AI and the Future of Treasury

Optimizing Working Capital with Intelligence and Insight

Why AI Matters for Working Capital

Automation is unlocking a new level of agility and precision in working capital management.

Working capital rarely runs on autopilot. Treasury teams juggle liquidity monitoring, collections, and spending priorities, often across siloed systems that limit visibility and slow response times. The result? Fragmented dashboards and manual reconciliations that keep treasury reactive instead of strategic.

AI changes that. By unifying data across enterprise resource planning (ERP) and payment platforms, AI can automate repetitive tasks, anticipate cash shortfalls, and accelerate capital flow. More importantly, it frees treasury professionals to focus on high-value decisions, transforming working capital management from reactive to proactive.

Three AI Capabilities Driving Treasury Forward

From anticipating risk to recommending actions and executing them, these AI approaches are reshaping how treasury manages working capital.

1. Predictive AI

Predictive AI uses machine learning to analyze historical and real-time data, helping treasury anticipate what’s next. Common applications include:

  • Cash Forecasting: Conducts real-time, probability-weighted analysis to estimate buffers and hedging needs.
  • Receivables Optimization: Models payment behavior to refine credit and collection strategies.
  • Fraud Detection: Flags anomalies in payment patterns and suspicious activity.

2. Prescriptive AI

Prescriptive AI goes beyond prediction to recommend next steps. It compares historical trends with real-time metrics to balance liquidity, profitability, and risk. In practice, this supports:

  • Scenario Modeling: “What-if” simulations for funding or investment decisions. 
  • Strategic Recommendations: Tailored collection strategies or capital allocations. 
  • Resource Guidance: Operational adjustments to improve cash flow.

3. Agentic AI

Agentic AI combines predictive and prescriptive capabilities to execute predefined actions autonomously. Within treasury, this means:

  • Working Capital Optimization: Automated cash sweeps, payable prioritization, or credit line draws. 
  • Continuous Monitoring: Real-time adjustments to maintain liquidity targets. 
  • Principled Automation: Treasury-defined rules applied instantly to shifting conditions.

What AI-Powered Treasury Looks Like

AI brings together intelligence and execution to help treasury move at the pace of the business.

Imagine this: AI detects invoices trending four days late, calculates the liquidity cost, and recommends establishing a dedicated collections position for higher risk accounts, comparing staffing expenses to recovered cash. 

Meanwhile, agentic AI rebalances payables and sweeps excess cash in real time. The result? Faster decisions, stronger collaboration, and treasury teams focused on strategy, not spreadsheets.

Larger organizations are following more adoption and scaling best practices for gen AI deployment than are smaller organizations[1]

Dedicated Team to Drive Gen AI Adoption

52% - Firms > $500MM+ revenue

23% - Firms < $500MM+ revenue

Role-Based Capability Training Courses

31% - Firms > $500MM+ revenue

17% - Firms < $500MM+ revenue

Practical “No-Regrets” Steps for Treasurers Preparing for GenAI

Even if full-scale AI adoption is a year or two away, treasury leaders can take steps now to help determine readiness.

1. Build a Single, Quality Source of Truth

Identify where critical liquidity, payments, and forecasting data resides within ERP systems, bank portals, and spreadsheets. Consolidate fragmented data into a centralized repository with clear ownership, validation rules, and security protocols. AI thrives on clean, unified data.

2. Standardize Processes

Establish consistent workflows and controls for processes that you plan to automate. Structured processes make AI deployments smoother.

3. Upskill Your Team

Train treasury staff on data literacy, AI fundamentals, and the compliance considerations surrounding both. Human oversight remains integral for interpreting insights and setting guardrails.

The Bottom Line: Human Oversight Still Matters

AI amplifies treasury’s capabilities, but human judgment and governance remain essential.

Even the most advanced algorithms require human direction. AI can surface risks and optimize scenarios, but it can’t independently weigh regulatory obligations, ethical considerations, or enterprise priorities, which reinforces the need for strategic oversight. According to McKinsey, 28% of organizations assign AI governance to the CEO, and 17% to the board, underscoring the need for leadership involvement.

Without guardrails, automation could optimize liquidity at the expense of payroll obligations, compliance requirements, or other critical priorities. Human experience helps confirm forecasts are validated and decisions align with enterprise goals.

The future of treasury lies in balance: automation accelerates analysis, while human oversight helps facilitate control. AI is not a replacement, it’s a catalyst for smarter, more agile working capital decisions.