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Agentic AI is rapidly becoming one of the most discussed innovations in industrial software. The promise is compelling: AI systems that don’t just analyze data, but actively reason, coordinate, and act across workflows. But as excitement grows, so do the questions. In industrial environments, adding more AI agents does not automatically reduce complexity—in many cases, organizations worry it could increase it.

Masaharu Hatano deeply understands the experience of 50 million people around the world. As director of research at Japan’s Senior Dementia Institute, he has a ringside view of how the disease affects the lives of patients and their families, and its sudden episodes of anxiety, wandering, delusions, even aggression. Determined to make a difference, he and his team tried a new approach.

Calcamite designs, manufactures, operates and maintains small-scale, modular wastewater treatment plants for remote communities and commercial sites across Africa.

Posted: September 28, 2026 Many industrial organizations know their current process simulation approach is holding them back. Models are hard to re-use. Engineering data gets duplicated across tools. Validation is time-consuming. And each new project feels like it starts from scratch. Yet even when the case for modernization is clear, change can stall for one reason: risk.

For upstream companies, the cost of unplanned downtime has always added up — research from Senseye Predictive Maintenance shows that the average oil and gas producer loses up to $128 million per year.[1] But in today's environment, that math has fundamentally changed.

Posted: September 24, 2026 Researchers at the National Laboratory of the Rockies (NRL) are developing eGridGPT, a generative AI assistant designed to support grid operators. Read on for: Using AI to improve grid reliability and lower costs at the U.S. Dept.

For the last several years, the but the “trilemma”—complex as it may be—can be solved. In chemicals, the fastest lever is execution: fewer upsets, less wasted energy, and less time lost between an alarm and a confident next step. AI becomes valuable when it shortens that gap—by pulling the relevant history, constraints, and documentation into a usable narrative teams can act on. Methanex, the world’s largest producer and supplier of methanol, is one chemicals company leading the way.

Posted: September 11, 2026 Missed the earlier installments? Read blog 1 and blog 2 Key takeaways from the 2026 AVEVA Utility Executive Summit Part three in our series exploring the capabilities utility leaders say are essential for modern operations By Ann Moore Artificial intelligence was one of the most discussed topics at the 2026 AVEVA Utility Executive Summit. Leaders see significant potential for AI to improve planning, operations, asset management, and decision-making.

Posted: September 09, 2026 The University of Iowa and the city of Muscatine are building a cloud-based machine learning platform to turn unpredictable waste streams into pipeline-quality renewable natural gas.

Posted: September 09, 2026 TechnoEconomic Analysis (TEA) is the combination of technical feasibility study and economic sensitivity analysis of a given process. Traditionally, those two steps are executed sequentially and typically involve iterations. As a common trend in our industry, integration can drive productivity gains that reduce time to decision and enable teams to explore opportunities more thoroughly through automation.