The manufacturing world is experiencing a major shift this week as companies embrace agentic AI systems that can think independently and make smart decisions without constant human guidance. At the technology conference CES 2026, thousands of business leaders gathered to see how artificial intelligence is transforming factories, construction sites, and industrial operations. This represents a move from simple automation toward intelligent transformation, where machines can understand situations and adapt their own behavior.

One of the most exciting developments shown at CES 2026 was physical AI, which means AI technology built into robots and machines that can perform real-world tasks. Unlike older robots that follow exact instructions, these new robots can learn, improve, and handle unexpected situations. Companies like Siemens and NVIDIA announced partnerships to build what they call an industrial AI operating system. Siemens also launched Digital Twin Composer, a software tool that lets manufacturers create digital copies of their factories to test changes before they happen in real life. This technology helps companies like PepsiCo in the United States test upgrades to their facilities without any risk.

In the semiconductor and materials manufacturing field, artificial intelligence is helping scientists create better products. Michigan State University in the United States received $3 million in funding to use agentic AI for growing high-quality diamonds in laboratories. These diamonds are used in computer chips for electronics, electric vehicles, and renewable energy equipment. The AI system learns to spot defects before they happen and can adjust the growing process automatically to prevent problems. Researchers believe this technology will eventually make the process faster and better than traditional methods.

The chemical and materials manufacturing industry is seeing the biggest changes from agentic AI. Instead of following recipes step-by-step, companies are now using agentic AI systems that behave like super scientists. These AI agents can read thousands of research papers and company lab notebooks at the same time and use all that knowledge to help discover new materials. This approach is similar to how AlphaFold, Google's AI system, changed the way pharmaceutical companies design medicines. Companies like Syensqo, a specialty chemicals producer, have created their own AI agents that analyze information from research papers, patents, and internal data to find the best research projects to pursue. Microsoft's Discovery platform helps these companies with this process by using advanced AI agents to work through mountains of information in days instead of months.

One remarkable example is ChemLex, a Singapore-based company that built an amazing system for chemical manufacturing. This platform combines robots with AI agents to handle chemistry experiments. The robots act like the hands of a chemist, running experiments with perfect precision and speed. The AI acts like the brain, deciding what experiments to try, looking at the results, and figuring out what to do next. The system works like a closed loop—each experiment teaches the AI something new, helping it get smarter and tackle harder chemistry problems. Merck, a major chemical company, signed an agreement with ChemLex to use this technology to speed up its research and development work.

Companies are adopting agentic AI because it solves real problems in manufacturing. According to a recent research report, about 94 percent of companies said they had to stop working on promising projects because they didn't have enough time or computing power. By using AI-driven simulations instead of only doing physical experiments, companies save an average of $100,000 on each research project. Most impressively, about 73 percent of companies said they would accept slightly less perfect results if the AI could run simulations 100 times faster. The switch to agentic AI isn't replacing old methods—instead, companies are blending traditional techniques with new AI tools, with 42 percent already using AI-native platforms and another 34 percent testing AI systems. This hybrid approach lets manufacturers get the best of both worlds: the reliability of proven methods plus the speed and intelligence of artificial intelligence.