What AI Can't Do Yet — But Will
Large companies like Procter & Gamble have nearly 50,000 direct suppliers, and orders of magnitude more sub-suppliers. Over 80% of supply chain data is unstructured, and less than 6% of companies have visibility beyond tier one. At FRDM, applying Large Language Modeling on top of enormously disparate data sets is saving months of work in data cleansing, hydration, and federation — speeding up the process by up to 400%. Beyond data, AI is also detecting risk signals: applying learning models to trade data and sub-tier supplier relationships allows companies to identify potentially harmful patterns early. Just as AI predicts logistical risks from weather patterns, it can cross-reference climate data projections with human migration predictions — because where there is migration, there is exploitation.
Over 80% of supply chain data is unstructured — AI is finally making sense of it.
Large companies like Procter & Gamble have nearly 50,000 direct suppliers, and orders of magnitude more sub-suppliers. Over 80% of supply chain data is unstructured, and less than 6% of companies have visibility beyond tier one. At FRDM, applying Large Language Modeling on top of enormously disparate data sets is saving months of work in data cleansing, hydration, and federation — speeding up the process by up to 400%. Beyond data, AI is also detecting risk signals: applying learning models to trade data and sub-tier supplier relationships allows companies to identify potentially harmful patterns early. Just as AI predicts logistical risks from weather patterns, it can cross-reference climate data projections with human migration predictions — because where there is migration, there is exploitation.
AI can analyze vast data sources to identify patterns of potential violations before they become liabilities.
AI is beginning to be used to predict human rights risks in supply chains by analyzing supplier records, social media, news articles, and other public data to identify patterns and indicators of potential violations. It can learn from past incidents to predict the likelihood of future risks, allowing companies to take preventive action and focus mitigation efforts where they matter most. According to Deloitte, AI-powered supplier risk management can reduce the time to evaluate supplier risk by up to 63%. At FRDM, evaluation periods are even faster — with AI prioritizing mitigation workflows for trade compliance and procurement teams, updating risk assessments in real-time and ensuring companies stay ahead of emerging threats.
AI is solving supply chain problems faster than anyone expected — and it's only getting better.
The reality is that AI is solving a lot of problems very quickly, and the pace of improvement is accelerating. FRDM was created to build a supply chain transparency movement — allowing businesses and consumers to buy with their values. Artificial intelligence is speeding this movement up by decades. Supply chain transparency is a quadruple win scenario: saving time, money, people, and planet. We simply cannot solve entrenched human rights issues or climate change without it, since supply chains account for an average of 80% of a company's total emissions.
*not sales material disguised as 'resources.'
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