Managing AI in Manufacturing Systems - Solving the Data Bottleneck

The Data Bottleneck refers to the challenge of ensuring the availability of the right data at the right time in AI-driven projects. Early stages often involve uncertainty about when, how, and how much data will be required. The proposed approach focuses on estimating data requirements and determining when the data is needed at each phase of the AI lifecycle. This includes identifying critical data dependencies, ensuring data quality, managing imbalanced datasets, and implementing post-deployment monitoring to adapt to data shifts. By addressing these issues, organizations can enhance fairness, accuracy, and adaptability while sustaining model performance. Effective data bottleneck management empowers organizations to unify their data, improving trust, accessibility, and control. This approach supports key business objectives while enabling the development of reliable, scalable, and adaptable AI systems.