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市場調查報告書

AI與IoT的融合:市場機會、課題 (2019年)

Convergence of AI and IoT-Market Opportunities and Challenges, 2019

出版商 Frost & Sullivan 商品編碼 929270
出版日期 內容資訊 英文 56 Pages
商品交期: 最快1-2個工作天內
價格
AI與IoT的融合:市場機會、課題 (2019年) Convergence of AI and IoT-Market Opportunities and Challenges, 2019
出版日期: 2020年03月02日內容資訊: 英文 56 Pages
簡介

融合了IoT與AI的解決方案具備提升業務效率和成本最佳化等優點,隱藏著為供應商和引進企業帶來新獲益的可能性。

本報告調查了AI與IoT融合帶來的市場機會,並統整了IoT發展現況、IoT環境中AI系統的重要性、AI及IoT的匯流系統結構、引進規劃、案例研究、引進的推動因素與挑戰、成功策略與成長機會的分析、未來展望等。

執行摘要

IoT發展的現況

  • 引進IoT裝置:部門別
  • IoT的下一階段:預測
  • 與新興技術的匯流

何謂AI

  • AI作為技術框架
  • 邏輯化與決策流程
  • 學習與機器學習流程

IoT、AI匯流:系統結構、引進規劃

  • AI系統在IoT環境的作用
  • AI系統的即時行動&預測功能
  • AI與IoT匯流的系統結構
  • 引進規劃:雲端基礎的AI與IoT匯流模式
  • 引進規劃:邊緣基礎的AI與IoT匯流模式
  • 引進規劃:混合基礎的AI與IoT匯流模式

AI與IoT匯流:引進

  • 定性分析:部門別的引進動態
  • 案例研究:GE、FogHorn
  • 案例研究:CSOT、IBM
  • 案例研究:ENEL、C3.ai
  • 案例研究:Bright Machines
  • 案例研究:SparkCognition
  • 推動引進因素
  • 引進的挑戰
  • 開發AI專案:流程與成本
  • IoT、AI專案投資評估

AI、IoT匯流:市場情勢

  • AI、IoT匯流:複雜的生態系統
  • 生態系統的IoT側
  • 生態系統的IoT、AI側

成長機會與建議行動

  • 成長機會:推動產業部門的數位轉型
  • 成長機會:推動電力部門的數位轉型
  • 成長機會:為了中期機會致力於市民取向領域
  • 成長機會:全球性IoT、AI策略開發
  • 成長機會:以挖角和收購帶來創新
  • 成功、成長的策略要務

總論

附錄

關於Frost & Sullivan

目錄
Product Code: MF1D-67

Transformative Impact of Artificial Intelligence and Internet of Things will Enable New Levels of Prediction and Automation in IIoT Environments

The convergence of Internet of Things (IoT) and artificial intelligence (AI) has the potential to drive new revenues for vendors and adopters. Improved efficiency and cost optimization of organizational processes are core advantages that are made possible through the application of such solutions.

IoT-AI convergence can deliver new advantages in terms of process automation enablement. It also facilitates proactive approaches such as the ability to predict undesired conditions and situations that may occur in the environment in which the IoT solution is deployed. Organizations can benefit from the convergence of IoT and AI if they are data ready and security proofed and has a sound digital transformation strategy that embraces emerging technologies.

The vendor landscape features a combination of IoT providers and analytics participants and an emerging and lively world of start-ups offering IoT-AI platforms and solution suites at both cloud and edge levels. The manufacturing, oil and gas and mining industries appear to be the most receptive to the convergence of IoT and AI solutions. The energy industry is looking with interest at the convergence, with some early examples of adoption evident. There is also strong potential in healthcare and smart city applications.

This study will outline:

  • The state of development of IoT
  • An overview of Artificial Intelligence?
  • Architecture and deployment scenarios
  • Adoption levels
  • Market landscape

The convergence of IoT and AI is in an early stage, but the pace of adoption will accelerate in the period 2019-2022. Designing and deploying IoT-AI-based solutions requires a ‘small deployment-test-scale' approach, where AI specialists can play an important role.

After the machine-to-machine (M2M) period in which the objective was to monitor assets remotely for specific business purposes, IoT brought the objective of monitoring environments, controlling them, and acting on them using different sources of data. The next step is predicting the behavior of the environments through the behavior of their components (machines, humans, and objects). Predicting means prescribing changes to avoid undesired situations.

There are several areas of convergence occurring across the IoT arena that seek to solve the challenges experienced with the technology. Distributed Ledger Technology (often coined Blockchain) aims to secure IoT and create a network of trusted objects. 5G is the infrastructure enabler. Infrared (IR) looks at the interaction between humans and IoT environments. At the core of all this, there is AI, which enables a sophisticated level of data analysis, particularly predictive analysis.

Table of Contents

Executive Summary

  • Key Findings

State of Development of IoT

  • IoT Device Adoption by Sector
  • Next Phase of IoT-Prediction
  • Convergence with Emerging Technologies

What is Artificial Intelligence?

  • Artificial Intelligence as a Framework of Techniques
  • Process of Reasoning and Decision Making
  • Process of Learning and Machine Learning

IoT-AI Convergence-Architectural View and Deployment Scenarios

  • Role of AI System in an IoT Environment
  • Real-time Action and Prediction Capability of an AI System
  • Architectural View of IoT-AI Convergence
  • Deployment Scenarios-Cloud-based AI-IoT Convergence Model
  • Deployment Scenarios-Edge-based AI-IoT Convergence Model
  • Deployment Scenarios-Hybrid AI-IoT Convergence Model

IoT-AI Convergence-Adoption

  • Adoption by Sector-Qualitative Assessment
  • Case Study-GE Capacitors and FogHorn
  • Case Study-CSOT Quality Control and IBM
  • Case Study-ENEL and C3.ai
  • Case Study-Infotainment Electronic Consoles Manufacturer and Bright Machines
  • Case Study-Oil Platform Operator and SparkCognition
  • Drivers for Adoption
  • Challenges of Adoption
  • Developing an AI Project-Process and Costs
  • IoT-AI Project Investment Assessment

IoT-AI Convergence-Market Landscape

  • IoT-AI Convergence-Complex Ecosystem
  • IoT Side of the Ecosystem
  • IoT-AI Side of the Ecosystem

Growth Opportunities and Companies to Action

  • Growth Opportunity 1-Empowering Digital Transformation in Industrial Sectors
  • Growth Opportunity 2-Empowering Digital Transformation in the Utility Sector
  • Growth Opportunity 3-Attention on Citizen-oriented Areas for a Mid-term Opportunity
  • Growth Opportunity 4-Developing a Global IoT-AI Strategy
  • Growth Opportunity 5-Innovation via Scouting and Acquisition
  • Strategic Imperatives for Success and Growth

Key Takeaways

  • Key Takeaways
  • Legal Disclaimer

Appendix

  • List of Exhibits

The Frost & Sullivan Story

  • The Frost & Sullivan Story
  • Value Proposition: Future of Your Company & Career
  • Global Perspective
  • Industry Convergence
  • 360º Research Perspective
  • Implementation Excellence