AIoT 솔루션 : 용도, 서비스, 업계별(2026-2030년)

Overview:

Agentic AI represents a major shift from pre-programmed automation to autonomous, goal-directed reasoning. By integrating with the Internet of Things (IoT), these AI agents move beyond simple "if-this-then-that" rules to deliver Decisions as a Service (DaaS), fundamentally transforming how industries operate.

The Shift from Rules to Autonomous Decisions

Traditional IoT relies on static thresholds, whereas Agentic IoT interprets context to make complex choices independently.

• Pre-programmed Automation: Executes fixed commands when specific sensor data triggers a rule
• Agentic Automation: Evaluates ambient conditions, anticipates downstream consequences, and self-corrects to meet an objective
• Decisions as a Service: Offloads real-time operational choices to AI models that charge based on outcomes or API calls

Real-World Impact Across Key Sectors

Agentic AI converts passive data networks into self-managing physical systems.

• Predictive Maintenance: Agents detect anomalies, order replacement parts, and schedule human technicians without management intervention
• Smart Grid Management: Systems balance energy distribution, predict peak loads, and trade power autonomously on open markets
• Supply Chain Logistics: Vehicles reroute themselves based on weather patterns, port congestion, and real-time inventory levels

Core Challenges to Widespread Adoption

Deploying autonomous decision-makers into physical infrastructure introduces critical technical and operational hurdles.

• Latency Constraints: Cloud-reliant agents struggle with time-sensitive physical actions that require sub-millisecond edge computing
• Deterministic Safety: Industries require guaranteed, predictable behaviors, which inherently conflict with probabilistic AI reasoning
• Security Risks: Compromised agents can manipulate physical valves, locks, or heavy machinery, escalating digital risks to physical threats

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 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.

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 2026 through 2030.

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.

Table of Contents

1. Executive Summary

2. Introduction

• 2.1 Defining AIoT
  • 2.1.1 AI in IoT vs. AIoT
• 2.2 Artificial General Intelligence
  • 2.2.1 Ambient Intelligence and Smart Lifestyles
• 2.3 IoT Network and Functional Structure
  • 2.3.1 Economic and Social Impact
  • 2.3.2 Enterprise Adoption and Investment
• 2.4 AIoT Market Dynamic Analysis
  • 2.4.1 Market Drivers and Opportunities
  • 2.4.2 Market Restraints and Challenges
• 2.5 AIoT Value Chain Analysis
  • 2.5.1 Device Manufacturers
  • 2.5.2 Equipment Manufacturers
  • 2.5.3 Platform Providers
  • 2.5.4 Software and Service Providers
  • 2.5.5 User Communities

3. Technology and Application Analysis

• 3.1 AIoT Market Analysis
  • 3.1.1 Equipment and Component
  • 3.1.2 Cloud Equipment and Deployment
  • 3.1.3 3D Sensing Technology
  • 3.1.4 Software and Data Analytics
  • 3.1.5 AIoT Platforms
  • 3.1.6 Deployment and Services
• 3.2 AIoT Sub-Market Analysis
  • 3.2.1 Supporting Device and Connected Objects
  • 3.2.2 IoT Data as a Service
  • 3.2.3 AI Decisions as a Service
  • 3.2.4 APIs and Interoperability
  • 3.2.5 Smart Objects
  • 3.2.6 Smart City Considerations
  • 3.2.7 Industrial Transformation
  • 3.2.8 Cognitive Computing and Computer Vision
  • 3.2.9 Consumer Appliances
  • 3.2.10 Domain Specific Network Considerations
  • 3.2.11 3D Sensing Applications
  • 3.2.12 Predictive 3D Design
• 3.3 AIoT Technology Analysis
  • 3.3.1 Cognitive Computing
  • 3.3.2 Computer Vision
  • 3.3.3 Machine Learning Capabilities and APIs
    • 3.3.3.1 Deep Machine Learning
    • 3.3.3.2 Machine Learning APIs
  • 3.3.4 Neural Networks
  • 3.3.5 Context Aware Processing
• 3.4 AIoT Enabling Technology Analysis
  • 3.4.1 Edge Computing
    • 3.4.1.1 AIoT Edge Architecture
    • 3.4.1.2 Edge AI Platform
  • 3.4.2 Blockchain Networks
  • 3.4.3 Cloud Technologies
  • 3.4.4 5G Technologies
  • 3.4.5 Digital Twin Technology and Solutions
  • 3.4.6 Smart Machines
  • 3.4.7 Cloud Robotics
  • 3.4.8 Predictive Analytics and Real Time Processing
    • 3.4.8.1 All Flash Array
    • 3.4.8.2 Real Time Operating Systems (RTOS)
  • 3.4.9 Post Event Processing
  • 3.4.10 Haptic Technology
• 3.5 AIoT Applications Analysis
  • 3.5.1 Device Accessibility and Security
  • 3.5.2 Gesture Control and Facial Recognition
  • 3.5.3 Home Automation
  • 3.5.4 Wearable Device
  • 3.5.5 Fleet Management
  • 3.5.6 Intelligent Robots
  • 3.5.7 Augmented Reality Market
  • 3.5.8 Drone Traffic Monitoring
  • 3.5.9 Real-time Public Safety
  • 3.5.10 Yield Monitoring and Soil Monitoring Market
  • 3.5.11 HCM Operation

