The Value

From forecast to action

A cash flow forecast is not the finish line. It is the starting line. The entire purpose is to identify risks and opportunities early enough to act on them.

This is where the real business value lives. And this is where most companies under-invest. They spend all their energy building the forecast and then struggle to extract actionable insights from it.

Excess cash

If your forecast shows surplus liquidity for the next three months, that cash should not sit in a zero-interest account. Money market funds, term deposits, or paying down a revolving credit facility — the right instrument depends on amount, duration, and your investment policy. An intelligent agent can analyse current money market rates across approved counterparties, check policy limits, and recommend a specific allocation. The treasurer reviews, approves, executes.

Cash shortages

If the forecast shows a funding gap in eight weeks, you have time to arrange a credit facility, draw on a revolving line, accelerate receivables, or delay payables. Eight weeks of lead time is the difference between a planned financing decision and a panic call to the bank.

FX exposure

For companies operating in multiple currencies, the cash flow forecast reveals currency mismatches. If you are forecasting EUR expenses and USD receipts, you have FX risk. Identifying it in the forecast means you can hedge it before it becomes an FX loss on your P&L. The forecast tells you the notional amount and the timing. The agent can recommend specific hedging instruments aligned with your hedging policy.

Early warning system

Most importantly, a cash flow forecast is an early indicator of business health. If a division’s cash flows are deteriorating — customer receipts dropping, supplier payments stretching — that is a signal. Management can act before it becomes a crisis. This is what turns treasury from a back-office function into a strategic partner.

Our Treasury Agent at NineAnts goes beyond simply flagging “you might have excess cash.” It analyses the size, duration, and investable amount, then translates that into concrete investment scenarios. It calculates FX hedging recommendations based on your forecasted exposures and your company’s hedging policy. It identifies negative trends in specific business units before they become visible in the P&L. Actionable recommendations, not just observations.

The flywheel: actuals vs. forecast

AI cash flow forecasting is not a one-time exercise. It is a flywheel — and the flywheel is what separates companies that get marginal value from AI from those that get transformational value.

Three things make the flywheel turn. Comparing actuals to what was forecasted. Keeping disciplined records of each forecast version. And comparing forecasts against each other. The next sections cover each in turn.

Actuals vs. forecast

Every day, new actuals come in. They are automatically compared against what was forecasted. Where was the forecast accurate? Where did it miss? By how much? And why? This is variance analysis — traditionally one of the most time-consuming tasks in treasury. Most teams do it monthly, if at all. An AI agent does it continuously, detecting accuracy gaps and significant deviations: a large capital expenditure that was never forecasted; a customer receipt that came in two weeks late. It identifies patterns in the forecast errors themselves: are you consistently overforecasting supplier payments, underforecasting tax obligations?

Comparing actuals to forecast across dozens of categories, multiple entities, and various time horizons requires enormous manual effort, which is why it is done so rarely.

These meta-patterns are surfaced to the treasurer as clear feedback to act on — adjusting assumptions, refining categories, or correcting source data so the next forecast cycle starts from a better place. Each cycle of forecast, actuals, analysis, and learning makes the next forecast better.

The model itself is not being retrained on your data. The improvement comes from the humans getting better, helped by the agent. The compounding effect is powerful.

Forecast versions

A cash flow forecast is not a single static document. It evolves. Tracking versions matters for two reasons. First, accountability: when the CFO asks “how has our outlook changed since last month?”, you need to pull up both versions and show exactly what changed and why. Second, accuracy measurement: a forecast made six weeks ago should be less accurate than one made two weeks ago for the same period. If it is not, something is wrong with your process.

It is a living process. Every week, new data comes in. Assumptions change. Subsidiaries update their inputs. The AI refines its predictions. Each of these moments creates a new version of the forecast. Without version history, every forecast exists in isolation, and you lose the ability to track how your understanding of the future evolves. Versions are the memory of the process.

How often should you create a new forecast version?

