By Mike Nader, Payments Dive
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AI is making it easier for finance to connect the dots, but better forecasting still depends on people who understand the business, tech executive Mike Nader writes.
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.