Rising financing costs, volatile supply chains, and uncertainty in sales markets are increasing the pressure on companies to manage liquidity efficiently. At the same time, traditional working capital management (WCM) approaches are reaching their limits. While metrics such as Days Inventory Outstanding (DIO), Days Sales Outstanding (DSO) and Days Payables Outstanding (DPO) provide valuable transparency, they are primarily backward-looking and often support only reactive measures. 

Artificial Intelligence (AI) fundamentally changes this paradigm. Instead of analyzing only historical data, AI identifies complex interdependencies, forecasts future developments, and supports or automates operational decisions. As a result, working capital management can evolve from a reporting function into an intelligent, data-driven steering system that connects functions across the value chain. 

The value of AI extends beyond automating individual processes. It can continuously learn from millions of data points. Modern machine learning models analyze information from ERP systems, market indicators, supplier portals, CRM databases, and numerous external data sources. This creates greater transparency into liquidity and capital commitment risks. 

A traditional analyst may realistically consider only around 5 to 10 influencing factors, while an AI model can process several hundred parameters at the same time. This can improve forecast precision and provide more robust decision-making foundations based on scenarios and evaluated models. 

The effectiveness of AI in working capital management combines several technology components. Modern data platforms form the foundation by integrating information from ERP systems such as SAP S/4HANA, finance applications, CRM systems, supply chain solutions, and external data sources. Several different AI methods can then analyze this integrated data. 

Machine learning models identify patterns and interdependencies in historical data and generate forecasts for demand, inventory, incoming payments, and supplier risks. Deep learning methods process large, complex datasets and can improve forecast quality, particularly in volatile markets. Generative AI analyzes unstructured information such as emails, contracts, supplier evaluations or management reports and presents results in natural language. This is complemented by large language models (LLMs), which act as an intelligent user interface and enable business users to retrieve analyses through simple natural-language prompts. 

Multi-agent systems represent the next stage of development. Specialized AI agents for inventory management, cash forecasting, accounts receivable, and accounts payable can work together autonomously. Through reinforcement learning, the systems can continuously evaluate the impact of their decisions and optimize their steering logic over time. Combined with real-time data, digital twins, and cloud architecture, this can create a self-learning working capital management system that detects deviations early, simulates scenarios, and implements optimization measures almost in real time.

Depending on the industry, inventory can account for around 50 to 70 percent of working capital. This gives AI substantial potential to improve capital efficiency. 

The latest forecasting algorithms combine historical demand data with external factors such as economic indicators, weather data, market trends, price developments, and customer behavior. AI independently identifies patterns and interactions that conventional forecasting methods may overlook because of their complexity. 

Current generative and foundation models are particularly powerful because they not only create forecasts but can also explain the drivers behind changes in demand. These “explainable forecasts” help planners understand the results and support effective collaboration between people and technology.  

Potential benefits include: 

• Higher forecast accuracy 

• Improved delivery performance 

• Lower safety stocks 

• Reduced obsolescence risks 

• Improved capital turnover 

Depending on data quality, process maturity, and implementation, companies can reduce inventory by 20 to 30 percent while maintaining stable or improving service levels. 

In accounts receivable, AI changes how companies manage incoming payments. Instead of monitoring receivables only by due date, machine learning models analyze each customers’ payment behavior. 

For example, AI can predict: 

• Which invoices are likely to be paid late 

• Which customers show an increased default risk 

• Which dunning measure has the highest probability of success 

• Which customers are suitable for cash discount or financing offers 

Generative AI can also support the automated creation of customer-specific communication measures. This allows receivables management to evolve from an administrative function into a proactive liquidity lever.

AI also creates new opportunities in accounts payable. AI systems analyze payment terms, cash discount potential, supplier risks, and liquidity forecasts in real time. 

Based on these analyses, these systems can generate recommendations such as: 

• Use cash discounts when they offer an attractive return 

• Extend payment terms without damaging supplier relationships 

• Prioritize strategically important suppliers to protect supply continuity 

• Optimize supply chain finance programs 

This makes payables steering an active component of corporate finance. 

The most exciting development at present is the use of Agentic AI. This refers to autonomous AI agents that not only analyze and recommend but can also initiate measures independently. 

For example, a working capital agent could: 

• Detect declining demand 

• Automatically adjust order quantities 

• Recalculate safety stocks 

• Inform suppliers 

• Simulate the impact on cash flow and EBIT 

• Implement the selected course of action 

Several specialized agents for inventory, receivables, payables, and cash management can work together as an integrated system. This can create a “Digital Working Capital Control Tower” that continuously identifies and realizes optimization potential. A clear governance model must define which decisions agents may make autonomously and which require human involvement (“human-in-the-loop”). 

AI makes working capital management significantly more intelligent, faster and more effective. The decisive benefit does not only lie in automating existing processes, but in the ability to make better decisions and increasingly execute them autonomously. Companies that systematically apply predictive AI, generative AI and agentic AI can sustainably reduce capital commitment, strengthen liquidity and at the same time increase operational resilience. AI can therefore evolve from an analytics tool into a central steering mechanism for value-oriented working capital management. Successful implementation requires a coordinated combination of technology, operating model, governance body, and change management. This approach enables companies to build a working capital management system that meets their specific requirements and can scale as needs evolve.

Dommermuth, J. / Kröber, J. / Sennhenn, P.