Cadence varies. Many teams version weekly — every Monday a fresh forecast incorporating the latest connected-system data. Others version monthly, aligned with the financial reporting cycle. Some create event-driven versions before a board meeting, a funding decision, or a major payment run. Each version is a snapshot — once finalised, it should be locked, so that when you look back at March 1st you see exactly what was forecasted on March 1st.

The cadence depends on your business, your reporting cycles, and how dynamic your cash flows are. There is no single right answer, but there are clear patterns.

• Weekly works well for companies with active, transaction-heavy businesses where the cash flow picture changes meaningfully from week to week. Every Monday, a new version is created. The latest data from connected systems is pulled in. The AI fills the gaps for the forecast horizon. The team reviews and adjusts. By Tuesday morning, the organisation has a fresh, up-to-date view of the coming weeks and months.
• Monthly versions are created at the start of each month, incorporating the previous month’s actuals and refreshing the forward-looking predictions. This is common in companies with more stable, predictable cash flows or in organisations where treasury operates on a monthly rhythm.
• Event-driven versions capture a point-in-time view that can be referenced later.

Locking preserves the integrity of each version for comparison purposes. If versions keep getting edited after creation, you lose the ability to track how your forecasting accuracy evolves.

In NineAnts, creating a new forecast version takes seconds. The agent can do it on a schedule, automatically pulling in the latest data from connected sources and generating AI forecasts for each category. Previous version data can optionally be carried forward as a starting point, so the team is not starting from scratch each time. Versions can be locked once finalised, and the agent tracks the full history for comparison.

Forecast vs. forecast

Forecast-vs-forecast analysis reveals something that actuals-vs-forecast cannot: the stability of your process itself. Are your forecasts converging as the period approaches? Healthy. Are they volatile right up until the last moment? Inputs are unreliable or your process is not capturing information early enough. It also reveals which categories are forecastable and which are not — and where to focus your improvement efforts.

Converging forecasts are a sign of a healthy process where each iteration adds information. If Customer Receipts are consistently stable across versions but Capex swings wildly, that tells you where to focus your improvement efforts. It might also tell you that Capex needs a fundamentally different forecasting approach, or that the business needs to provide better visibility into planned capital expenditures.

In NineAnts, every forecast is stored as a versioned snapshot. The agent can compare any two versions side by side, highlighting the differences by category and period. It tracks how forecasts evolve over time and surfaces insights about forecast stability that would take hours to produce manually.

Scenario analysis: what if?

A forecast tells you what is most likely to happen. Treasurers also need to understand what happens if things go differently. What if customer receipts drop by 20% next quarter? What if a major supplier changes payment terms from 30 to 60 days? What if EUR/USD moves 10% against you?

These are the questions that keep treasurers up at night, and they are exactly the questions that scenario analysis is designed to answer.

How agents make scenario analysis practical

Traditional scenario analysis meant rebuilding three versions in Excel: best, worst, base. By the time you finished the worst case, the base case was already outdated. An agentic system makes this practical: you describe the scenario in plain language or adjust a few parameters, and the agent recalculates the entire forecast accordingly. The most valuable scenarios are compound: lose a major customer and the EUR weakens 5%; supplier payments accelerate and tax obligations come due. Each has cascading effects across the forecast — losing a customer affects receipts but also the FX exposure profile.

Each of those Excel versions required manual adjustments across every category, every entity, and every time period. With an agent, it is different. Want to see what happens if Customer Receipts decline by 15%? The agent applies the adjustment across all relevant periods, recalculates your cash position, and immediately shows you the impact on your liquidity, funding needs, and investment capacity.

The most valuable scenarios are not just “what if revenue drops by X%.” They are compound scenarios that reflect real business situations:

• What if we lose our largest customer AND the EUR weakens by 5%?
• What if supplier payments accelerate because of tighter credit terms across the industry?
• What if we delay the planned acquisition by one quarter?

Accelerating supplier payments affects cash outflows but also changes the investment window. An agentic system can model these compound effects, giving treasury a much richer picture of their risk landscape.

In NineAnts, the agent supports scenario modelling where you can adjust forecasts by category or across the board, and instantly compare the scenario against the baseline. You see both the adjusted forecast and the original side by side, making it easy to quantify the impact of each scenario.