Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The artificial intelligence sphere faces a paradoxical reality where unknown investment and technological advancement attend with patient perpetration challenges. This dissociation stems primarily from abecedarian scarcities in underpinning data structure rather than limitations in algorithmic approaches. Organizations constantly underrate the complexity of data engineering conditions for product-grade AI systems, creating cycles of promising aviators that fail to gauge. The document examines how data engineering breakdowns manifest across AI executions, revealing common failure patterns including channel complexity, specialized debt accumulation, and integration challenges. Through artificial case exemplifications gauging manufacturing, consumer electronics, and independent systems, the document illuminates critical architectural principles for erecting AI-ready data platforms. A perpetration roadmap outlines practical approaches for transubstantiating heritage systems, developing organizational capabilities, and measuring success through specialized performance pointers aligned with AI issues.