How Data and AI Help Overcome Tariff Volatility

One of the biggest global economic headlines this year has been the ever-changing tariffs on imports and the associated uncertainty that is forcing many industries to restructure operations and optimize costs. 

The recent U.S. move to raise average import duties to up to 50% on some countries or sectors (such as steel and aluminum) is directly impacting global supply chains, inflating costs and compelling companies to fundamentally rethink their sourcing and operational strategies. Day-to-day developments demonstrate how fast and unstable this landscape is — and these rapid changes make agility and visibility critical for businesses.

The manufacturing, retail, healthcare, life sciences, agriculture and automotive industries, as a few examples, face new challenges to stay competitive and agile in operations ranging from sourcing and supply chain to the final cost of goods sold. Companies are now forced to fine-tune logistics and adjust pricing models and demand forecasts, as well as make product and market modifications. 

This restructuring is happening in a fast-moving environment, where siloed data makes it hard to see and manage end-to-end operations. Companies need unified, real-time systems and AI capabilities to speed up analysis, improve forecasting and adapt quickly to market shifts. But agility also depends on secure collaboration across the broader ecosystem. Without the ability to share data with suppliers, logistics providers and other partners, decisions remain slow and disconnected. When these challenges are addressed, organizations gain a more connected, transparent supply chain, with insights shared across teams and industries.

Below, we explore the impact of tariffs in more detail along with examples of how businesses are navigating this complex environment. A major theme is the growing importance of a unified AI data strategy to help businesses turn data into insight and take action with simplicity, speed and precision, all while meeting regulatory requirements. A strategic approach to tariffs is crucial for companies to gain competitive advantage. Reframing these challenges as opportunities for optimizing insights and analysis to restructure supply chains, products and market choices will ultimately lead to higher revenue and staying ahead. 

How companies are adapting to tariff turbulence 

In the face of shifting tariffs and global trade uncertainty, companies are making significant adjustments to stay agile in the short term and build resilience for the long term. Meeting this moment requires faster, data-driven decision-making and the effective use of AI. 

Risk management is even more important now in this changing global trade environment, due to elevated supplier bankruptcy risks and geographic exposure to volatile trade policy shifts. The complexity of compliance, such as tracking parts across multiple borders, adds to the need to analyze and optimize supply chains.

To support these efforts, many organizations are turning to Snowflake’s AI Data Cloud to unify internal and external data, power AI and machine learning for predictive modeling, and enable secure collaboration across both internal teams and external partners. With capabilities such as Secure Data Sharing — enabled through core data sharing functionality and the listing option for direct sharing or publication on Snowflake Marketplace — companies can work more effectively with suppliers, logistics providers, customs brokers and other stakeholders across their supply and distribution networks.

Snowflake customers are using these capabilities to respond to tariff volatility more effectively by leveraging a dynamic, collaborative, unified data ecosystem and AI capabilities in three stages of operations affected by tariffs: 

• Supply chain management and sourcing: Bringing together internal and external data and using AI-powered predictive modeling gives insight into risks and costs associated with suppliers and their exposure to tariffs and helps organizations optimize supply chains, reevaluate shipping routes and be equipped to consider reshoring or nearshoring.

• Market and product strategy adjustment: A simplified landscape of market data, including demand patterns, regional regulations and forecasts of tariff effects, informs decisions on increasing local production and manufacturing or strategizing product design.

• Pricing and competitive positioning: Native AI/ML capabilities can run complex predictive models to help companies balance how much of their tariff costs can be passed on to consumers, while also avoiding a negative impact to their sales volume or market share.

   

Supply chain management and sourcing

Companies relying the most heavily on imported critical components are facing the biggest challenges today, such as the heavy reliance on active pharmaceutical ingredients (APIs) from abroad. Tariffs can accumulate in cases of cross-border supply chains — for example, auto parts that traverse multiple crossings can incur tariffs of up to 200%. A lack of real-time visibility into sourcing and supply chain risks and costs means slow, challenging and detrimental responses to these and other global trade factors.

Organizations are using Snowflake to unify internal and external data and harness AI-powered analytics to actively reengineer their sourcing and supply chains — reducing tariff exposure, mitigating risk and gaining greater control over costs. 

Predictive models allow supplier risk scoring to assess viability and financial health, reducing exposure to tariff risks. Furthermore, Snowflake enables geographic exposure analysis to visualize and understand risks associated with specific regions and suppliers. Snowflake also automates compliance monitoring and reporting, tracking tariff classifications and enabling audit-ready documentation, while providing early warning systems with real-time alerts on supplier risks, policy changes and trade disruptions.

Where tariffs touch the supply chain cycle and how to mitigate effects

Tariffs have the potential to affect components across the supply chain — from the cost of sourcing to shipment and logistics to the location of production. Each step presents risks but also opportunities to leverage a robust data foundation and AI, giving an advantage to companies that are able to minimize disruption. Snowflake allows companies to bring together data from across the supply chain cycle; collaborate across departments, companies and industries; and use those insights to adjust and reduce costs.

Let’s start with insights into sourcing. Advanced data analytics and models can help companies simulate cost impacts, inventory levels and alternative sourcing strategies. They can then identify and evaluate suppliers in countries not subject to specific tariffs or increase the use of domestic suppliers. Or they can use predictive models to assess supplier viability, health and exposure. 

