EuroFinance

Treasurers talk about AI-driven forecasting, but the groundwork required before AI can become genuinely useful should not be forgotten.

For many multinational companies, the immediate challenge is still achieving consistent visibility over cash positions, inflows and liquidity across multiple banking and ERP environments. Until that foundation exists, automation and forecasting improvements remain constrained by disconnected systems and manual processes.

That is the environment Ugo Prechner, vice president and treasurer at SLB, a global energy technology company, is focused on transforming and says treasurers already have access to significant amounts of financial data across the organisation. The difficulty lies in bringing it together into a single, connected view.

“We have a significant amount of data available, but not yet a fully unified platform,” he says.

For SLB, treasury manages both short-term liquidity oversight and a broader six-month outlook on corporate inflows and outflows. Centralised liquidity structures provide visibility and flexibility over cash usage, funding and liquidity allocation, but maintaining that visibility requires continuous monitoring.

“We have a six-month outlook on inflows and outflows to determine liquidity,” says Prechner. “This is continually monitored on a day-to-day basis for liquidity needs.”

Building a clearer picture of cash

Despite advances, achieving cash visibility remains complex.

Prechner explains that treasury teams need to consolidate bank statements, balances and payment information from multiple ERP environments to build an accurate picture of liquidity. Short-term forecasting depends on obtaining a clear view of payments and collections across the business.

Some of this process has become streamlined through automation tools and internal applications. But treasury workflows still require human oversight, particularly where bank connectivity is limited.

Why scenario testing matters more in volatile periods

The importance of visibility increases during periods of market or geopolitical disruption.

According to Prechner, volatility places particular pressure on scenario planning, sensitivity analysis and liquidity access. Customer collections can become less predictable, making forecasting harder to manage.

In those situations, a treasurer’s role goes beyond monitoring balances. Teams must assess how disruptions in one region could affect liquidity positions across the wider organisation and determine how funding, cash allocation and working capital strategies should adapt in response, he added.

Currently, much of that work relies on expert judgement, scenario analysis and ongoing review by treasury teams..

But this is where Prechner sees the long-term potential for AI and advanced analytics.

Rather than automating routine tasks, AI could allow treasury teams to model liquidity impacts in real time, identify sensitivities earlier and respond dynamically to changing conditions.

“In an ideal state, once data is consolidated and governed, advanced analytics and AI‑enabled tools can be used to explore scenarios on a single global platform,” he says.

The ability to immediately assess how a disruption affects liquidity and model potential responses across payments, collections and funding structures, could improve treasury decision-making during volatile periods.

AI starts with data readiness

While AI remains a major focus across treasury, Prechner stresses that implementation is not simply about deploying new tools.

“We are on a multi‑year journey to ensure the organisation is ready to deploy AI effectively.,” he says.

For many companies, the priority is building the infrastructure required to support AI effectively. That includes standardising data flows, improving system integration and strengthening governance frameworks.

“Success will ultimately depend on robust data integration, strong governance and effective change management.,” Prechner says.

That foundation is critical because treasury teams are increasingly looking beyond automation. The objective is to combine forecasting accuracy, real-time cash visibility and scenario analysis within a connected liquidity framework.

Prechner says one of the key priorities for improvement remains gaining a stronger view of corporate inflows and outflows alongside enhanced sensitivity analysis capabilities.

For treasury, the direction is becoming clearer. AI may ultimately reshape forecasting and liquidity management, but its value will depend on something more fundamental first: creating a unified, reliable view of cash across the organisation.

Ugo Prechner, vice president and treasurer at SLB, will be speaking at the EuroFinance’s 30th International Treasury & Cash Management Summit Miami  in May.  He will speak in the session “Cash management and forecasting: refining best practices with new AI technologies”, alongside Ouassila Sayd, SVP, head of cash management and payment at Siemens.