How finance teams can use AI to support targeted working capital improvements
1. A Longstanding Issue, a Modern Response
Net Working Capital (NWC) is one of the most effective levers to release cash from daily operations and strengthen financial flexibility. Yet optimization is rarely straightforward: it spans cross-functional processes, and many organizations struggle with limited visibility, unclear ownership, and a lack of actionable insights. Artificial Intelligence (AI) helps finance teams move beyond static dashboards: uncover hidden inefficiencies, forecast working-capital dynamics, and simulate the impact of targeted interventions. This means shifting from reactive analysis to proactive, fact-based decisions.
The central question is: How can organizations deploy AI to enhance NWC in a way that is structured, feasible, and aligned with business priorities?
2. Our transition approach
To translate AI’s potential into measurable impact, a structured approach is essential. We use a four-phase cycle that drives sustainable NWC improvements
- What is happening? Descriptive analysis establishes transparency of the current state.
- What will happen? Predictive modeling uses historical data and operational drivers to forecast how working capital metrics may evolve.
- What is wrong? Diagnostic assessment focuses on analyzing outliers and irregular behaviors.
- What should we do? Prescriptive simulation provides a scenario-building environment to test the impact of potential interventions before execution.
Taken together, these phases form a continuous loop: insights feed predictions, predictions guide diagnostics, and simulations turn options into decisions.
3. The four phases in practice
3.1. Descriptive analysis: Understanding the current state
Traditional finance dashboards provide transparency into NWC development across customers, suppliers, and materials. These cover core KPIs such as Days Sales Outstanding (DSO), Days Inventory Outstanding (DIO), and Days Payables Outstanding (DPO).
Typical insights include:
- DSO increased by 30 days for certain Customer segment in last quarter
- DIO exceeded policy thresholds by 15 days for certain product groups
- DPO dropped by 10 days below target in the last quarter for certain vendor groups
This stage lays the foundation by revealing correlations and interrelated drivers, forming the basis for AI-driven analysis.
3.2. Predictive modeling: Forecasting what’s ahead
Machine-learning frameworks (such as XGBoost) are increasingly applied to NWC modeling to forecast how key metrics like DSO, DPO, and inventory levels will evolve under different business scenarios. They combine historical patterns with operational drivers to predict future behavior.
Examples:
- Predicting whether DSO will remain elevated based on customer payment history, billing cycles, and invoice error rates
- Forecasting DIO trends using inputs such as sales demand, planning buffers, and procurement policies
- Estimating DPO behavior linked to payment discipline, supplier terms, and sourcing behavior
Model explainability tools (like as SHAP) help finance teams understand the drivers behind each forecast, validate results, and sharpen analysis.
3.3. Diagnostic assessment: Analyzing the anomalies
Once patterns are predicted, the next step is to examine anomalies and deviations that fall outside expected ranges. Outlier analysis enables the identification of irregular behaviors (policy-related, behavioral, or process-driven) and process gaps that may bias predictions or indicate where corrective action is required.
Example findings:
- Increase in DSO due to unusually long delays among a small customer group
- Increase in inventory due to sudden stock build-up in a single product line
- Decrease in DPO due to extremely short payment cycles for certain suppliers
Together, prediction and diagnostics create a cycle that not only projects outcomes but also explains why variances occur.
3.4. Prescriptive simulation: Testing impact before acting
With root causes identified, the focus shifts from explanation to simulation. Instead of prescribing fixed solutions, the main aim is to provide a scenario-building environment where finance teams can test the effects of potential adjustments before execution.
Simulated actions may include:
- Reducing invoice errors to accelerate collections and lower DSO.
- Adjusting safety stock in stable supply chains to reduce excess inventory.
- Aligning payment terms across supplier groups to influence DPO.
By simulating changes in advance, finance leaders can quantify potential liquidity gains compare trade-offs, and prioritize the changes that deliver the most significant liquidity improvements.
4. Concluding remarks
Identifying optimization potential using business analytics – especially in NWC – is a journey, not a one-off. Common hurdles include resistance to change, data availability and quality in fragmented system landscapes, and limited cross-functional collaboration across finance, operations, and supply chain. Advanced analytics provides a structured way to revisit the challenge: understand inefficiencies, anticipate trends, and use a controlled environment to test the impact of change before acting.
AI-supported decision-making is continuous, not static. Each cycle of prediction, analysis, and simulation feeds back into the model, improving accuracy and adaptability over time, and turns it into a dynamic decision-support system.
We work with CFO teams to set priorities, to design the journey to modern Performance Management, and to drive measurable progress across the finance agenda.