Retailers rely on Snowflake to analyze data on sourcing and supply chain and enable cost savings. For instance, Kraft Heinz is actively managing costs and exploring alternative sourcing and reformulation to mitigate the impact of tariffs and inflation on the cost of goods sold (COGS) by leveraging data-driven insights. 

Moving on to supply chain analysis, companies can gain a real-time, end-to-end view of global supply chains by integrating supplier, shipment and market data. Kimberly-Clark is responding to significant cost impacts from tariffs (estimated at $300 million, affecting 20% of its U.S. business) by optimizing its supply chain operations.

By joining procurement data with supplier directories and country-of-origin and logistics data in Snowflake, companies perform sourcing simulations across geographies. Snowflake also enables secure data sharing and makes it easier to collaborate with third-party logistics, brokers or contract manufacturers in real time. The resulting collaborative ecosystem insights enable multiparty analysis — from shared supplier risk models to joint scenario planning.

For more future-oriented visibility, they can predict disruptions by identifying at-risk suppliers and geographies using live feeds and AI-driven risk models. Snowflake’s partner network allows companies to leverage third-party data and solutions for enhanced visibility and decision-making — the Exiger Supply Chain Explorer, for instance, maps supply chains and identifies high-risk suppliers and dependencies.

The resulting simple, unified, fast insights and analysis lead to faster supplier diversification, reduced procurement costs, fewer disruptions and increased agility in sourcing decisions.

Companies can also optimize logistics. Real-time logistical data lets them reevaluate shipping routes and modes and look for ways to offset the impact of tariff costs while increasing efficiency. This often involves complex modeling of transit times, fuel costs and potential duty stacking and the need to evaluate how costs change if new suppliers are part of the restructuring. Global logistics and supply chain leader Penske Logistics uses Snowflake’s scalability and automation to equip its leaders with rich reports spanning years for data comparisons in a matter of minutes — helping to optimize routes, ensure the seamless flow of goods and reduce disruptions.

Finally, companies can reconsider the location of production. Reshoring or nearshoring is an option to reduce cross-border tariff exposure and shorten supply chains. Snowflake supports these decisions by aggregating external data — such as labor costs, real estate, energy prices and tax incentives — with internal operational and cost data to drive actionable insights. Data modeling helps businesses assess the long-term feasibility and cost-effectiveness of bringing production closer to home or to nearby countries, with deep analysis of capital investment, labor costs and geopolitical risk. 

Market and product strategy adjustment

Comprehensive market data gives companies a guide to help prioritize the right investments and rethink their product strategies to minimize tariff effects. 

For example, data on demand patterns, regional regulations and potential tariff effects is guiding decisions on investments in local production and manufacturing capabilities in key markets — reducing costs by reducing dependence on tariff-affected imports. In July, AstraZeneca announced a $50 billion U.S. expansion to include a new Virginia plant and expansion in three other states, responding to reports that the U.S. administration has been considering tariffs potentially as high as 200% on imported drugs.

Companies also redesign products by modifying components or designs to use materials or parts not subject to higher tariffs. This process is often driven by detailed data on material availability, cost implications and regulatory compliance. For manufacturing and CPG companies, tariffs affect specific imported components or raw materials. 

Snowflake makes it easy to bring together diverse internal data (ERP, CRM, logistics, inventory) with external data (tariff schedules, geopolitical risk, market indices from Snowflake Marketplace). Ingesting ERP data into Snowflake allows a detailed analysis of bills of materials (BOMs), part-level costs and supplier data. Companies can then quantify how tariffs inflate input costs and can prioritize the products or lines most at risk.

The unified access through Snowflake’s AI Data Cloud allows governed, real-time data sharing with suppliers, customers and partners.

This eliminates complex data pipelines and data silos for a simplified data landscape that lets businesses quickly model tariff impacts in real time.

Pricing and competitive positioning

Companies are also faced with how much cost can be passed on to consumers — if any — and need data-driven insights to make those decisions as well as negotiate with suppliers. By adding tariff data from Snowflake Marketplace, for instance, companies can perform precise COGS calculations.

Native AI/ML capabilities in Snowflake such as Snowpark for machine learning can run complex predictive models for price elasticity, COGS impact and alternative sourcing scenarios, helping companies hit a balance between how much of their tariff costs can be passed on to consumers while avoiding a negative impact to their sales volume or market share. 

Alternatively to passing on costs to consumers, companies can leverage data on supplier costs to negotiate with suppliers.

The role of data in navigating tariffs

The strategies outlined above — managing supply chains and sourcing, modifying products and markets and optimizing pricing — depend on fast, intelligent decision-making powered by a unified data and AI platform. In today’s volatile trade environment, companies that overcome data fragmentation and foster real-time collaboration will gain a clear advantage.

Snowflake’s AI Data Cloud delivers exactly that. It brings together vast amounts of internal and external data, enables predictive modeling and advanced analytics and makes it easy to share governed insights with suppliers, logistics providers and other stakeholders. With Snowflake, organizations are not just reacting to tariff shifts, but they are anticipating change, optimizing operations and making smarter, faster decisions at scale.

The businesses that lead in this environment will be those that use data and AI not just to react but to transform uncertainty into opportunity.

Learn how the Snowflake AI Data Cloud can help your organization mitigate the impact of tariffs and build a more resilient future. Contact us.