Industrial AI in manufacturing to hit USD $153.9 billion by 2030
The global industrial artificial intelligence (AI) market is projected to increase to USD $153.9 billion by 2030, from its value of USD $43.6 billion in 2024, reflecting a 23% compound annual growth rate according to new analysis from IoT Analytics.
Findings from the Industrial AI Market Report 2025–2030 show that although spending on industrial AI currently accounts for just 0.1% of manufacturing sector revenue, there has been a notable shift in strategic focus areas, including industrial data management, quality inspection, edge AI, industrial copilots, employee training and upskilling, and initial trials involving agentic AI.
Strategic shift
Analysis by IoT Analytics indicates that AI is now moving beyond pilot phases and isolated projects within manufacturing. The adoption is becoming a CEO-level priority, with a greater share of resources being allocated to modernising data architectures and scaling use cases previously proven in areas such as quality inspection.
Knud Lasse Lueth, CEO at IoT Analytics, comments that "Industrial AI reached $43.6 billion in 2024 and is set to surpass $150 billion by 2030. While spending remains below 10% of manufacturing R&D or IT budgets, it is increasingly driven by CEO-level strategies. The focus has shifted to modernizing data architectures and scaling proven use cases such as quality inspection. Following the wave of industrial copilots launched in 2024 and 2025, edge AI and agentic AI are now emerging as the next areas of attention going into 2026."
IoT Analytics' report outlines that manufacturers are adjusting their approaches after early AI trials in previous years often produced disappointment due to unclear business cases or insufficient value creation. The report highlights a sea change as end-user awareness and AI education improve across the sector.
Technological drivers
According to the research, key technological drivers now include high-performance data architectures, the expansion of edge AI solutions, and the deployment of AI "copilots" to support operational decisions and workflows on the manufacturing floor. Manufacturers are also focusing on workforce training to ensure employees can adapt to and manage new AI-enabled solutions.
Fernando Brügge, Senior Analyst at IoT Analytics, adds that "Industrial AI is finally taking off. Today, we see AI becoming a central part of manufacturers' strategies. Back in 2021, this was far from the case: many companies struggled to identify a business case or derive real value from early deployments, and a good number were left disappointed when promised outcomes did not materialize. Now, we are in a very different position: the infrastructure is in place, end-user awareness and education have improved, and the technology is beginning to deliver tangible benefits. These factors are driving manufacturers to develop their own industrial AI strategies. In the years ahead, industrial AI will not just support operations but increasingly shape how machines are designed, how supply chains are managed, and how factories compete."
The IoT Analytics report details how the quality inspection segment is already seeing AI-driven enhancements, with computer vision and machine learning being used for defect detection, predictive maintenance, and optimising production processes. The emergence of edge AI – AI processing located on or near the physical equipment – is also highlighted as a next step for manufacturers seeking to reduce data latency and increase operational autonomy.
Next areas of attention
Expert commentary in the report signals that following the adoption of industrial copilots in 2024 and 2025, attention is now shifting to the potential of agentic AI. This approach enables autonomous agents, supported by advanced machine learning, to perform decision-making tasks and optimise industrial processes without direct human intervention.
The report emphasises the relatively modest proportion of current manufacturing budgets dedicated to AI, but points to evidence of increasing investment driven by strategic objectives at the top levels of industrial organisations.
IoT Analytics' findings also suggest that the maturation of AI use in manufacturing is a result of both infrastructure readiness and improved education, making it possible for manufacturing firms to extract measurable benefits from their AI deployments.
The report and the accompanying public analysis provide further detail on ten key insights regarding AI's transformation of the manufacturing industry and can be accessed through IoT Analytics by interested parties.
