8 Ai-Driven Advantages For Manufacturers Impacted By Steel, Aluminum Tariffs

Some of President Donald Trump's tariffs echo his first-term policies which imposed 25% tariffs on steel and 10% on aluminum in 2018 under Section 232 of the Trade Expansion Act of 1962, citing national security concerns.

However, the 2025 measures are more aggressive, closing loopholes such as tariff exemptions and raising the aluminum tariff to match steel at 25%. The president has also signaled a willingness to impose even higher tariffs, as evidenced by the short-lived 50% threat against Canada.

These tariffs are part of a broader trade agenda that includes a 20% levy on all Chinese imports and plans for reciprocal tariffs to match the import taxes imposed by other countries on U.S. goods. While the administration argues that these measures will protect domestic industries and strengthen national security, critics warn they could rekindle already high inflation, slow economic growth and harm U.S. manufacturers reliant on imported materials. 

Unfortunately, reshoring production isn’t a quick fix. Rebuilding the infrastructure and workforce to sustain domestic production could take decades. In the meantime, manufacturers must find ways to stay agile, competitive and compliant in a volatile trade war.  

While tariffs are designed to incentivize domestic production, history suggests they often trigger alternative responses. Instead of onshoring, many companies sidestep tariffs by shifting production to lower-cost regions like Vietnam, Chile or Mexico. During Trump's first term, the 2018 steel and aluminum tariffs benefited U.S. metal producers but imposed significant costs on downstream industries.

A 2023 U.S. International Trade Commission report found that the $2.3 billion increase in production by steel and aluminum manufacturers was offset by a $3.5 billion decline in output among companies that use these materials. 

The immediate pain of the tariffs will be felt by manufacturers that rely on steel and aluminum. For instance, Steelport Knife Co., a small Oregon-based manufacturer, has already seen a 10% price hike from its U.S. steel supplier. The company's Japanese and German competitors can source steel at lower costs, putting the business at a disadvantage. To cope, Steelport is cutting costs by tightening inventories and reducing travel to trade shows. 

By leveraging AI, companies can proactively manage risks, optimize supply chains and stay ahead of policy shifts.

Using large language models (LLM)  and machine learning, AI-powered platforms can scan thousands of global trade sources including government updates, regulatory changes and industry reports in real-time. These systems instantly flag relevant changes, assess their impact and provide actionable insights.

Automated alerts ensure that manufacturers are informed of critical updates the moment they occur, reducing reaction time and enabling proactive adjustments. This means manufacturers can pivot quickly, rather than scrambling to react after new tariffs take effect.  

Instead of guessing how new tariffs will affect profitability, AI-driven predictive models simulate different scenarios, helping manufacturers anticipate costs and adjust strategies accordingly. Experts warn that businesses relying on metal components, from vehicle manufacturing to food packaging, will struggle to absorb the added costs, ultimately passing them down to consumers.

Some companies may attempt to shift away from metal-dependent production methods, such as beverage brands emphasizing plastic packaging over aluminum cans. If the steel tariff threatens margins, AI can recommend alternative sourcing strategies, pricing adjustments or contract renegotiations to mitigate losses.  

High tariffs can significantly impact supply chains by making certain imported components prohibitively expensive. AI-driven supply chain management tools can assess alternative sourcing options from different countries, taking into account production capabilities, cost-effectiveness and regulatory requirements. This allows manufacturers to quickly pivot their supply chains without major disruptions. 

Smaller manufacturers, in particular, face challenges when shifting suppliers, as they lack the economies of scale and automation advantages of larger firms. AI-driven analytics can help these companies identify the most feasible supply chain adjustments, whether by diversifying suppliers, nearshoring production or modifying component sourcing to mitigate tariff exposure.   

Tariff engineering is the strategic modification of a product’s design, composition or sourcing to qualify for lower-duty classifications. AI enhances this process by analyzing historical tariff rulings, trade agreements and HS code structures to identify opportunities for tariff optimization. By leveraging AI-driven insights, manufacturers can explore ways to legally adjust materials or assembly locations to minimize tariff costs while maintaining compliance.

For example, if a steel component incurs a high tariff but an alternative composite material falls under a lower-duty classification, AI can flag this opportunity and recommend a viable material substitution. Similarly, AI can assess whether minor product modifications—such as unassembled versus pre-assembled shipments—can result in lower tariff rates.

By continuously analyzing tariff structures and trade agreements, AI empowers manufacturers to implement cost-saving design and sourcing strategies while maintaining regulatory compliance.

Accurate classification under the Harmonized System (HS) is crucial for determining correct tariff rates, but misclassification can lead to hefty fines and shipment delays. AI-powered classification tools use deep learning to analyze product descriptions, assign accurate HS codes and cross reference historical tariff decisions, reducing compliance risks while saving time and resources.

Automated workflows further streamline compliance by integrating with enterprise resource planning (ERP) and trade management systems, ensuring that product classifications are consistently updated across an organization. This reduces human error, accelerates customs clearance and minimizes regulatory penalties. 

Trade compliance requires meticulous documentation, and AI can streamline this process by automating the generation and verification of required paperwork. AI-driven systems ensure that import and export documentation is accurate and complete, reducing errors that could lead to shipment delays or customs penalties.

Additionally, AI can optimize duty drawback claims by identifying overpaid tariffs and automating the reimbursement process, improving cash flow for manufacturers.

AI enables manufacturers to adjust pricing in real time based on tariff fluctuations, supplier costs and market conditions. By analyzing procurement data, transportation costs and regional tariff structures, AI-driven tools can identify cost-saving opportunities, suggest supplier diversification and optimize profit margins without compromising competitiveness.  

In industries like automotive and home construction, where metal tariffs can significantly raise material costs, dynamic pricing models allow companies to remain profitable while maintaining competitive pricing.

For example, rising costs for steel screws, wires and machine parts could increase the price of dishwashers, dryers and homebuilding materials. AI-powered pricing tools help manufacturers adjust strategies in response to these shifting cost structures.  

Trade compliance isn’t just a legal issue, it affects procurement, finance, sales and operations. AI-driven platforms centralize trade data, providing real-time visibility across departments. With automated dashboards and customized reporting, AI ensures that all stakeholders work from the same dataset. When teams work from the same playbook, they can make faster, more strategic decisions to mitigate risks and capitalize on opportunities. 

Trade policies will continue to evolve, but manufacturers that integrate AI into their operations will be best positioned to thrive. The companies that win in this environment won’t be the ones waiting for policy stability, rather they’ll be the ones using technology to stay ahead of the curve.