AIoT Solutions Improve Operational Effectiveness and the Value of Machine Data by up to 29% by 2028
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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.
- AI adds value to IoT through machine learning and improved decision-making
- IoT adds value to AI through connectivity, signaling, and data exchange
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.
With the purchase of this report at the Multi-user License or greater level, you will have access to one hour with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This will need to be used within three months of purchase.
This report also includes a complimentary Excel file with data from the report for purchasers at the Site License or greater level.
Table of Contents
Companies Mentioned
- Sharp
- SAS
- DT42
- Chania Tech Giants: Baidu, Alibaba, and Tencent
- 5.4.1 Baidu
- 5.4.2 Alibaba
- 5.4.3 Tencent
- Xiaomi Technology
- NVidia
- Intel Corporation
- Qualcomm
- Innodisk
- Gopher Protocol
- Micron Technology
- ShiftPixy
- Uptake
- C3 IoT
- Alluvium
- Arundo Analytics
- Canvass Analytics
- Falkonry
- Interactor
- Cisco
- IBM Corp.
- Microsoft Corp.
- Apple Inc.
- Salesforce Inc.
- Infineon Technologies AG
- Amazon Inc.
- AB Electrolux
- ABB Ltd.
- AIBrian Inc.
- Analog Devices
- ARM Limited
- Atmel Corporation
- Ayla Networks Inc.
- Brighterion Inc.
- Buddy
- CloudMinds
- Cumulocity GmBH
- Cypress Semiconductor Corp
- Digital Reasoning Systems Inc.
- Echelon Corporation
- Enea AB
- Express Logic Inc.
- Facebook Inc.
- Fujitsu Ltd.
- Gemalto N.V.
- General Electric
- General Vision Inc.
- Graphcore
- H2O.ai
- Haier Group Corporation
- Helium Systems
- Hewlett Packard Enterprise
- Huawei Technologies
- Siemens AG
- SK Telecom
- SoftBank Robotics
- SpaceX
- SparkCognition
- STMicroelectronics
- Symantec Corporation
- Tellmeplus
- Tend.ai
- Tesla
- Texas Instruments
- Thethings.io
- Veros Systems
- Whirlpool Corporation
- Wind River Systems
- Juniper Networks
- Nokia Corporation
- Oracle Corporation
- PTC Corporation
- Losant IoT
- Robert Bosch GmbH
- Pepper
- Terminus
- Tuya Smart
Methodology
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