Industrial AI means something different depending on who's using the term, and this piece breaks down what's actually behind it.
The man who founded Uber has decided the next big thing is not an app. It is steel, concrete and machinery. Travis Kalanick now argues that mining, construction, heavy transport and food production are the industries waiting to be transformed, and that industrial AI is, in his words, getting ready to power the next industrial revolution.
Travis is not alone. Siemens has built an entire event brand around the phrase. Hannover Messe ran its 2026 edition on the theme. South Korea launched national industrial AI projects in June 2026. Aramco uses the term about itself. In one month this summer, industrial AI appeared in more than 2,500 online articles.
So it is worth asking what people actually mean by it.
What is Industrial AI?
Industrial AI is artificial intelligence applied to physical operations rather than digital ones. Not writing emails or summarizing documents, but running plants, predicting failures, optimizing yield and deciding when a piece of equipment needs attention.
The difference is consequence. When a chatbot is wrong you get an awkward sentence. When an industrial system is wrong you get a shutdown, a write-off or a safety incident. That single fact shapes everything about how these systems have to be built, and it is why almost none of them are pure machine learning.
How companies are using it
Siemens uses industrial AI to mean scale and connection. It attaches the phrase to CNC controls, engineering agents and shipbuilding partnerships, on top of an installed base it puts at more than 35 million connected devices. The proposition is one intelligence layer across hardware, software and connectivity rather than a shelf of separate products.
AspenTech uses it to mean better models. Aspen Hybrid Models blend live plant data with machine learning and first principles simulation. Aramco deployed them across its refinery planning in April and reported yield and quality prediction accuracy of up to 98.5 percent in key units. Emerson followed in May with AVA, an agentic platform built on the same domain models.
Kalanick uses it to mean robots. His version is software, sensors, robotics and AI automating the operations of entire sectors, which is a story about replacing physical labor rather than understanding physical assets.
Akselos and Industrial AI
Akselos builds industrial AI for the structures that carry energy infrastructure: hulls, steam methane reformers, pressure vessels, hydrotreaters, coker drums, ORVs.
The architecture is hybrid by design. Machine learning reconstructs the loads and operating conditions that were never measured directly. A physics solver then computes what those conditions do to the steel. Machine learning extrapolates from a history the asset has not lived through yet. Physics by itself has always been too slow to run against live plant data.
The solver is RB-FEA, reduced basis finite element analysis, licensed exclusively from MIT. It runs full-scale structural models roughly 1,000 times faster than conventional finite element analysis, just as accurate, which turns structural integrity from a study you commission into a question you can ask while the plant is running. This is the physics-based AI underneath Structural Performance Management, SPM: the system that takes that live structural picture and turns it into decisions on throughput, capital timing and asset life.
On Shell’s Bonga FPSO, that means a model covering more than 15,000 fatigue measurement locations, which pinpointed 230 critical fatigue hotspots, validated by Lloyd’s Register.
Kalanick is right that the next industrial revolution will be about atoms. He is looking at the machinery that moves things. We are looking at the structures that hold them up. As our CEO Thomas Leurent puts it, the cheapest power plant is the one you already own. Industrial AI is how you find out how much of it you have left.
Oz shapes the category of AI for Structural Integrity and works with industry leaders to scale adoption across complex industrial environments. He will be available for strategic conversations on predictive performance, category leadership, and how SPM accelerates industrial AI programs.