ENGINEERING PROBLEM SOLVING WITH ARTIFICIAL INTELLIGENCE: An Evidence-Based Industry 4.0 Framework for Engineers Who Use AI Without Delegating Judgment
ENGINEERING PROBLEM SOLVING WITH ARTIFICIAL INTELLIGENCE: An Evidence-Based Industry 4.0 Framework for Engineers Who Use AI Without Delegating Judgment
ENGINEERING PROBLEM SOLVING WITH ARTIFICIAL INTELLIGENCE: An Evidence-Based Industry 4.0 Framework for Engineers Who Use AI Without Delegating Judgment - Lostal Martínez, PhD Félix Rubén
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ENGINEERING PROBLEM SOLVING WITH ARTIFICIAL INTELLIGENCE: An Evidence-Based Industry 4.0 Framework for Engineers Who Use AI Without Delegating Judgment
Lostal Martínez, PhD Félix Rubén
Synopsis "ENGINEERING PROBLEM SOLVING WITH ARTIFICIAL INTELLIGENCE: An Evidence-Based Industry 4.0 Framework for Engineers Who Use AI Without Delegating Judgment"
Artificial Intelligence can accelerate engineering problem solving—but it should never replace engineering judgment. Engineering Problem Solving with Artificial Intelligence is a practical guide for engineers, managers, technical specialists, and industrial professionals who want to incorporate AI into structured problem-solving processes without delegating critical decisions to algorithms. The book connects three areas that are too often treated separately: engineering problem-solving methodologies, Industry 4.0, and Artificial Intelligence. Drawing on academic research, industrial experience, evidence-based management, and previously published studies, Rubén Lostal presents a disciplined framework for using AI as an analytical support tool rather than as a substitute for technical reasoning. The central principle is simple: structure first, AI second, validation always, human decision last. Throughout the book, the reader learns how AI can support problem definition, data analysis, hypothesis generation, root-cause investigation, pattern recognition, technical documentation, simulation, verification, and organizational learning. At the same time, the book explains the risks of cognitive dependency, automation bias, poor-quality data, weak processes, hallucinated conclusions, and the dangerous tendency to accept AI-generated answers without engineering validation. Special attention is given to the integration of AI with established methodologies such as PDCA, DMAIC, A3, SPC, Lean Manufacturing, root-cause analysis, FMEA, evidence-based management, and Industry 4.0 systems. This is not a book about asking better prompts. It is a book about solving engineering problems better with AI. Readers will find practical frameworks, verification gates, engineering cases, decision criteria, implementation principles, governance recommendations, and references to academic research in APA format. The book is especially useful for professionals who are beginning to use generative AI and advanced analytical tools in manufacturing, quality, maintenance, operations, industrial engineering, process engineering, and technical management. Its fundamental message is clear: AI can analyze. AI can suggest. AI can identify patterns. AI can accelerate the investigation. But the engineer remains responsible for the decision. For organizations moving toward Industry 4.0 and AI-enabled operations, the competitive advantage will not come from simply adopting more technology. It will come from combining structured problem solving, reliable data, engineering knowledge, critical thinking, and responsible human supervision. Technology supports the engineer. It does not replace engineering responsibility.
