Building a Structured Ecosystem for Engineering AI: Pillars, Phases, and Future Directions
Dr Saima Shaikh
Mentor Head, Dept of IT Director, Care Maharashtra College of Arts, Science and Commerce
Mohd Faizan Rahmani
Research Scholar Dept of IT Maharashtra College of Arts, Science and Commerce
Mubin Shaikh
Research Scholar Dept of IT Maharashtra College of Arts, Science and Commerce
Abstract :
In recent years, artificial intelligence (AI) and machine learning (ML) techniques have been progressively integrated into a wide range of engineering disciplines, including manufacturing, energy systems, transportation, civil infrastructure, and materials science. These methods have demonstrated substantial promise in improving predictive accuracy, optimizing complex processes, reducing downtime through predictive maintenance, and accelerating design exploration. However, despite impressive technical results in research settings, many engineering AI initiatives struggle to transition from experimental prototypes to dependable production systems. Common obstacles include fragmented development workflows, inconsistent data quality, inadequate infrastructure planning, insufficient incorporation of domain expertise, and lack of long-term maintenance strategies. This paper proposes a practical, structured ecosystem and an eight-phase lifecycle designed to guide engineering teams through problem definition, infrastructure provisioning, rigorous data preparation, systematic domain integration, model design and training, interpretability and trust-building, iterative evaluation, and robust deployment with continuous monitoring. For each phase, we describe the key activities, expected outputs, and simple best practices aimed at reducing risk and increasing the likelihood of sustained operational value.
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