AI is here. Are you able to take full advantage of it?
No matter what team, industry, or role you’re in… it’s about having the right foundation—five specific prerequisites that most treasuries are missing. Get these wrong, and AI becomes an expensive disappointment. Get them right, and you’ll successfully transform your operations.
As Joe Barker, VP of Product Management at Trovata, explains, “Agentic AI is now on the radar of CFOs and CIOs who see its potential to automate decision-making and unlock value beyond treasury alone.”
In a recent Strategic Treasurer webinar, Trovata’s Director of Sales Engineering, Yussuf Ali, shared his insights on the importance of AI readiness, how you can leverage it in treasury, and the importance of getting the tech foundation right.
Read on to get a summary of his AI-readiness checklist or catch the full webinar recording here.
What Every Treasury Team Needs to Be AI-Ready
1. Real-Time, API-Based Connectivity
Do you have direct API connections with your banks?
If you’re still downloading CSV files each morning, you’re feeding AI yesterday’s news. As Bruce Edlund from Cloud Software Group discovered, “Getting transactions through APIs is way better. They just work better. They’re always on. The tech team loves it because they’re getting data from all our banks in one clean, consistent feed.”
Are you receiving enriched transaction data with metadata intact?
Here’s what the tech guides don’t tell you: AI needs context, not just numbers. For example, when EVERSANA connected their 50+ bank accounts via APIs, they preserved every data field—vendor names, payment references, BAI codes, and remittance details. This granular detail is what allows AI to distinguish between customer payments, vendor types, and even specific bank fees.
Why it matters: Clean, real-time data is essential for powering AI features like cash forecasting and anomaly detection. Without rich metadata, AI is just making educated guesses.
2. A Cloud-Native, Not Just Cloud-Hosted, Platform
Is your treasury platform truly built for the cloud?
Cloud-hosted systems are like moving your old house onto a new plot of land — it still has the same layout, the same plumbing quirks, and the same limitations, just in a different location.
Cloud-native platforms are like modern smart homes, built from the ground up with today’s materials and technology. They’re energy efficient, adapt to your needs automatically, expand as you grow, and update themselves continuously.
Can it scale without re-architecture or major upgrades?
When transaction volumes spike or you add new entities, does your system adapt automatically? Cloud-native platforms handle growth gracefully. Legacy systems require consultants, downtime, and budget approvals.
Why it matters: AI needs fast, modular, continuously updated environments to thrive. According to J.P. Morgan’s Future of Treasury Report, cloud migration returns $3.86 for every dollar spent, but only when it’s truly cloud-native.
3. Access to a Treasury Data Lake
Are you storing multi-bank data centrally with metadata preserved?
73% of enterprise data remains untapped for analytics. In treasury, that untapped data is AI’s fuel. A data lake isn’t just storage—it’s a centralized repository preserving everything in its raw, searchable format.
Can your platform search and analyze across historical data instantly?
Tim Green, Treasury Manager at EVERSANA, loaded 18 months of historical data when implementing Trovata, which is built with a data lake. This depth gave their forecasting models enough context to identify seasonal patterns. “Since implementing Trovata, we’ve seen far better accuracy in our cash forecast,” Green reports. “We’ve gained visibility into all of our accounts globally that we didn’t have before.”
Why it matters: Machine learning algorithms rely on consistent, high-quality historical data. Three months of data produces guesswork. Three years produces intelligence.
4. Cross-System Integrations
Can your platform push/pull data to ERPs and BI tools?
Your treasury data can’t live in isolation. When cash insights flow seamlessly into NetSuite or Power BI dashboards, AI’s impact multiplies across your organization.
Is your data formatted to support downstream reconciliation and forecasting?
Normalized data should be clean, consistent, and ready to power reports, models, and downstream integrations. If accounting spends hours reformatting your exports, you’re not AI-ready.
Why it matters: The real power emerges when AI orchestrates workflows across systems. Imagine AI detecting a cash shortfall, modeling solutions, and automatically alerting AP teams with recommendations—all without manual intervention.
5. Purpose-Built AI Tools (Not Just a Buzzword)
Have you identified the right AI tool for your needs?
Move over ChatGPT. If you have the first four items on this list covered, you’re ready to incorporate a purpose-built AI tool for finance and treasury, like Trovata AI. With all of your bank data in one place, you’re prepared to let AI do the heavy lifting.
Instead of just using Claude or ChatGPT, which are excellent tools, you can now access an AI assistant without having to leave your treasury platform.
When you ask Trovata AI a question, its generative AI technology taps into Trovata’s native capabilities within your private Trovata environment. In seconds, it serves up the information you’re looking for. Since Trovata aggregates, stores, and manages your bank data, the answers will always be accurate and private.
Does your AI cut through the hype?
Last but not least, actual AI readiness includes explainability, traceability, and control—not just an ‘AI-powered’ label. Ask vendors specific questions about their models, data protection, and audit capabilities.
Hard-Hitting AI Questions to Ask Your Vendors
“How does your AI handle data quality issues?”
- Good answer: Automated cleansing with quality scoring
- Red flag: “Your data needs to be perfect first.”
“Can we start small and expand?”
- Good answer: Modular approach with clear growth path
- Red flag: “Full implementation required.”
“Show me a failed forecast and how your AI learned from it.”
- Good answer: Detailed audit trail with model adjustments
- Red flag: “Our AI doesn’t fail.”
“How do you prevent AI bias in cash forecasting?”
- Good answer: Regular model validation and bias testing
- Red flag: Vague promises about “advanced algorithms”
“Can you customize AI assumptions and audit outcomes?”
- Good answer: Transparent audit trails with the ability to adjust assumptions and see why forecasts changed or anomaly flags were triggered.
- Red flag: Black-box AI where you can’t explain variances or review how results were calculated.
Your Next Steps
First, audit your current state. How far along are you on your AI-readiness journey? Can you check every box on this list? Take a moment to assess.
Second, prioritize ruthlessly. API connectivity and clean data matter more than fancy algorithms. Start with bank connections and data quality.
Finally, demand transparency. With 33% of enterprise applications expected to include agentic AI by 2028, you’ll face increasing pressure to adopt AI. Choose vendors who’ve built the foundation, not just the facade.
Again, Joe Barker captures the opportunity perfectly: “Agentic AI will reduce risk, improve decisions, and free up teams to focus on strategy instead of spreadsheets.”
Recommended: Why Agentic AI Isn’t Hype: Real Treasury Tasks It Will Soon Automate
Your treasury’s AI transformation doesn’t start with algorithms or models. It starts with APIs, clean data, and the right platform. Get those right, and AI becomes a powerful ally. Get them wrong, and it’s just an expensive disappointment.
The checklist is clear. The benefits are proven. The only question is: When will you start?
At Trovata, we’ve helped businesses put AI-powered treasury to action. Book a demo with Trovata to explore how our API-native, cloud-built platform provides the foundation your AI initiatives need.
