Manufacturers are grappling with macroeconomic challenges such as geopolitical tensions, volatile demand, rising costs, and supply chain disruptions, alongside internal issues like talent shortages and data silos. These challenges are exacerbated by fundamental data issues, with many manufacturers still relying on manual data entry and struggling to integrate diverse data sources, which hampers their ability to leverage AI and predictive analytics effectively. Companies are beginning to explore generative and non-generative AI to enhance forecasting, design, and communications, aiming to improve operational efficiency and reduce costs. Tredence and Fivetran offer solutions to these challenges by providing unified data architectures, scalable data ingestion processes, and AI-driven insights, enabling manufacturers to optimize their operations and build supply chain command centers for real-time decision-making. An example of successful application is an aluminum manufacturer that reduced production stoppages and improved product quality through predictive models and real-time data integration, demonstrating the potential for significant business value from advanced data and AI technologies in manufacturing.