Convergence of AI and IoT—Market Opportunities and Challenges, 2019
Convergence of AI and IoT—Market Opportunities and Challenges, 2019
Transformative Impact of Artificial Intelligence and Internet of Things will Enable New Levels of Prediction and Automation in IIoT Environments
02-Mar-2020
Global
$3,000.00
Special Price $2,250.00 save 25 %
Description
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.
RESEARCH: INFOGRAPHIC
This infographic presents a brief overview of the research, and highlights the key topics discussed in it.Click image to view it in full size
Table of Contents
Key Findings
IoT Device Adoption by Sector
Next Phase of IoT—Prediction
Convergence with Emerging Technologies
Artificial Intelligence as a Framework of Techniques
Process of Reasoning and Decision Making
Process of Learning and Machine Learning
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
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—Complex Ecosystem
IoT Side of the Ecosystem
IoT-AI Side of the Ecosystem
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
Legal Disclaimer
List of Exhibits
The Frost & Sullivan Story
Value Proposition: Future of Your Company & Career
Global Perspective
Industry Convergence
360º Research Perspective
Implementation Excellence
Our Blue Ocean Strategy
Popular Topics
No Index | No |
---|---|
Podcast | No |
Author | Adrian Drozd |
Industries | Information Technology |
WIP Number | MF1D-01-00-00-00 |
Is Prebook | No |
GPS Codes | 9705-C1,9AA5-C1,9B07-C1 |