The path from artificial intelligence (AI) pilot to impact follows a clear pattern with specific and actionable steps that treasury leaders can take, including:

Step 1: Audit the baseline.

Before selecting a use case, start with some diagnostic questions:

Where are treasury teams spending the most time? Look for high-volume, repetitive tasks such as payments monitoring, statement reconciliation, and transaction classification.
What decisions are slowing down the business? Look for bottlenecks such as forecasting delays, liquidity decisions based on incomplete data, or risk events that surface too late.
What data is available today? Inventory your core treasury systems—like the treasury management system, enterprise resource planning (ERP) system, bank connections, and data lakes—and assess whether the data is clean, complete, and timely.
What governance is already in place? Determine whether AI recommendations can be tracked, escalated, and reviewed properly by humans who are in the loop.

Step 2: Map the use case portfolio.

Use a simple value/effort matrix:

One axis should plot effort required, including data readiness, system integration, and change management.
The other should plot business value, including time saved, risk reduced, decisions improved, and revenue opportunity created.

This creates four broad categories:

High value & low effort. Start here, often with process-focused use cases.
High value & high effort. Pursue next, once foundational capabilities are in place to support analytics-driven use cases.
Low value & low effort. Deprioritize.
Low value & high effort. Avoid.

Step 3: Choose your pilot pathway based on your starting point.

If treasury is burdened by manual work and exceptions, start with process automation such as payments monitoring and bank statement monitoring. These use cases can create immediate capacity relief and build organizational confidence in AI.
- Typical timeline: 3–4 months
- Example success metric: Approximately 50% reduction in time spent on exception handling
If the challenge is forecast accuracy or decision velocity, start with analytics-driven use cases such as cash flow forecasting and liquidity analytics. These take longer to deploy, but they can create strategic differentiation.
- Typical timeline: 4–6 months
- Example success metric: Improved forecast accuracy, faster liquidity decisions, and the ability to answer "what-if" questions in hours rather than days
If treasury is already relatively mature (e.g., good processes, decent data, strong governance), consider end-to-end agentic workflows, such as autonomous cash management agents that monitor, analyze, and recommend funding decisions. This is still early-stage, but it may be appropriate for organizations with strong foundations.
- Typical timeline: 6–9 months
- Sample success metric: Approximately 80% of routine cash management decisions automated with human review; treasury team shifting to strategic capital structure decisions

Step 4: Design governance before deployment.

Define up front:

How much human review is required for each decision type?
What is the escalation path when AI flags a risk or falls outside normal parameters?
Who is accountable if an AI recommendation is wrong?
What audit trail must be maintained?

Build governance explicitly. Do not assume it will emerge organically once the pilot starts.

Step 5: Resource the team properly.

In addition to the AI tool itself, budget for data engineering, change management, treasury domain expertise, and skills development. The team needs enough training to understand what AI is doing and enough capacity to oversee it effectively. Many organizations underestimate this. AI projects that fail usually do so because the organization has not invested enough in the people and capabilities required to make it work.

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Getting Started: A Practical AI Roadmap for Treasury Leaders

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