Why Real-Time Cash Is Making Spreadsheet Treasury Obsolete

Cash, today more than ever, is the lifeblood of the enterprise. But paradoxically, many businesses treat cash like it’s still mostly paper.

Even in 2026, today’s treasury teams rely on fragmented spreadsheets, delayed bank reporting, and static dashboards that provide a backward-looking view of liquidity to inform decisioning around whether the company can fund operations, seize opportunities, or weather volatility.

But the siloed history of treasury infrastructure is heading toward a full-speed collision with the single-source-of-truth needs modern businesses increasingly have. Spotting the trend-lines, vendors are more and more expanding their capabilities to offer integrated platforms that combine multibank connectivity, forecasting, payments and governance.

Even Apple on Tuesday (March 24) announced it was consolidating its enterprise offerings across Apple Business Connect, Apple Business Essentials and Apple Business Manager into one “Apple Business” platform.

The shift of centralization is being driven by a combination of better data, faster connectivity and advances in artificial intelligence. Together, those forces are reshaping how companies forecast cash, move money and think about risk.

See also: Why Companies Are Racing to Centralize Treasury Operations

What a Modern Treasury Setup Looks Like

In a world of increasing volatility, whether from interest rate fluctuations, supply chain disruptions, or geopolitical risk, liquidity management is becoming continuous rather than periodic for companies. As a result, the first-order advantages are frequently going to organizations that can see their cash positions clearly in order to predict what might come next and act quickly.

At a high level, the architecture of a modern treasury setup begins with data ingestion: multibank API feeds and ERP streams that provide continuous updates on balances, transactions, and operational drivers of cash flow.

This data flows into machine learning models that generate cash forecasts across entities, currencies and time horizons. The outputs are then fed into a policy engine, which evaluates potential actions against predefined rules and risk thresholds. Finally, execution is carried out through controlled workflows, with full audit trails and approvals.

The result is a closed-loop system: data informs forecasts, forecasts drive decisions, and decisions are executed and fed back into the system for continuous learning. Treasury becomes not just a consumer of information, but an active participant in shaping the company’s financial posture.

Read more: Can Your Treasury Function Put Money to Work Immediately?

Historically, these categories operated in silos. TMS platforms focused on cash positioning and risk management, ERPs handled transactional data, and FinTechs offered point solutions for payments or connectivity. Today, the boundaries are blurring.

For organizations looking to move in a forward direction, the path to a more agile treasury function is less about wholesale transformation and more about incremental, governed experimentation.

A logical starting point is the 13-week rolling cash forecast, a staple of treasury that can be significantly enhanced with better data and analytics. By systematically measuring forecast accuracy across different horizons and incorporating signals from accounts receivable, accounts payable and bank data, teams can build a baseline for improvement.

At the same time, investing in connectivity pays immediate dividends. Wiring in API-based bank feeds and ERP integrations provides the real-time data foundation required for more advanced use cases. In parallel, prioritizing ISO 20022 readiness ensures that this data is structured and actionable.

And by defining thresholds and rules for common actions such as auto-sweeps, investment ladders, or FX triggers, treasury can begin to standardize decision-making.

This does not remove humans from the process. Instead, it shifts their role. Treasury teams spend less time gathering data and more time setting policies, monitoring risk and overseeing exceptions. The work becomes more strategic.

The broader lesson is that modern treasury is not a single system, but an evolving capability. It requires alignment across technology, data and governance.

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