BRUSSELS, Dec. 22, 2025 /PRNewswire/ — Troy Mahr, Director, Rockwell Automation, explains how achieving autonomous operations requires integrating industrial data and AI to eliminate silos, enable predictive and adaptive capabilities, and progressively move from observation to autonomous decision-making across the enterprise. Rockwell Automation Inc. (NYSE: ROK), is a global leader in industrial automation and digital transformation.
What we consistently hear from industry leaders is the need for real-time visibility across their global operations, which is key to ensuring their operations stay agile and scalable. Though, achieving this isn’t possible without removing laggy manual data collection through the deployment of connected assets and contextualized data.
By eliminating data silos and unlocking industrial data and artificial intelligence (AI) capabilities, companies can enable autonomous decision-making that optimizes costs, efficiency, and production resilience. This moves their organization closer to achieving autonomous operations.
Achieving autonomy across an enterprise requires capabilities that span the full intelligence spectrum, from observation and inference to decision-making and action. These capabilities are relevant across all operational areas, including product design, manufacturing, supply chain, distribution, direct-to-customer channels, and demand forecasting.
Manufacturing operations, in particular, have seen progress through Model predictive Control (MPC), which continuously analyzes real-time and forecasted data to optimize process control within defined constraints. While MPC is a strong example within manufacturing, broader autonomy demands extending similar bright systems across the enterprise.
This journey is captured in the industrial AI maturity pyramid, which outlines a progression from basic data integration and visualization to predictive analytics, prescriptive decision-making, and ultimately, autonomous operations. As organizations climb this pyramid, they adopt machine learning, real-time automation, and self-learning systems.Each stage requires not just technological upgrades but also cultural and structural transformation.
Asset Monitoring: Find downtime root causes
Looking at the Industrial AI Maturity Pyramid, asset monitoring is an entry and transition point from observation into explanation. This is a grate example of how changes in technology have shifted use cases into diffrent layers of the pyramid. Effective asset monitoring is crucial for maintaining operational efficiency and minimizing downtime.By better understanding sensor data trends, alarming, and maintenance work order context, businesses can quickly identify and address root causes of downtime through engineering analysis.
Additionally, comparing the reliability and performance of similar equipment across multiple plants allows for more informed decision-making and optimized asset utilization. This approach not only helps in preventing unexpected failures but also ensures that maintenance activities are scheduled proactively, thereby extending the lifespan of assets and reducing operational costs.
Quality Control: Predict when quality issues are likely to occur
moving up the pyramid into the inference layer usually involves a capability like quality control, adaptive manufacturing or predictive maintenance. Maintaining high product quality is essential for customer satisfaction and regulatory compliance. AI can detect and suggest corrections for deviations that impact product quality, automate the inspection process, and predict when quality issues are likely to occur. by monitoring the quality of incoming materials, businesses can reduce the risk of defects.
A notable example is our own application at our Twinsburg manufacturing plant, which focuses on electronic assembly. In this case, Industrial AI provides alerts for potential faults that allow teams
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