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

  • Report

  • 56 Pages
  • March 2020
  • Region: Global
  • Frost & Sullivan
  • ID: 5007724

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

1. Executive Summary
  • Key Findings

2. State of Development of IoT
  • IoT Device Adoption by Sector
  • Next Phase of IoT - Prediction
  • Convergence with Emerging Technologies

3. What is Artificial Intelligence?
  • Artificial Intelligence as a Framework of Techniques
  • Process of Reasoning and Decision Making
  • Process of Learning and Machine Learning

4. 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

5. 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

6. IoT-AI Convergence - Market Landscape
  • IoT-AI Convergence - Complex Ecosystem
  • IoT Side of the Ecosystem
  • IoT-AI Side of the Ecosystem

7. 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

8. Key Takeaways
  • Legal Disclaimer

9. Appendix
  • List of Exhibits

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • Bright Machines
  • C3.ai
  • CSOT Quality Control
  • ENEL
  • FogHorn
  • GE Capacitors
  • IBM
  • Infotainment Electronic Consoles Manufacturer
  • Oil Platform Operator
  • SparkCognition