產業用AI及AIoT市場(2021年∼2026年)

本報告提供產業用AI及AIoT市場相關調查，產業用AI定義，產業用及非產業用AI解決方案比較分析，各市場區隔(提供類型、產業、地區、使用案例的)市場規模與成長率，新進入市場的Start-Ups企業數與其資金籌措趨勢，產業用AI及AIoT的實行案例，市場趨勢，市場課題等資訊。

調查對象企業

Accenture，阿里巴巴，Amazon AWS，AMD，Braincube，C3 AI，Cognizant，Google，IBM，Intel，Microsoft，Nvidia，SAP，SAS，Sparkcognition，TCS，Uptake，Zenodys等616家公司

目錄

第1章 摘要整理

• 摘要整理
• 產業用AI的來自調查報告(2019年發行)的變更點

第2章 簡介

• 定義 & 語義的歧義性的解除
• 平台定義
• AI的種類
• 產業用、非產業用AI解決方案比較

第3章 科技概要

• AIoT流程和科技概要
• Deep Dive:資料管理
• Deep Dive:模式開發
• Deep Dive:模式訓練
• Deep Dive:模式管理
• Deep Dive:MLOps
• Deep Dive:AI硬體設備

第4章 市場規模與展望

• 全球產業用AI市場
  • 各提供類型
  • 各產業
  • 各使用案例
  • 各地區
  • 各國
  • 各地區、產業
  • 前十名國家、各產業
• Deep Dive:主要5個國家

第5章 競爭情形

• 企業形勢
• Deep Dive:大產業用AI供應商12家公司
• Deep Dive:產業用AI平台概要
• Deep Dive:產業用AI平台的競爭情形
• Deep Dive:產業用AI的Start-Ups企業
• Deep Dive:AI程式庫

第6章 使用案例

• 產業用AI的主要的使用案例
• 其他有潛力使用案例

第7章 產業用AI軟體的經營模式

• 產業用AI的收益來源
• AIoT供應商的提供
• Deep Dive:AI模式的收益化

第8章 由於終端用戶的採用

• 產業用AI及資料分析的相關調查
• 各類型的AI採用的相關調查

第9章 趨勢與課題

• 趨勢
• 課題

第10章 調查手法和市場定義

第11章 關於IoT Analytics

252-page report examining artificial intelligence in industrial settings, including:

• Market size and outlook 2021-2026, with breakdowns:
  • By Offering Type (AI Hardware, AI Software, AI Services)
  • By Industry
  • By Use case
  • By Region/Country

• Vendor landscape of ~630 firms, with detailed information on top industrial AI vendors' market share, startup funding, and AI libraries

• Detailed use case analysis and discussion of 6 recent case studies

• In-depth analysis of industrial AI software business models, including AI model development monetization strategies

• Results from two surveys on the "Industrial AI end-user perspective" (incl. a view on user preferences and key success factors)

• Discussion of 11 current trends and 2 challenges, plus updates on previously identified trends and challenges

• Database of ~630 key vendors active in the industrial AI space

• Database of ~150 industrial AI projects

• And More...

The Industrial AI and AIoT Market Report 2021-2026 is the first major update of IoT Analytics' ongoing coverage on industrial AI and AIoT market (Industrial and Software workstreams). The report includes new information on the implementation and technology stack of industrial AI solutions, updates on market forecasts (by offering type, industry, region, and use case) and top players' market shares. Several new trends, and challenges are highlighted, and new deep dives on select topics are provided (e.g., industrial AI software business models).

Questions answered in this report:

• How Industrial AI is defined

• What the major differences between industrial and non-industrial AI solutions are

• How the industrial AI and AIoT market has evolved since 2018

• How big the industrial AI and AIoT market is and how fast it is growing across various dimensions (by offering type, region, industry, use case)

• How many start-ups have joined the market and their funding details

• Which companies are leading the market (by offering type, including market shares)

• How industrial AI and AIoT solutions are being implemented today across 12 detailed use case categories and 33 sub use cases (with details on the implementation process of industrial AI solutions and 6 detailed case studies)

• What the new developments within the various tech layers are and implementation steps of industrial AI solutions (AI hardware, Data management, AI model development, AI model management)

• What the main AI analytics methods are that are used today

• How industrial AI solutions are sold and offered to end users

• How the end use thinks about Industrial AI adoption, satisfaction with specific vendors, solution preferences, success factors, current and future budgets

• What trends are characterizing the market currently

• What challenges are holding the market back

• And More...

Selected companies:

Accenture, Alibaba, Amazon AWS, AMD, Braincube, C3 AI, Cognizant, Google, IBM, Intel, Microsoft, Nvidia, SAP, SAS, Sparkcognition, TCS, Uptake, Zenodys +616 more.

Table of Contents

1. Executive Summary

• 1.1 Executive Summary
• 1.2 Changes Since the 2019 Industrial AI Report

2. Introduction

• 2.1 Definition and Disambiguation
• 2.2 Definition of a Platform
• 2.2 Types of AI
• 2.3 Non-industrial vs. Industrial AI Solutions

3. Technology Overview

• 3.1 AIoT Process and Technology Overview
• 3.2 Deep Dive: Data Management
• 3.3 Deep Dive: Model Development
• 3.4 Deep Dive: Model Training
• 3.5 Deep Dive: Model Management
• 3.6 Deep Dive: Machine Learning Operations
• 3.7 Deep Dive: AI Hardware

4. Market Size and Outlook

• 4.1 Global Industrial AI Market
  • 4.1.1 Global Industrial AI Market, by Offering Type
  • 4.1.2 Global Industrial AI Market, by Industry
  • 4.1.3 Global Industrial AI Market, by Use Case
  • 4.1.4 Global Industrial AI Market, by Region
  • 4.1.5 Global Industrial AI Market, by Country
  • 4.1.6 Global Industrial AI Market, by Region and Industry
  • 4.1.7 Global Industrial AI Market, by Top 10 Countries and Industry
• 4.2 Deep Dives: The Leading Five Countries

5. Competitive Landscape

• 5.1 Company Landscape
• 5.2 Deep Dive: The 12 Largest Industrial AI Vendors
• 5.3 Deep Dive: Overview of Industrial AI Platforms
• 5.4 Deep Dive: Industrial AI Platforms-Competitive Landscape
• 5.5 Deep Dive: Industrial AI Startups
• 5.6 Deep Dive: AI Libraries

6. Use Cases

• 6.1 Main Industrial AI Use Cases
• 6.2 Other Promising Use Cases

7. Industrial AI Software Business Models

• 7.1 Industrial AI Revenue Streams
• 7.2 AIoT Vendors' Offerings
• 7.3 Deep Dive: The Monetization of AI Models

8. End User Adoption

• 8.1 Industrial AI and Data Analytics Survey
• 8.2 Types of AI Adoption Survey

9. Trends and Challenges

• 9.1 Trends
• 9.2 Challenges

10. Methodology and Market Definitions

11. About IoT Analytics