DUBLIN--(BUSINESS WIRE)--The "AI Market by Technology Type, Deployment Method, Solution Type, Integration (Technologies, Networks, and Devices) and Industry Verticals 2023 - 2028" 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 2023 through 2028.
Select Report Findings:
- Total global AI solution market will reach $301.2 billion by 2028, growing at 29.4% CAGR
- Global unsupervised machine learning market will reach $15.6 billion by 2028, growing at 25.1% 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 2028
- 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 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 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 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 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 policy making and strategy at the highest levels of enterprises as well as governmental organizations.
Key Topics Covered:
1.0 Executive Summary
2.0 Introduction
3.0 Technology and Application Analysis
3.1 AI Technology Matrix
3.1.1 Machine Learning
3.1.1.1 Deep Learning
3.1.1.2 Supervised vs. Unsupervised Learning
3.1.1.3 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
3.2.8 Wit.ai
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
3.3.1.1 Facial Detection APIs
3.3.1.2 Text Recognition APIs
3.3.1.3 Speech Recognition APIs
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
5.0 Market Analysis and Forecasts 2023 - 2028
5.1 AI Market
5.2 AI Market by Segment
5.2.1 Hardware
5.2.1.1 Embedded Devices
5.2.1.2 Embedded IoT Systems
5.2.1.3 Semiconductor Components
5.2.2 Software
5.2.3 Services
5.3 AI Market by Management Functions
5.4 AI Market by Technology
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
5.8 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
5.18.1 North America AI Market by Country
5.18.2 APAC AI Market by Country
5.18.3 Europe AI Market by Country
5.18.4 MEA AI Market by Country
5.18.5 Latin America AI Market by Country
5.19 AI Embedded Unit Deployment Forecast 2023 - 2028
5.19.1 Overall AI Embedded Unit Deployment
5.19.2 AI Embedded Unit Deployment by Solution
5.19.2.1 Non-IoT Devices
5.19.2.2 IoT Devices
5.19.2.3 IoT Things/Objects
5.19.2.4 IoT Semiconductors
5.19.2.5 Software
5.19.3 IoT Unit Deployment by Region
6.0 Conclusions and Recommendations
Companies Mentioned
- 24/7.ai Inc.
- AB Electrolux
- ABB Ltd.
- Advanced Micro Devices Inc.
- AIBrian Inc.
- Amazon Inc.
- AOL Inc.
- Apple Inc.
- ARM Limited
- Atmel Corporation
- Baidu Inc.
- Brighterion Inc.
- Buddy
- Cisco Systems
- CloudMinds
- Cumulocity GmBH
- Digital Reasoning Systems Inc.
- Facebook Inc.
- Fraight AI
- Fujitsu Ltd.
- Gemalto N.V.
- General Electric
- General Vision Inc.
- Google Inc.
- Graphcore
- H2O.ai
- Haier Group Corporation
- Hewlett Packard Enterprise
- Huawei Technologies Co. Ltd.
- IBM Corporation
- Inbenta Technologies Inc.
- Infor Global Solutions
- Intel Corporation
- InteliWISE
- IPsoft Inc.
- iRobot Corp.
- Juniper Networks, Inc.
- Koninklijke Philips N.V
- KUKA AG
- Leap Motion Inc.
- LG Electronics
- Lockheed Martin
- MAANA
- Micron Technology
- Microsoft Corporation
- MicroStrategy Incorporated
- Miele
- Motion Controls Robotics Inc.
- motion.ai
- Next IT Corporation
- Nokia Corporation
- Nuance Communications Inc.
- NVidia Corporation
- Omron Adept Technology
- Oracle Corporation
- Panasonic Corporation
- PointGrab Ltd.
- Presenso
- QlikTech International AB
- Qualcomm Incorporated
- Rethink Robotics
- Rockwell Automation Inc.
- Salesforce
- Samsung Electronics Co Ltd.
- SAP
- SAS Institute Inc.
- Sentient Technologies Holdings Limited
- Siemens AG
- SK Telecom
- SoftBank Robotics Holding Corp.
- Spacex
- SparkCognition Inc.
- Teknowlogi
- Tellmeplus
- Tend.ai
- Tesla Inc.
- Texas Instruments Inc.
- Veros Systems Inc.
- Whirlpool Corporation
- Wind River Systems Inc.
- Xiaomi Technology Co. Ltd.
- XILINX Inc.
For more information about this report visit https://www.researchandmarkets.com/r/1cjc8t-market-by?w=4
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