4. AIoT 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
• 4.49 Graphcore
• 4.50 H2O.ai
• 4.51 Haier Group
• 4.52 Helium Systems
• 4.53 Hewlett Packard Enterprise
• 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. AIoT Market Analysis and Forecasts 2026 – 2030

• 5.1 AIoT Market 2026 – 2030
  • 5.1.1 Global AIoT Market 2026 – 2030
  • 5.1.2 Global AIoT Market by Segment
    • 5.1.2.1 Global AIoT Market by Infrastructure Type
      • 5.1.2.1.1 Global AIoT Market by Chipset Type
        • 5.1.2.1.1.1 Global AIoT Market by 3D Sensing Technology
        • 5.1.2.1.1.2 Global AIoT Market by 3D Sensing Application
      • 5.1.2.1.2 Global AIoT Market by Cloud Infrastructure Type
    • 5.1.2.2 Global AIoT Market by Software and Platform Type
      • 5.1.2.2.1 Global AIoT Market by Analytics Software Type
    • 5.1.2.3 Global AIoT Market by Service Type
      • 5.1.2.3.1 Global AIoT Market by Professional Service Type
  • 5.1.3 Global AIoT Market by AI Technology
  • 5.1.4 Global AIoT Market by Application
  • 5.1.5 Global AIoT Market by IoT Sector
  • 5.1.6 Global AIoT Market by City vs. Rural Zone
  • 5.1.7 Global AIoT Market by Deployment
  • 5.1.8 Global AIoT Market by Marketing Channel
  • 5.1.9 Global AIoT Market by Enterprise Size
  • 5.1.10 Global AIoT Market by Industry Vertical
  • 5.1.11 Global Smart City Market in AIoT
  • 5.1.12 Global IoT Data as a Service Market in AIoT
  • 5.1.13 Global AI Decision as a Service Market in AIoT
  • 5.1.14 Global Blockchain Driven AIoT Market
  • 5.1.15 Global 5G Driven AIoT Market
  • 5.1.16 Global AIoT Market by Region
    • 5.1.16.1 North America AIoT Market by Country
    • 5.1.16.2 APAC AIoT Market by Country
    • 5.1.16.3 Europe AIoT Market by Country
    • 5.1.16.4 MEA AIoT Market by Country
    • 5.1.16.5 Latin America AIoT Market by Country
• 5.2 Regional AIoT Market 2026 – 2030
  • 5.2.1 North America AIoT Market by Infrastructure, Platform, Service, Technology, Application, Deployment, Marketing Channel, Industry Vertical, Smart City, IoT DaaS, AI Decision Service, Blockchain, and 5G
  • 5.2.2 APAC AIoT Market by Infrastructure, Platform, Service, Technology, Application, Deployment, Marketing Channel, Industry Vertical, Smart City, IoT DaaS, AI Decision Service, Blockchain, and 5G
  • 5.2.3 Europe AIoT Market by Infrastructure, Platform, Service, Technology, Application, Deployment, Marketing Channel, Industry Vertical, Smart City, IoT DaaS, AI Decision Service, Blockchain, and 5G
  • 5.2.4 MEA AIoT Market by Infrastructure, Platform, Service, Technology, Application, Deployment, Marketing Channel, Industry Vertical, Smart City, IoT DaaS, AI Decision Service, Blockchain, and 5G
  • 5.2.5 Latin America AIoT Market by Infrastructure, Platform, Service, Technology, Application, Deployment, Marketing Channel, Industry Vertical, Smart City, IoT DaaS, AI Decision Service, Blockchain, and 5G
• 5.3 AIoT Deployment Unit 2026 – 2030
  • 5.3.1 Global AIoT Deployment Unit 2026 – 2030
  • 5.3.2 Global AIoT Deployment Unit by Segment
    • 5.3.2.1 Global AIoT Deployment Unit by Infrastructure Type
      • 5.3.2.1.1 Global AIoT Deployment Unit by Chipset Type
      • 5.3.2.1.2 Global AIoT Deployment Unit by Cloud Infrastructure Type
    • 5.3.2.2 Global AIoT Deployment Unit by Software and Platform Type
  • 5.3.3 Global AIoT Deployment Unit by Region
    • 5.3.3.1 North America AIoT Deployment Unit by Country
    • 5.3.3.2 APAC AIoT Deployment Unit by Country
    • 5.3.3.3 Europe AIoT Deployment Unit by Country
    • 5.3.3.4 MEA AIoT Deployment Unit by Country
    • 5.3.3.5 Latin America AIoT Deployment Unit by Country
• 5.4 Regional AIoT Deployment Unit 2026 – 2030
  • 5.4.1 North America AIoT Deployment Unit by Infrastructure and Software Platform
  • 5.4.2 APAC AIoT Deployment Unit by Infrastructure and Software Platform
  • 5.4.3 Europe AIoT Deployment Unit by Infrastructure and Software Platform
  • 5.4.4 MEA AIoT Deployment Unit by Infrastructure and Software Platform
  • 5.4.5 Latin America AIoT Deployment Unit by Infrastructure and Software Platform