Heavy industries often face hurdles in integrating machine learning and data science tools, despite their potential benefits like enhanced efficiency. Some primary challenges include: Infrastructure Mismatch: Legacy systems in heavy industries , hinder smooth technological integration with market available data science tools, or bespoke solutions. Data Quality: The vast, rugged, and decentralised industrial operations make consistent data collection difficult due to communications, systems and personnel integration. Often needing synchronisation between the application specialists and the tech team. Skills Gap: There's often a lack of in-house expertise to handle advanced data tools or lack of a holistic approaches, especially when applied to complex problems. Current AI and Computing solutions are too niche and rigid in problem solving capability, with an inability to evolve as the problem evolves. Current market available products are complex, expensive and accuracy is a problem.