Artificial intelligence is everywhere. But for CFOs and treasury leaders, the question isn’t whether AI is coming, it’s whether the solutions being pitched are built for the work you do.

You’ve probably heard the promises about real-time forecasts, smarter liquidity modeling, and automation that takes work off your plate. On the surface, it all sounds impressive. But when you look closer, many of these so-called “AI-powered” solutions simply don’t hold up. They’re built on generic models, offer little to no auditability, and often force teams to give up control in exchange for convenience.

So how do you separate real innovation from packaged noise?

Here’s a straightforward framework to help you evaluate whether a treasury AI solution is truly ready for enterprise use, and what to insist on before you consider adoption in your financial operations.

1. Purpose-Built for CFOs and Treasury

The first question you should ask is simple: Was this AI designed for treasury and financial applications?

Many providers apply AI developed for marketing or operations and repurpose it for finance. But treasury isn’t a plug-and-play use case. It has unique data structures, regulatory expectations, and time-sensitive workflows. A solution that works for sales forecasting or predictive maintenance doesn’t automatically translate to cash flow modeling or FX risk.

Look for platforms where AI is embedded in the core of treasury operations, supporting decisions around liquidity, risk, forecasting, and reconciliation in ways that reflect how treasury teams’ work.

2. Transparent and Explainable

Accuracy is important, but in finance, explainability matters just as much.

You need to know why a forecast changed, what assumptions were used in a risk model, or how a liquidity recommendation was generated. If the system can’t show its work, or worse, if it relies on proprietary logic it won’t disclose, you’re taking on risk without realizing it.

This isn’t just about governance. It’s about credibility. If your CFO or auditor asks how a number was calculated, “the AI said so” isn’t a valid answer.

A strong treasury AI solution provides:

• Clear documentation of inputs and assumptions

• Visualizations that connect results to underlying data

• Audit trails that trace model activity

If that visibility isn’t available, keep looking.

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3. Aligned to Compliance and Security Standards

Treasury data is sensitive. It includes banking details, cash positions, exposures, and investment flows. Any AI that touches that data must be secure by design, not secure as an afterthought.

This means:

• Data residency and sovereignty controls (U.S., EU, APAC regions)

• No use of your data for model training

• Encryption at rest and in transit

• Isolation by client, with no cross-client data blending

Compliance frameworks like ISO/IEC 42001 and the upcoming EU AI Act are raising the bar for accountability. The AI you use should already meet or exceed those standards.

Don’t assume compliance. Ask for it.

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4. Inference-Only Models, Not Learning on the Job

This is a subtle but crucial detail: trustworthy treasury AI doesn’t continue to "learn" from your data unless you explicitly allow it.

Models should run in inference mode by default, which means they generate results based on your inputs but don’t retain that data to improve or adjust future outputs. That ensures your internal activity never becomes part of someone else’s model—or your own model’s future unpredictability.

If a vendor can’t confirm this or isn’t clear about how its models handle post-processing, you could be exposing your organization to risk down the line.

5. User Control and Configurability

Finally, you want AI that works for your team, not the other way around.

You should be able to:

• Turn features on and off at the user or business-unit level

• Adjust thresholds for recommendations and alerts

• Schedule or trigger AI insights based on your cadence, not the system’s

In other words, treasury should remain in the driver’s seat. AI should act as a co-pilot, not an autopilot you can’t steer.

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Red Flags to Watch For

Here are a few warning signs that an AI solution isn’t ready for treasury:

• It can’t show where its answers come from

• It uses public LLMs with no client data isolation

• It requires your data to improve its models

• It provides no way to audit, test, or validate results

• It wasn’t built with treasury-specific inputs or logic

Even if the vendor is trusted, even if the demo looks good, these issues should give you pause.

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What This Means for Your Next AI Evaluation

The pressure to adopt AI is real. Treasury teams are being asked to move faster, cut manual work, and deliver sharper insights with leaner teams. AI can absolutely help, but only if it’s built for your reality.

Before you commit, ask the questions. Read the fine print. Make sure the solution earns your trust, not just your attention.

That’s what good treasury AI looks like.  

Ready to evaluate AI tools with a sharper lens?
Use this framework as your starting point. And if you're looking for a treasury AI solution that delivers transparency control, and domain-specific intelligence, we'd be glad to show you how GSmart AI fits. Get in touch to learn more.

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