IVALUA | WHITEPAPER
Real-world implementations demonstrate IVA’ s impact. A multinational steel manufacturer with high exposure to metal and country-specific tariffs used IVA to identify potential new sources of supply facing lower or no tariffs while meeting various company criteria.
“ They were able to identify a shortlist of potential new suppliers to engage in minutes,” Alex reveals.“ That would have normally taken days or weeks in the past.”
Another customer, a global technology group, used IVA to extract and analyse contract terms across suppliers to assess its flexibility in responding to tariffs and determine when new suppliers would be needed. It then used IVA to identify potential alternatives for those categories and invite additional suppliers to sourcing events, turning what would have been a months-long manual review into a rapid, data-driven process. Adding one extra qualified supplier to each sourcing event alone delivered almost $ 10m in incremental savings.
“ Depending on the competitive dynamics of each industry, the impact of tariffs is absorbed by a different part of the overall chain”
Alex Saric, Chief Marketing Officer, Ivalua
Overcoming the data barrier Despite AI’ s clear potential, significant barriers remain to adoption for tariff and risk management.
The most critical challenge is data quality and accessibility. For AI to provide accurate, effective recommendations and actions, it requires access to accurate, complete data – specifically, a comprehensive view of the supply chain, including all suppliers and the sub-tier suppliers they depend upon to deliver goods and services.
Very few companies have mapped their full supply chain, which fundamentally limits the ability of AI – or employees for that matter – to understand the impact and exposure of shocks and trade policies.
Additionally, information about suppliers, such as performance, spend and risk, is often siloed and difficult to access and map to the same supplier record. This fragmentation prevents AI and other systems from utilising the data effectively. No or poor meta data( information about data) also limits the ability of AI to properly leverage business data.
The solution lies in gaining control of supplier data through Source-to-Pay( S2P) platforms built on a unified data model with a robust meta data layer. Platforms with master data management capabilities that can synchronise data stored in enterprise resource planning systems are optimal for addressing this challenge as they also fix underlying data issues such as duplicates that exist in ERP systems.
58 November 2025