Retailers are entering a new phase in AI deployment where the focus extends beyond theft prevention to full-spectrum financial optimization. From dynamically adjusting supplier payment terms to forecasting cash impact of promotions and stress-testing sourcing strategies, machine learning is enabling supply chain and finance leaders to rewire how cash is preserved and deployed across global networks.
From Loss Control to Liquidity Management
AI tools that once flagged suspicious transactions are now steering financial decisions. One example is dynamic payment term optimization. In such cases, algorithms assess supplier performance, on-time delivery, defect rates, contract compliance, to determine whether payments should be accelerated for discounts or delayed to preserve cash.
Retailers like Best Buy and Target, for instance, have experimented with AI tools that assess vendor reliability and historical performance to decide whether to accelerate payments for early discounts or extend terms to improve days payable outstanding (DPO). A 2024 study by The Hackett Group found that firms using AI for payment optimization improved working capital by as much as 15%.
Promotions, another drain on liquidity, are also being reevaluated. Traditional models forecast top-line uplift. Newer AI tools simulate downstream effects—return rates, inventory lock-up, and markdown liabilities. Unilever, for example, uses trade promotion optimization software that integrates cash impact modeling. By embedding cash sensitivity into promotional planning, companies can prioritize campaigns that both move inventory and preserve working capital.
Stress-Testing Supply Lines
In sourcing, companies are using AI to simulate financial exposure. Adidas runs scenario models that measure vendor risk under geopolitical shifts or raw material shocks. This informs volume allocation decisions based not only on price and delivery reliability, but on a supplier’s financial resilience.
Others are automating capital decisions. Maersk is piloting an AI-driven treasury platform that reallocates funds in real time based on freight rates, procurement cycles, and liquidity needs.
According to Deloitte’s 2025 Global Supply Chain Finance Survey, 37% of supply chain-intensive firms are actively developing AI-led capital allocation engines to manage liquidity with greater precision.
These tools go beyond dashboards, they create feedback loops between procurement, finance, and operations. For example, when return rates spike in a certain region, the system can throttle promotions or slow reorders in that geography to prevent cash drag.
Financial Precision at the Operational Front Line
AI’s role in supply chain management is no longer confined to cost efficiency or operational agility. It is becoming part of financial governance. For supply chain leaders, the challenge is to embed these systems within enterprise workflows while maintaining data discipline and oversight. The technology’s value lies not just in its ability to automate, but in how it improves judgment, on when to commit capital, where to hold back, and how to maintain flexibility in constrained environments.
