Empowering Engineers with Transparent, Data-Driven Insights through AI-Backed Pipelines

Manmohan Alla ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 17, 45 - 53

Abstract: The digital transformation of engineering environments has catalyzed a paradigm shift from data collection to meaningful interpretation and action. Industrial facilities now generate unprecedented volumes of information, creating challenges and opportunities for operational excellence. This article examines how AI-backed data pipelines transform complex data streams into accessible insights that empower engineers and business leaders. The evolution from fragmented legacy systems to integrated platforms has fundamentally altered how engineering knowledge is generated, shared, and applied. Modern architectures incorporating real-time processing, API-driven integration, cloud-based warehousing, and explainable AI create a technical foundation that enables cross-functional collaboration by establishing a common data language. The transformative impact on decision-making speed and quality becomes evident through case studies spanning predictive maintenance, energy optimization, and product development. Integrating these technologies represents more than technological advancement—it fundamentally reimagines how organizations leverage collective expertise and information resources. By transforming data from static records into dynamic collaboration mediums, these systems enable more transparent, responsive, and effective engineering practices while preserving the central role of human judgment.

Keywords: Artificial Intelligence; Data Pipelines; Industrial Engineering; Cross-Functional Collaboration; Explainable AI (search for similar items in EconPapers)
Date: 2025
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