Currently, the United States (U.S.) has issues with declining industrial values and supply chain digitalization, which threaten the country's industrialization and Industry 4.0 transformation. The country needs to reverse this trend through effective policy implementation. However, the literature's understanding of effective policies' long-run associations with the industrial value added (INV) of the country remains limited. Thus, this research investigates the long-term cointegrated relationships of several variables, such as artificial intelligence innovation (AIP), trade openness (TOP), supply chain digitalization (SCD), foreign direct investment (FDI), and GDP growth (GDPG), with INV based on the national level data from 1990 to 2023. The autoregressive distributed lag findings suggest that TOP, SCD, and GDPG have significant positive associations with INV in the long run, indicating that the significance of trade openness, supply chain digitalization, and economic growth is associated with the positive performance of industrial values within the country. However, both AIP and FDI exhibit significant negative associations with INV, indicating transitional and adjustment cost pressure of AI innovations and crowding out effects of domestic investments associated with FDI. That particular research suggests that all the variables would play individual effects and interact with one another as an Industry 4.0 transformation system to facilitate the improvement of industrial values in the country over long-run dynamics. Together, these findings can help federal policymakers empirically tailor long-term strategies to turn decreasing industrial value-adding performance into an upward trend and transform into Industry 4.0 by ensuring positive combined associations from technology-driven strategies.