Finance Chiefs Discover AI Vendors Sold Them Vaporware—Again

The corporate finance world is having its "emperor has no clothes" moment with artificial intelligence, and it's happening in the most predictable way possible: quietly, expensively, and with nobody willing to admit they got sold a demo that never became a product.

Here's the pattern that's emerging across finance departments: A vendor shows up with a slick AI demo that automates invoice processing or cash forecasting. The CFO signs a six-figure contract. Six months later, the finance team is still manually reconciling spreadsheets while the "AI" sits unused, occasionally spitting out numbers so wildly wrong that someone has to babysit it full-time—which defeats the entire purpose.

The issue isn't that AI doesn't work. It's that the AI you saw in the sales demo and the AI that shows up in your production environment are two completely different products. The demo was trained on clean, curated data that someone spent weeks preparing. Your actual system gets fed the chaos of real-world general ledger data, complete with typos, duplicate entries, and that one subsidiary that's still using a different chart of accounts because nobody's gotten around to standardizing it.

Let me put it this way: Imagine buying a self-driving car based on a test drive through an empty parking lot, then being surprised when it can't handle rush hour traffic. That's essentially what's happening in finance departments right now, except the "car" cost $200,000 and your controller is threatening to quit.

The vendors, naturally, have a response ready. "Oh, well, the AI needs more training data." Or: "Your data quality isn't quite where it needs to be yet." Or my personal favorite: "This is actually working as designed—you just need to adjust your expectations about what 'automation' means." (Translation: You still have to do all the work, but now you get to do it while staring at a dashboard.)

What makes this particularly galling for finance leaders is that they're usually the ones telling other departments to be skeptical of vendor promises. CFOs are professionally trained to read the fine print, to ask about implementation timelines, to demand proof of ROI. But something about AI has short-circuited that instinct. Maybe it's FOMO. Maybe it's board pressure to "do something with AI." Maybe it's just exhausting to be the person saying "I don't think this is ready yet" when everyone else is drinking the Kool-Aid.

The real cost isn't just the software licenses—though those are painful enough. It's the opportunity cost of finance teams spending months trying to make a half-baked product work instead of focusing on actual value-add analysis. It's the credibility hit when the CFO has to go back to the board and explain why that AI initiative they championed isn't delivering results. It's the cynicism that sets in among finance staff who now assume every "AI-powered" tool is vaporware.

Here's what's actually happening behind the scenes: The AI works great on the vendor's standardized test cases. It falls apart the moment it encounters your company's specific quirks—the way you handle intercompany transactions, your revenue recognition policies, that weird adjustment you make every quarter for reasons nobody quite remembers but everyone knows you have to do. The vendors know this. They're just betting you'll blame yourself (or your data) rather than their product.

The pattern is so consistent it's almost funny. Finance team buys AI tool. AI tool requires massive data cleanup before it can function. Data cleanup takes six months and costs more than the software. AI tool finally goes live. It automates 60% of a process that still requires human review, meaning you've just added a new step to your close process rather than eliminating one. Finance team quietly shelves the tool and goes back to Excel.

What finance leaders need to demand—and what vendors need to start providing—is radically more transparency about what "AI-powered" actually means in production. Not the demo. Not the theoretical capability. The actual, day-one, with-your-messy-data performance. If a tool requires three months of data preparation and ongoing human oversight, that's fine—but say that upfront, and price it accordingly.

The alternative is what we're seeing now: a growing pile of expensive AI tools that promised to transform finance operations and instead transformed into very costly reminders that if something sounds too good to be true, it probably is. Even when it's powered by machine learning.