Sustainability

What is consistently heard 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. However, 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. 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 intelligent 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.