Excel vs. Python: Finance Leaders Clash Over AI Forecasting Tools at Industry Workshop

Excel vs. Python: Finance Leaders Clash Over AI Forecasting Tools at Industry Workshop

A fractional CFO's pointed question at an AI forecasting workshop last week has reignited a familiar debate in corporate finance: when does sophisticated technology actually solve problems, and when does it just create new ones?

The confrontation came during a live workshop run by AI Finance Club on February 19, after participants had spent the session building revenue prediction models in Python, complete with seasonal adjustments and backtesting. A fractional CFO raised his hand with what he called a "grumpy old man" objection: his three-statement financial models in Excel already did everything the Python exercises were attempting to accomplish.

His use case illustrated why the objection landed. When working with clients, he tweaks a single assumption—days sales outstanding, for instance—and immediately shows them the cash position impact six months out. The real-time visibility often prompts clients to pick up the phone and chase receivables on the spot. For that workflow, migrating to Python would add complexity without adding value.

But the workshop revealed a more nuanced reality than simple tool tribalism. Another participant manages forecasting for more than 90,000 active products, where the core challenge is accurate monthly cost of goods sold predictions. At that scale and complexity, Excel's manual processes become a liability rather than an asset.

The exchange exposed what AI Finance Club describes as a persistent pattern across finance teams: the tendency to select tools first and force-fit problems second. Two camps dominate the landscape. One group defaults to Excel for every scenario, even when forecasting across 50 product lines in 20 regions requires hundreds of copy-paste operations that compound error risk with each additional tab. The other chases whichever AI tool gains LinkedIn momentum, even when a transparent spreadsheet model would deliver faster, clearer results.

"If you talk to a data scientist or somebody who's very technical, then their solution is often going to be very technical," the workshop materials noted. "It may not be the correct solution." The data scientist gravitates toward Python because it's their domain expertise. The Excel expert stays in spreadsheets because that's their trust zone. Neither necessarily starts by diagnosing the actual problem.

The stakes extend beyond wasted hours. When finance leaders present forecasts built in the wrong tool—whether that's an opaque Python model to executives who need transparency, or a sprawling Excel workbook for problems requiring automation—they risk something more valuable than time: credibility. A black-box model that stakeholders can't interrogate, or a manual process that can't scale, both erode the trust that finance teams need to influence business decisions.

The workshop's implicit lesson wasn't about declaring a winner between Excel and Python. It was about matching tool capabilities to problem characteristics before writing a single formula or line of code. For the fractional CFO working one-on-one with clients who need immediate scenario visibility, Excel's simplicity is a feature, not a bug. For the finance team drowning in SKU-level forecasting complexity, Python's automation becomes essential infrastructure.

The question finance leaders should ask, according to the workshop's framing, isn't which tool is objectively better. It's which tool matches the specific problem they're solving right now—and whether they're honest enough to admit when their preferred hammer isn't the right tool for the nail in front of them.