Why Treasury Needs AI Agents More Than Any Other Function

The case for skipping past automation and moving straight to autonomy.

When people talk about AI in finance, they often imagine it starting in the back office: accounts payable, expense reports, budget variance analysis.

But if you want to see where AI agents will have the biggest and most immediate impact, look at treasury.

Why? Because the stakes are higher, the complexity is deeper, and the workflows are perfectly misaligned with how traditional software works.

This isn’t just about streamlining tasks. It’s about upgrading how financial decisions happen under pressure.

Treasury lives at the intersection of risk, liquidity, and real-time execution

Few functions in a business carry as much cross-functional gravity as treasury:

• You’re tracking balances across 30+ accounts, in 10+ currencies, across 5+ entities.
• You’re managing short-term funding, FX exposure, and idle cash optimization - simultaneously.
• You’re handling reconciliation while preparing 13-week forecasts, while answering “what if we miss plan?” scenarios.

And you're likely doing all this with a team of… one or two.

This is the exact kind of environment that breaks traditional systems and where AI agents thrive.

Why spreadsheets and rules engines fall apart

Let’s take three of treasury’s core jobs: daily positioning, cash forecasting, and reconciliation.

Traditionally, each is handled with a combination of exports, templates, conditional logic, and domain expertise.

But here’s the catch: none of these tasks are actually rule-based.

• Positioning requires interpretation of yesterday’s flows + today’s expected events.
• Forecasting depends on judgment. Should that inflow be included, or is it at risk?
• Reconciliation requires pattern recognition, not just matching GL codes.

And yet, most tools treat these problems like math, not nuance. Which is why finance teams still spend 10-20 hours per week manually fixing what their systems can’t automate.

Where agents fit perfectly

AI agents flip the model.

Instead of waiting for a user to stitch together files and make a decision, agents:

• Connect to banks, ERPs, PSPs, and treasury systems in real time
• Maintain a clean internal model of cash, policies, exposures, and constraints
• Reason over that model to propose actions (not just reports)
• Route them for approval, then execute once cleared
• Improve over time based on feedback

Now imagine a Forecast Agent that adjusts predictions in real time as collections data comes in, a Liquidity Agent that rebalances idle cash or flags underutilized credit lines, or a Reconciliation Agent that tags and explains mismatches, no rules required

This isn’t just marginal improvement. This is a full-stack transformation of how treasury decisions get made.

Why treasury benefits more than any other function

Every part of finance has manual work. But few have the combination of:

• Real-time volatility (markets move, so do cash needs)
• System fragmentation (banks, ERPs, FX platforms, payroll)
• Strategic consequence (a bad decision on liquidity or hedging can cost millions)
• Exception-heavy workflows (no two forecasts or cash cycles are the same)

In AP, AI agents can save time.
In AR, they can speed collections.
But in treasury? They can fundamentally change the role from reactive to proactive.

And the impact is measurable.

This is already happening

Teams working with Nilus are already seeing it:

• 40-100 hours saved per month on reconciliation
• 30%+ improvements in forecast accuracy
• $1-2M unlocked from better cash allocation
• Internal users saying it “feels like we hired a full-time analyst”

And importantly, they’re getting these results without ripping out their existing stack. The agentic platforms leading this shift layer on top, augmenting current systems, not replacing them.

Treasury doesn’t need another dashboard. It needs a system that decides.

The old playbook of “collect, analyze, decide, and execute” is too slow for today’s liquidity environment.

Treasury doesn’t need faster reporting. It needs faster reasoning. And that’s exactly what agents deliver. If you're leading finance today, this isn’t just a nice-to-have. It's how you stay ahead of surprises, board pressure, and burn constraints.

AI is showing up across the enterprise, but treasury is where it will matter most.