Global Artificial Intelligence Market Report 2021: AI Solution Market will Reach $227.5Billion by 2026, Growing at 28.6% CAGR


Dublin, May 20, 2021 (GLOBE NEWSWIRE) -- The "AI Market by Technology Type, Deployment Method, Solution Type, Integration (Technologies, Networks, and Devices) and Industry Verticals 2021 - 2026" report has been added to's offering.

This report evaluates the AI technology and solutions market, including 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 2021 through 2026.

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 with 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 favor 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 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 behaviors 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 AI 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 which 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 policy making and strategy at the highest levels of enterprise as well as governmental organizations.

Select Report Findings:

  • Total global AI solution market will reach $227.5B by 2026, growing at 28.6% CAGR
  • Global unsupervised machine learning market will reach $13.9 by 2026, growing at 21.8% CAGR
  • Combination of AI and IoT (AIoT) will drive up to 23% 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 2026
  • Key AI technology systems integration opportunities include Expert Systems, Decision Support Systems, Fuzzy Systems, and Multi-Agent Systems

Key Topics Covered:

1.0 Executive Summary
1.1 Overview
1.2 Research Objectives
1.3 Select Findings

2.0 Introduction
2.1 Defining Artificial Intelligence
2.1.1 Artificial General Intelligence
2.1.2 Artificial Super Intelligence
2.1.3 Artificial Intelligence Language
2.1.4 Conversational User Interfaces
2.2 Artificial Intelligence Types
2.3 Artificial Intelligence Systems
2.4 AI Outcomes and Enterprise Benefits
2.5 Cognitive Computing and Swarm Intelligence
2.6 AI Market SWOT Analysis
2.6.1 Market Drivers and Strengths
2.6.2 Market Constraints and Threats
2.6.3 Market Opportunities
2.7 AI Technology Goals
2.8 AI Tools and Approaches
2.9 AI Market Predictions
2.10 AI Market Landscape
2.10.1 Embedded Devices and Things
2.10.2 AI Software and Platforms
2.10.3 AI Component and Chipsets
2.10.4 AI Services and Deployment Options
2.11 AI Patent and Regulatory Framework
2.12 Value Chain Analysis
2.12.1 Artificial Intelligence Companies
2.12.2 IoT Companies and Suppliers
2.12.3 Data Analytics Providers
2.12.4 Connectivity Infrastructure Providers
2.12.5 Components and Chipsets Manufacturers
2.12.6 Software Developers and Data Scientists
2.12.7 End Users
2.13 Competitive Landscape Analysis

3.0 Technology and Application Analysis
3.1 AI Technology Matrix
3.1.1 Machine Learning Deep Learning Supervised vs. Unsupervised Learning Reinforcement Learning
3.1.2 Natural Language Processing
3.1.3 Computer Vision
3.1.4 Speech Recognition
3.1.5 Context-Aware Processing
3.1.6 Artificial Neural Network
3.1.7 Predictive APIs
3.1.8 Autonomous Robotics
3.2 AI Technology Readiness
3.2.1 Machine Learning APIs
3.2.2 IBM Watson API
3.2.3 Microsoft Azure Machine Learning API
3.2.4 Google Prediction API
3.2.5 Amazon Machine Learning API
3.2.6 BigML
3.2.7 AT&T Speech API
3.2.9 AlchemyAPI
3.2.10 Diffbot
3.2.11 PredictionIO
3.2.12 General Application Environment
3.3 AI Technology and Solution Integration
3.3.1 AI in Emotion Detection Solutions
3.3.2 AI in IoT Applications and Big Data Analytics
3.3.3 AI in Data Science and Predictive Analytics
3.3.4 AI in Edge Computing and 5G Network
3.3.5 AI in Cloud Computing and Machine Learning
3.3.6 AI in Smart Machines and Digital Twin Technologies
3.3.7 AI in Factory Automation and Industry 4.0
3.3.8 AI in Building Automation and the Smart Workplace
3.3.9 AI in Cloud Robotics and Public Security
3.3.10 AI in Self-Driven Networks
3.3.11 AI in Predictive 3D Design
3.4 AI Application Delivery Platforms and Business Models
3.4.1 The Role of AI Software
3.4.2 AI and Machine Learning as a Service
3.5 Enterprise Adoption and AI Investment
3.5.1 Market Leaders in AI Funding and Initiatives
3.5.2 Enterprise AI Drive Productivity Gains
3.6 AI Applications in Industry Verticals
3.6.1 Leading Industry Verticals in AI Solution Implementation
3.6.2 AI Use Cases by Company and Solution

