The manufacturing industry around the globe is now facing one of the most nervous upheavals in history, wherein the prediction of financial performance now ranks among the most crucial processes of production. In the case of technology-intensive companies, standard planning may not be as precise as it needs to be to cope with high capital intensity effectively, the uncertainty of the market, and speedy innovation cycles. This study fills this large strategic-execution gap by suggesting an AI-based Strategic Financial Real-Time (SFRT) to combine machine learning with fundamental management practices. The research design employed in the study, which includes the use of mixed methods, is applied by researchers working in the aerospace and semiconductors sectors to quantify the role of Strategic Intelligence in comparison with raw computational power. Performance analysis on a simulated dataset demonstrates that the suggested AI-based strategic model can attain an accuracy of the forecast at 94.8 %, which is 22.4 % higher than the traditional ones. In addition, the time taken to make a decision has been decreased by more than 90 % to less than one day. The strategic alignment score is gained by 24 %, and the budget variance (error rate) is reduced by 12.5 to 4.2 %, which is an excellent improvement of 66.4 %. These findings indicate that the most significant financial accuracy is achieved with the highest level of AI maturity, coupled with sound strategic governance and a redefined corporate strategy in the digital age. Finally, the results prove that a robust data-governance scheme correlates directly with financial certainty, whereby the leadership has the added confidence to concentrate on upper-level strategic shifts.
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