網路 & 技術

第3章 網路業者的AI策略:利用案例分析

  • 網路業者領域的AI所扮演的角色
    • AI引進的現狀:各業者
  • 對網路業者來說的AI的利用案例
    • 簡介
    • 詐欺檢測 & 減輕
    • 預知保全
    • 網路最佳化 & 虛擬化
    • 客戶支援技術
    • 銷售 & 行銷效能
    • 工作人力生產率的學習 & 開發
  • AI引進的限制
    • 價格相關障礙
    • 企業文化相關課題
    • 法規的課題

第4章 網路業者的AI策略:市場預測 & 要點

  • 簡介
    • 簡介 & 調查手法
    • 加入活用AI的行動網路的用戶
    • 網路業者的AI營運支出總額
    • 每一手機用戶的業者的AI平均支出額
目錄

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