4.0 AI Ecosystem Analysis
4.1 NVidia Corporation
4.2 IBM Corporation
4.3 Intel Corporation
4.4 Samsung Electronics Co Ltd.
4.5 Microsoft Corporation
4.6 Google Inc.
4.7 Baidu Inc.
4.8 Qualcomm Incorporated
4.9 Huawei Technologies Co. Ltd.
4.10 Fujitsu Ltd.
4.12 Juniper Networks, Inc.
4.13 Nokia Corporation
4.14 ARM Limited
4.15 Hewlett Packard Enterprise
4.16 Oracle Corporation
4.17 SAP
4.18 Siemens AG
4.19 Apple Inc.
4.20 General Electric
4.21 ABB Ltd.
4.22 LG Electronics
4.23 Koninklijke Philips N.V
4.24 Whirlpool Corporation
4.25 AB Electrolux
4.26 Wind River Systems Inc.
4.27 Cumulocity GmBH
4.28 Digital Reasoning Systems Inc.
4.29 SparkCognition Inc.
4.30 KUKA AG
4.31 Rethink Robotics
4.32 Motion Controls Robotics Inc.
4.33 Panasonic Corporation
4.34 Haier Group Corporation
4.35 Miele
4.36 Next IT Corporation
4.37 Nuance Communications Inc.
4.38 InteliWISE
4.39 Facebook Inc.
4.40 Salesforce
4.41 Amazon Inc.
4.42 SK Telecom
4.44 Buddy
4.45 AOL Inc.
4.46 Tesla Inc.
4.47 Inbenta Technologies Inc.
4.48 Cisco Systems
4.49 MAANA
4.50 Veros Systems Inc.
4.51 PointGrab Ltd.
4.52 Tellmeplus
4.53 Xiaomi Technology Co. Ltd.
4.54 Leap Motion Inc.
4.55 Atmel Corporation
4.56 Texas Instruments Inc.
4.57 Advanced Micro Devices Inc.
4.58 XILINX Inc.
4.59 Omron Adept Technology
4.60 Gemalto N.V.
4.61 Micron Technology
4.62 SAS Institute Inc.
4.63 AIBrian Inc.
4.64 QlikTech International AB
4.65 MicroStrategy Incorporated
4.66 Brighterion Inc.
4.67 IPsoft Inc.
4.68 24/ Inc.
4.69 General Vision Inc.
4.70 Sentient Technologies Holdings Limited
4.71 Graphcore
4.72 CloudMinds
4.73 Rockwell Automation Inc.
4.75 SoftBank Robotics Holding Corp.
4.76 iRobot Corp.
4.77 Lockheed Martin
4.78 Spacex
4.79 Fraight AI
4.80 Infor Global Solutions
4.81 Presenso
4.82 Teknowlogi

5.0 Market Analysis and Forecasts 2021 - 2026
5.1 AI Market
5.2 AI Market by Segment
5.2.1 Hardware Embedded Devices Embedded IoT Systems Semiconductor Components
5.2.2 Software Software Category AI Platforms
5.2.3 Services Professional Services
5.3 AI Market by Management Functions
5.4 AI Market by Technology
5.4.1 AI Technology by Major Solution Type
5.4.2 Machine Learning by Solution Type
5.5 AI Market by Industry Vertical
5.5.1 Medical and Healthcare
5.5.2 Manufacturing
5.5.3 Consumer Electronics
5.5.4 Automotive and Transportation
5.5.5 Retail and Apparel
5.5.6 Marketing and Advertising
5.5.7 FinTech
5.5.8 Building and Construction
5.5.9 Agriculture
5.5.10 Security and Surveillance
5.5.11 Government, Military, and Aerospace
5.5.12 Human Resource
5.5.13 Legal and Law
5.5.14 Telecommunication and IT
5.5.15 Oil, Gas, and Mining
5.5.16 Logistics
5.5.17 Education and Instruction
5.6 AI Market by Solution Type
5.7 AI Market by Deployment Method
5.7.1 AI Deployment Options
5.7.2 Private vs. Public Cloud Deployment
5.8 AI Market by AI System
5.9 AI Market by AI Type
5.10 AI Market by Connectivity
5.10.1 Non-Telecom Connectivity
5.10.2 Telecom Connectivity
5.10.3 Connectivity Standards
5.10.4 Enterprise
5.11 AI Market in IoT Networks
5.12 AI Market in IoT Edge Computing
5.13 AI Analytics Market
5.14 AI Market by Intent-Based Networking
5.15 AI Market in Virtualized Infrastructure
5.16 AI Market in 5G Networks
5.17 AI Market in Blockchain Networks
5.18 AI Market by Region
5.19 AI Embedded Unit Deployment Forecast 2021 - 2026
5.19.1 Overall AI Embedded Unit Deployment
5.19.2 AI Embedded Unit Deployment by Solution Non-IoT Devices IoT Devices IoT Things/Objects IoT Semiconductors Software
5.19.3 IoT Unit Deployment by Region North America Asia Pacific Europe Middle East and Africa Latin America

6.0 Conclusions and Recommendations

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