Ten years ago, it would have been unusual to see a data and analytics leader reporting to the CFO. Today, that is much more common.

That is not an accident. It reflects how much the role of finance has changed.

Finance is still responsible for explaining what happened. That part has not gone away. But, increasingly, finance is also being asked to explain why it happened, what changed and what the business should do next.

The last point in particular represents a fundamental shift for finance, which historically has focused on reporting past performance with limited visibility into real-time business signals that could reshape the outlook.

Consider this example: At a manufacturing plant in Peoria, Illinois, a supplier misses a shipment of tubing needed for a packaging line. Production keeps running, but not at the rate the plan assumed. Throughput falls to 80% of forecast.

The operational problem is obvious inside the plant; the financial impact may not be obvious for weeks.

Finance will eventually see it. Revenue will come in lighter than expected. The forecast will miss. Someone at corporate will ask what happened.

The answer was sitting inside the business the entire time.

Finance has spent years getting very good at producing accurate, repeatable reporting. That work still matters. But the real value is no longer in producing another version of the same report. The value is in understanding what the numbers are telling you and how the business should respond.

Era of rapid change

Today, the need to recognize operational signals quickly is more critical than ever. Tariffs are changing cost structures. Supply disruptions are altering production plans. Shifting demand patterns are making the task of financial forecasting more difficult. A forecast that looked reasonable two weeks ago can become stale before the month is over.

The current environment gives finance a chance to rethink processes that were built for a time when every new question required another extract, another spreadsheet, another reconciliation, or another meeting. Many of those processes made sense when the cost of getting to detail was high. The question is whether they still make sense now.

This is why I think much of the conversation around artificial intelligence in finance misses the point.

AI does not magically create business insights from thin air. What it can do is help finance arrive at answers faster. But that only works if the underlying information is accessible and trusted.

It also requires judgment. That’s why I don’t get excited about someone who is great at building pivot tables. I can generate 10,000 Excel workbooks. That is not the scarce skill anymore. What I need is someone who understands the business.

The best forecasters I have worked with were not necessarily the people who spent the most time in Excel. They were the people who understood how the business actually operated.

They knew when a supplier issue was going to create a downstream problem. They knew when demand was behaving differently than it should. They knew when a number looked wrong because it did not match what they were hearing from customers, seeing in the operation or sensing from the market.

That judgment is becoming more important, not less.

AI’s impact in finance

The rise of AI has simply given finance more capacity to focus on higher-value work.

A lot of the routine work around data gathering, validation and report production can now be automated. That does not make finance professionals less valuable. It changes where they add value.

The differentiator is not who can build the most complex spreadsheet. The differentiator is who understands the business well enough to recognize when something is changing and what that change is likely to mean.

Finance has been very good at explaining what happened. The opportunity now is to understand why it happened while there is still time to act.