Global AIoT Transformation Market: Hyundai, Samsung, and Other Leading Companies Anticipate 29% Increase in Machine Data Value by 2028

DUBLIN, July 19, 2023 /PRNewswire/ -- The "Artificial Intelligence of Things Solutions by AIoT Market Applications and Services in and Industry Verticals 2023 - 2028" report has been added to ResearchAndMarkets.com's offering.

This AIoT market report provides an analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2023 through 2028.

The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.

Select Report Findings:

• The global AIoT market will reach $91.2 billion by 2028, growing at 40.6% CAGR
• The global market for IoT data as service solutions will reach $9.8B USD by 2028
• The AI-enabled edge device market will be the fastest-growing segment within the AIoT
• AIoT automates data processing systems, converting raw IoT data into useful information
• Today's AIoT solutions are the precursor to next-generation AI Decision as a Service (AIDaaS)
• AIoT solutions improve operational effectiveness and the value of machine data by up to 29% by 2028

While it is no secret that AI is rapidly becoming integrated into many aspects of ICT, many do not understand the full extent of how it will transform communications, applications, content, and commerce. For example, the use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective smart city solutions in terms of decision-making.

The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to "thinking" networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals.

The goal of AIoT is to leverage AI techniques such as machine learning, deep learning, and data analytics to process and analyze the vast amounts of data generated by IoT devices. By applying AI algorithms to IoT data, AIoT aims to extract meaningful insights, detect patterns, and enable autonomous actions or intelligent responses.

AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into an array of infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.

While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.

Six key areas that we see within the scope of AIoT solutions are: Data Services, Asset Management, Immersive Applications, Process Improvement, Next-Gen UI and UX, and Industrial Automation.

These benefits will be manifest in the following areas:

• Efficient IoT Operations: AIoT can optimize and automate various aspects of IoT operations, such as device management, resource allocation, and network optimization. AI algorithms can help in predicting device failures, optimizing energy usage, and improving overall efficiency.
• Improved Human-Machine Interactions: By integrating AI capabilities into IoT devices, AIoT can enhance human-machine interactions. This includes voice recognition, natural language processing, computer vision, and contextual understanding, making interactions more intuitive and seamless.
• Enhanced Data Management and Analytics: AIoT can improve data management and analytics by utilizing AI algorithms to process and analyze IoT data in real time. This enables faster and more accurate decision-making, anomaly detection, predictive maintenance, and personalized services.
• Intelligent Automation and Adaptability: AIoT can enable autonomous decision-making and adaptive behaviors in IoT systems. This involves leveraging AI algorithms to enable devices and systems to learn, adapt, and make intelligent decisions based on real-time data and changing conditions.

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service systems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery, and support models.

We see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, and SaaS-managed service offerings. Recent years have witnessed rapid growth for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy service industries will lead the way.

As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine-generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT-supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.

The use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective decision-making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real time will add an entirely new dimension to service logic.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.

The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $5.9B by 2028. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. We see machine learning in edge computing as the key to realizing the full potential of IoT analytics.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

3.0 Technology and Application Analysis
3.1 AIoT Market Analysis
3.2 AIoT Sub-Market Analysis
3.3 AIoT Technology Analysis
3.4 AIoT Enabling Technology Analysis
3.5 AIoT Applications Analysis

4.0 Company Analysis
4.1 Sharp Corporation
4.2 SAS Institute Inc.
4.3 DT42 Co. Ltd.
4.4 Baidu Inc.
4.5 Alibaba Group Holding Limited
4.6 Tencent
4.7 Xiaomi
4.8 NVIDIA Corporation
4.9 Intel Corporation
4.10 Qualcomm Technologies Inc.
4.11 Innodisk Corporation
4.12 GBT Technologies
4.13 Micron Technology Inc.
4.14 ShiftPixy
4.15 Uptake Technologies Inc.
4.16 C3 AI Inc.
4.17 Alluvium IoT Solutions Pvt Ltd.
4.18 Arundo (Stanford Startx Company)
4.19 Canvass Analytics Inc.
4.20 Falkonry Inc.
4.21 Interactor
4.22 Google (DeepMind)
4.23 Cisco Systems
4.24 IBM Corporation
4.25 Microsoft Corporation
4.26 Apple Inc.
4.27 Salesforce Inc.
4.28 Infineon Technologies AG (Cypress Semiconductor)
4.29 Amazon Inc.
4.30 AB Electrolux
4.31 ABB Ltd.
4.32 AIBrian Inc.
4.33 Analog Devices Inc.
4.34 ARM Limited
4.35 Atmel Corporation (Microchip Technology)
4.36 Ayla Networks Inc.
4.37 Brighterion Inc.
4.38 Buddy (Blue Frog Robotics)
4.39 CloudMinds
4.40 Cumulocity IoT (Software AG)
4.41 Smarsh Inc. (Digital Reasoning Systems)
4.42 Enea AB
4.43 Express Logic Inc. (Microsoft Corporation)
4.44 Meta Platform Inc. (Facebook)
4.45 Fujitsu Ltd.
4.46 Thales Group (Gemalto N.V.)
4.47 General Electric (GE)
4.48 General Vision Services (GVS)
4.49 Graphcore
4.50 H2O.ai
4.51 Haier Group
4.52 Helium Systems
4.53 Hewlett Packard Enterprise (HPE)
4.54 Huawei Technologies
4.55 Siemens AG
4.56 SK Telecom
4.57 SoftBank Robotics
4.58 SpaceX
4.59 SparkCognition
4.60 STMicroelectronics
4.61 Broadcom Inc. (Symantec)
4.62 Tellmeplus (OVHCloud)
4.63 Tesla Inc.
4.64 Texas Instruments
4.65 Thethings.io
4.66 Veros Systems (Baker Hughes Company)
4.67 Whirlpool Corporation
4.68 Wind River Systems Inc.
4.69 Juniper Networks Inc.
4.70 Nokia Corporation
4.71 Oracle Corporation
4.72 PTC Corporation (ServiceMax)
4.73 Losant IoT
4.74 Robert Bosch GmbH
4.75 Pepper
4.76 Terminus Group
4.77 Tuya Inc.
4.78 NXP Semiconductors (Freescale Semiconductor)
4.79 Axiomtek Co. Ltd.
4.80 Pinnacle Solutions Inc.
4.81 Schneider Electric
4.82 TCL Technology
4.83 GREE Electric Appliances Inc.
4.84 Hisense International
4.85 Lenovo
4.86 Midea

5.0 Market Analysis and Forecasts 2023 - 2028
5.1 AIoT Market 2023 - 2028
5.2 Regional AIoT Market 2023 - 2028
5.3 AIoT Deployment Unit 2023 - 2028
5.4 Regional AIoT Deployment Unit 2023 - 2028

6.0 Conclusions and Recommendations

7.0 Appendix: General Purpose AI

For more information about this report visit https://www.researchandmarkets.com/r/ke7s87

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