PUBLISHER: Global Industry Analysts, Inc. | PRODUCT CODE: 1757696
PUBLISHER: Global Industry Analysts, Inc. | PRODUCT CODE: 1757696
Global Edge Artificial Intelligence in Industrial Automation Market to Reach US$248.0 Billion by 2030
The global market for Edge Artificial Intelligence in Industrial Automation estimated at US$62.6 Billion in the year 2024, is expected to reach US$248.0 Billion by 2030, growing at a CAGR of 25.8% over the analysis period 2024-2030. Hardware, one of the segments analyzed in the report, is expected to record a 27.8% CAGR and reach US$173.2 Billion by the end of the analysis period. Growth in the Software segment is estimated at 21.5% CAGR over the analysis period.
The U.S. Market is Estimated at US$16.4 Billion While China is Forecast to Grow at 24.3% CAGR
The Edge Artificial Intelligence in Industrial Automation market in the U.S. is estimated at US$16.4 Billion in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$37.5 Billion by the year 2030 trailing a CAGR of 24.3% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 23.8% and 22.1% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 17.6% CAGR.
Global Edge Artificial Intelligence in Industrial Automation Market - Key Trends & Drivers Summarized
Why Is Edge Artificial Intelligence Redefining the Landscape of Industrial Automation?
Edge Artificial Intelligence (AI) is rapidly transforming industrial automation by bringing real-time, intelligent decision-making directly to the source of data generation-on the factory floor, in robotics systems, and embedded within machinery. Unlike traditional AI, which relies on cloud-based data processing that introduces latency and bandwidth concerns, edge AI enables low-latency, autonomous operations by analyzing data locally at the edge of the network. This capability is crucial in high-stakes environments like manufacturing, logistics, energy, and automotive sectors, where milliseconds can determine safety, quality, and productivity outcomes. With edge AI, industrial systems can detect anomalies, perform predictive maintenance, manage energy consumption, and even optimize production workflows in real time without reliance on centralized cloud infrastructure. As factories evolve into smart, interconnected environments under the Industry 4.0 paradigm, edge AI supports scalable, adaptive systems that can operate independently, even under disconnected or low-bandwidth conditions. The ability to process data securely on-site also addresses critical concerns around data privacy, intellectual property, and compliance. As industrial environments become more complex and data-intensive, edge AI offers a streamlined, responsive solution for next-generation automation-enabling machines not just to operate, but to think, learn, and adapt at the point of action.
How Are Technological Advancements Powering the Adoption of Edge AI in Industrial Automation?
The integration of edge AI in industrial automation is being fueled by significant technological advances across hardware, software, and network infrastructure. High-performance, energy-efficient processors such as AI-optimized GPUs, FPGAs, and dedicated edge AI chips now enable complex neural network computations to be performed on compact edge devices. These processors support advanced functions like real-time image recognition, speech processing, and sensor fusion directly on production equipment, enabling faster and more context-aware decision-making. Sophisticated AI frameworks and machine learning libraries are now optimized for edge deployment, reducing the gap between model development and deployment. Moreover, the rise of 5G and private LTE networks is facilitating ultra-reliable, low-latency communication between edge devices and central systems, creating a seamless feedback loop for process optimization. Advances in embedded systems, industrial IoT (IIoT) sensors, and data compression algorithms are also improving the collection, processing, and transmission of machine-generated data, making AI models more accurate and robust. Cloud-edge hybrid architectures allow for continuous learning-where data is processed at the edge for immediate response and in the cloud for long-term model improvement and analytics. Together, these technologies are making edge AI not only feasible but essential, enabling manufacturers to deploy smarter automation systems that reduce downtime, increase output quality, and enhance workplace safety.
Why Do Industry-Specific Needs and Regulatory Environments Influence Edge AI Deployment Strategies?
The implementation of edge AI in industrial automation is highly influenced by sector-specific requirements, regulatory constraints, and operational priorities. In industries like automotive and aerospace manufacturing, precision and process traceability are paramount; edge AI enables real-time quality control through vision systems that can inspect hundreds of parts per second and flag deviations before they propagate. In the energy sector, edge AI supports predictive maintenance and fault detection in remote locations such as wind farms or oil rigs where connectivity is limited, and downtime is costly. Food and beverage manufacturers use edge AI to monitor hygiene compliance and product consistency in high-speed production lines. In pharmaceutical manufacturing, where regulatory compliance is tightly monitored, edge AI provides verifiable, on-site data processing that ensures both operational integrity and auditability. Regional data sovereignty laws, particularly in the European Union under GDPR, also necessitate edge computing solutions that keep sensitive industrial data within local confines. Safety standards in sectors like mining, chemicals, and utilities further dictate that AI-powered safety systems operate independently of cloud connectivity to ensure continuous protection. These diverse needs require highly customizable edge AI solutions that are not only tailored to technical requirements but also compliant with local laws and industry protocols. As a result, vendors and industrial firms must adopt flexible, modular strategies to ensure successful, scalable deployments across global operations.
What Are the Key Drivers Fueling Growth in the Edge AI for Industrial Automation Market?
The growth in the edge AI for industrial automation market is being driven by a convergence of macroeconomic, operational, and technological trends reshaping the global industrial landscape. A major driver is the rising demand for real-time intelligence and autonomous decision-making on the factory floor, where even slight inefficiencies can translate to significant losses in revenue and productivity. The acceleration of digital transformation across industrial sectors-fueled by global competition, supply chain disruptions, and shifting labor dynamics-is prompting organizations to invest in smart automation technologies that offer resilience, flexibility, and insight. The increasing volume of machine-generated data and the need to analyze it in real-time for safety, compliance, and optimization purposes make edge AI a natural fit. Furthermore, the proliferation of smart sensors and IIoT devices has created an ecosystem ripe for decentralized intelligence, reducing the need for cloud dependency and lowering latency. Government incentives and Industry 4.0 initiatives across major economies-such as Germany’s “Industrie 4.0,” China’s “Made in China 2025,” and the U.S. push for domestic manufacturing-are injecting capital and policy support into edge AI adoption. Additionally, advances in cybersecurity are addressing concerns around device-level data protection, making edge deployments more viable for mission-critical operations. Together, these drivers are transforming edge AI from an emerging technology into a foundational pillar of modern industrial automation, unlocking new efficiencies, capabilities, and competitive advantages across sectors.
SCOPE OF STUDY:
The report analyzes the Edge Artificial Intelligence in Industrial Automation market in terms of units by the following Segments, and Geographic Regions/Countries:
Segments:
Component (Hardware, Software, Services); Solutions (Predictive Maintenance, Quality Control & Inspection, Process Optimization, Anomaly Detection, Energy Efficiency Management, Other Solutions); Deployment (On-Premise, Cloud); End-Use (Manufacturing, Energy & Utilities, Oil & Gas, Automotive, Logistics & Transportation, Healthcare, Other End-Uses)
Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.
Select Competitors (Total 36 Featured) -
AI INTEGRATIONS
We're transforming market and competitive intelligence with validated expert content and AI tools.
Instead of following the general norm of querying LLMs and Industry-specific SLMs, we built repositories of content curated from domain experts worldwide including video transcripts, blogs, search engines research, and massive amounts of enterprise, product/service, and market data.
TARIFF IMPACT FACTOR
Our new release incorporates impact of tariffs on geographical markets as we predict a shift in competitiveness of companies based on HQ country, manufacturing base, exports and imports (finished goods and OEM). This intricate and multifaceted market reality will impact competitors by increasing the Cost of Goods Sold (COGS), reducing profitability, reconfiguring supply chains, amongst other micro and macro market dynamics.