This article is written by ETR Digital
Businesses are moving from AI curiosity to real-world application. And with growing C-suite support, treasury teams are no longer sitting on the sidelines of this digital transformation.
According to Operationalising AI – The View from the Top, a recent report by Womble Bond Dickinson, 97% of CTOs say they’re already using AI. Over half (54%) cite data-driven decision-making as a core reason. These aren’t experiments anymore, they’re embedded deployments reshaping processes and financial systems in real time.
For Dominic Broom, CEO, ETR Digital – and contributor to the report – this shift is long overdue. “There’s a lot of talk about AI in the abstract, but what matters is how it’s being applied in the real world. Like other digital tools, operationalising AI means turning ‘potential’ into tangible, practical outcomes.”
He highlights three areas where this is already happening in treasury:
1. Making financing smarter and fairer
Legacy credit models often fail to reflect real business performance. With AI, financing decisions can become more dynamic and tailored – using live operational data rather than outdated financial statements.
“We now have the capability to be more bespoke and considered,” says Broom. “AI-driven real-time analytics will allow lenders to adjust risk models and costs accordingly, ensuring fairer financing and ultimately faster economic growth.”
This could open up more affordable credit for treasury teams, especially in sectors or regions where capital access has historically been uneven. In turn, more dynamic access to credit could enhance working capital planning, in particular for companies juggling uneven cash inflows or seasonal cycles.
AI can also help treasurers model real-time borrowing needs more accurately, reducing reliance on high-cost buffers and unlocking more flexible financing to support the business. As James Kelly, Co-Founder, Your Treasury, notes: “For decades, treasury teams have been stuck managing funding with tools that belong in a museum. AI gives us the chance to break out of backward-looking models and actually align to the real pulse of the business. If collections are accelerating or customer behaviour is shifting, funding decisions should reflect that.”
Dan Kindler, Co-Founder and CTO, Bound, adds: “There’s a huge opportunity here for treasury teams to benefit from decisions that actually reflect how their business is operating today – not just how it performed last quarter, or last year. But the key, as always, is having the right data in the right place, at the right time. Without clean, connected financial data, AI will be running on fumes.”
2. Removing friction from cross-border operations
Despite progress, cross-border transactions are still plagued by delays, documentation burdens, and red tape. AI offers new ways to automate, predict, and streamline these processes.
“AI will play a huge role in international trade by reducing complexity,” says Broom. “And businesses that trade internationally grow faster and are more profitable.”
From a treasury perspective, cross-border inefficiencies have a knock-on effect on working capital – whether that’s delayed collections, poor visibility over receivables, or mistimed settlements. With AI, treasurers can anticipate these issues earlier and reduce strain on the cash conversion cycle caused by avoidable delays.
Kindler elaborates: “One of the biggest hidden costs in international treasury is the time spent on ‘what-if’ scenarios. What if that payment is delayed? What if the FX rate shifts? What if the invoice is held up? With AI, you can stress-test those possibilities in seconds, not hours, and plan accordingly.”
And the cumulative benefit is huge, says Kelly. “Every friction point you remove adds up. A few minutes saved on reconciliation, a reduction in payment queries, fewer compliance chases – it all frees up time and headspace for better, deeper, more valuable work.”
3. Turning data overload into confident decisions
Many treasury teams are data-rich but insight-poor. AI changes that, helping to surface what matters, when it matters. As Broom notes: “We don’t have to rely solely on historical data anymore. Real-time data can now be analysed almost instantly to support faster, better decisions.”
After all, AI can turn huge volumes of data into valuable information, says Kelly. “But it’s not about building the smartest model. It’s about helping someone make a better decision on a weekday afternoon,” he explains. “Eliminate the noise and automate low value tasks, then highlight the strategic activities and free people up to focus on those areas. That’s how you move from firefighting to forward-thinking.”
Kindler also believes better decision-making is the real promise of AI in this space. “We’re using APIs and AI to integrate directly with our customers’ financial data – everything from ERP systems to payment platforms. This helps us automatically identify and quantify their FX risk in real time, so they can be more confident in their hedging strategies.”
He adds: “We don’t try to predict the future. That’s not the goal. What we do is give our customers a clear picture of what’s happening now – where the exposures sit, how sensitive their plans are to movements, and what they can do about it. That’s how AI unlocks confident decision-making for treasurers.”
Where do we go from here?
AI is no longer theoretical. It is increasingly embedded in how businesses and treasury departments operate. But its impact depends on how well it aligns with real problems – and how willing treasury teams are to rethink entrenched processes.
Kelly summarises it perfectly: “AI is being operationalised to make financial systems work better. That’s not a side project for treasurers, that’s the job! Now it’s up to them to decide where to apply AI first – and what outdated process they’re finally ready to retire.”
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