<scp>AI</scp> in chemical engineering: From promise to practice

Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows. Near‐term value arises where AI augments, rather than replaces, process system engineering (PSE) practice (e.g., through soft sensing and surrogate models), while autonomous operations, fully automated hazard and operability (HAZOP) analysis, and large‐scale mechanistic discovery remain largely at the research stage. The decisive bottleneck is reliable deployment: AI models must be treated like any other engineered system, with validation, monitoring, and governance aligned with emerging frameworks such as the EU AI Act and NIST risk management framework (RMF). With incubator labs, open benchmarks, and retooled education pipelines, AI can become a safe, reliable, and sustainable co‐worker in the process industries within years.