Hervé Panetto

Artificial intelligence (AI) is rapidly transforming mechanical engineering (ME) practice, influencing activities that range from modeling and simulation to optimization, diagnostics, design, and technical communication. This article consolidates perspectives discussed at a dedicated round-table during the 28th ABCM International Congress on Mechanical Engineering—COBEM 2025—and offers a structured reflection for the Brazilian and international ME communities. Rather than providing an exhaustive survey, the paper develops a coherent narrative around the conditions under which AI can strengthen—rather than dilute—engineering rigor. The article begins by clarifying how contemporary AI systems operate, distinguishing statistical learning from human cognition and outlining their capabilities, limitations, and emerging agentic AI systems. It then examines industrial adoption, workforce transformation, and the growing role of AI in scientific discovery and engineering workflows. Particular attention is devoted to the energy and infrastructure demands of AI-driven computation and their implications for sustainability, power systems, and thermal management. Building on these constraints, the paper discusses integrating physical knowledge, structured reasoning, and domain expertise into AI systems for safety-relevant engineering applications. Emphasis is placed on representative ME applications, physics-informed and hybrid learning frameworks, model development under physical constraints, validation, uncertainty quantification, and certification challenges. The paper also addresses the educational, epistemological, and ethical implications of AI, including curriculum modernization, human-agent collaboration, professional accountability, and the long-term sustainability of engineering knowledge. It concludes with a synthesis of principles for the responsible adoption of AI in ME, encompassing physical consistency, validation, awareness of uncertainty, energy sustainability, human oversight, and ethical responsibility. Our central message is that AI should be viewed not as a substitute for engineering judgment, but as a powerful tool whose value depends on disciplined integration with physical reasoning, scientific rigor, and professional responsibility.