Global AI Solutions and Applications Market Research Report 2022-2027: Combination of AI and IoT (AIoT) will Drive Up to 27% of New AI Systems Integration, Primarily Involving IIoT

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Apr 25, 2022, 07:45 ET

DUBLIN, April 25, 2022 /PRNewswire/ -- The "AI Market by Technology Type, Deployment Method, Solution Type, Integration (Technologies, Networks, and Devices) and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report evaluates the AI technology and solutions market, including an analysis of leading AI vendors, strategies, solutions and applications.

The report assesses the state of AI development, implementation, and operation. The report analyzes the forecasts AI market sizing for by technology type, deployment method, solution type, network and technology integration, and by industry verticals from 2022 through 2027.

Select Report Findings:

Total global AI solution market will reach $282 billion by 2027, growing at 28.6% CAGR
Global unsupervised machine learning market will reach $14.3 billion by 2027, growing at 21.8% CAGR
The combination of AI and IoT (AIoT) will drive up to 27% of new AI systems integration, primarily involving IIoT
AI solutions in a public cloud environment shall be almost three times those of private cloud deployments through 2027
Key AI technology systems integration opportunities include Expert Systems, Decision Support Systems, Fuzzy Systems, and Multi-Agent Systems

Artificial Intelligence (AI) represents a wide variety of technologies including Machine Learning, Deep Learning, Natural language processing, and more. We see AI increasingly embedded within many systems and applications including everything from data management to retail shopping.

The AI segment is currently very fragmented, characterized by most companies focusing on silo approaches to solutions. Longer-term, the publisher sees many solutions involving multiple AI types as well as integration across other key areas such as the Internet of Things (IoT) and data analytics.

There are many potential use cases for AI within the cybersecurity domain. For example, AI may be used in IoT to bolster security, safeguard assets, and reduce fraud. There are varying opinions about security in IoT.

For example, some companies favour a distributed(decentralized) approach whereas other companies believe a more centralized approach leveraging strictly centralized cloud architecture makes more sense. We see little possibility in which signature-based security solutions will work with IoT in an edge computing environment for a variety of reasons including the limitation on the throughput of communications between distributed endpoints and centralized cloud.

AI has various advantages including the fact that it is a more lightweight application (because it does not require all the data that comes with tracking digital signatures/code for known viruses), more effective in identifying malware, easier and less costly to maintain as there is no need to constantly identify new malware code. This is all because AI-based security is looking for malicious behaviours rather than known malicious code.

Longer-term, AI will move beyond fraud prevention and prevention of malicious acts as AI will be used to feed advanced analytics and decision making. This will be especially true in IoT solutions involving real-time data as AI will be used to make determinations for autonomous actions.

Consumer-facing apps and services supported by AI are many and varied including chatbots and Virtual Personal Assistants (VPA) in support of customer care and lifestyle enhancement. The automobile industry is another example in which AI is becoming increasingly useful, both in the near term for solutions such as the inclusion of VPAs, and longer-term use cases such as support of self-driving vehicles. Another consumer market area in which AI will be integrated is wearable technology. As wearables become more mainstream and integrate into everyday life with increasing dependency, there will be a need for integration with Artificial Intelligence, Big Data, and Analytics.

AI is expected to have a big impact on data management. However, the impact goes well beyond data management as we anticipate that these technologies will increasingly become part of every network, device, application, and service. One area important to enterprise will be Intelligent Decision Support Systems (IDSS), which are a form of Expert System that utilize AI to optimize decision making. IDSS will be used in many fields including agriculture, medicine, urban development, and other areas. IDSS will also be used in policymaking and strategy at the highest levels of enterprises well as governmental organizations.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction