How AI Is Changing Cash Flow Forecasting for Manufacturers

Most manufacturers already have the numbers they need to see a cash shortfall coming. The hard part is how long it takes to turn those numbers into a forecast someone can act on. AI is shortening that gap, and cash flow forecasting for manufacturers is starting to look very different as a result.

During a recent live episode, Kevin Golden, CPA, shared his perspective on how AI is changing the way manufacturers forecast revenue, expenses, working capital and cash flow. The discussion highlighted a simple point: faster data only helps if it leads to better decisions.

Start With the Problem You Need to Solve

Manufacturing generates data everywhere, and AI can produce even more of it in seconds. Kevin’s first step is to decide what the data is supposed to help with before looking at any of it.

“What is the problem, right? So, if cash flow is a problem, great.”

He suggests a different question when comparing notes with other manufacturers. Instead of asking what metrics they track, ask the reason behind them. “More important question is, ‘Why are you looking at that?'”

From there, Kevin recommends working backward from the problem, pulling the data that matters and testing it on a weekly, monthly or quarterly cadence. If decisions aren’t improving, either the data isn’t being used in the right form or it’s the wrong set of data.

From Year-End Forecasts to Real-Time Updates

The biggest change Kevin sees is speed. Getting to key KPIs used to mean slow, manual processes. Now, manufacturers can feed their financials into AI and generate forecasts almost instantly. He stresses that this depends on having good infrastructure and a solid foundation in place first (“I can’t emphasize that enough”).

That speed changes how often leaders can look ahead. A manufacturer doesn’t have to wait until near year-end to project the following year. At any point, they can forecast one, two or five years down the road.

Compare the Forecast Against Itself

Kevin points to a second benefit: comparing today’s forecast to last month’s version of the same forecast.

“Where were we this time last month? Where do we expect we should be next month, right?”

Variables will change, he notes, but a faster turnaround on data helps teams become far more proactive.

Speed Is Part of a Good Decision

Kevin describes effective decision-making as a regular cadence of measuring and then checking whether things are actually improving. He’s realistic about the pace of that progress.

“These aren’t light switches we’re turning on and off, but are we making progress toward that?”

Timing matters just as much. “Effective decision-making isn’t just what am I doing with it, how quickly am I able to respond,” Kevin said. Working from data that’s a month or two out of date limits how fast anyone can react.

A Small Example of a 12-Month Projection

Kevin applies the same thinking to his own planning. He sets money aside each month for things like an annual family trip, income taxes and school tuition. He then uses AI to project those balances 12 months out and see when one will run thin.

The payoff is earlier planning. “I should probably cash flow plan for that 6 months out instead of 6 weeks out,” he said.

The scale is far smaller than a manufacturing operation, but the logic carries over. See the pressure point early enough to do something about it.

AI Shows You Where to Look

Kevin also cautions against treating AI output as the answer. In one case, a manufacturer’s margins were slipping. AI flagged the drop and pointed to accounts that could be responsible, but it couldn’t explain why.

The team went back to the invoices and found that charges from the waste company kept increasing. Those charges were based on weight, and the weight came from scrap. An internal process that wasn’t being followed was creating more scrap than normal.

“It’s going to tell you kind of where to look, but it’s not going to tell you the cure,” Kevin said.

Turning Faster Forecasts Into Better Decisions

Kevin expects financial leadership to become “less of a historian” and more focused on what comes next. For manufacturers, that means using faster forecasts to act months earlier while relying on people to explain what the numbers mean for operations.

Watch the full episode with Kevin Golden to hear more about how AI is changing forecasting and decision-making for manufacturers.

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