Finance Chiefs Discover AI Vendors Sold Them Software That Doesn't Exist Yet
The corporate finance world is confronting an uncomfortable truth: many AI tools marketed to CFOs over the past 18 months were sold based on capabilities the software couldn't actually deliver at the time of purchase.
This pattern—vendors demonstrating impressive AI features during sales cycles, then delivering far more limited functionality post-contract—has cost finance departments millions in wasted implementation costs and lost productivity, according to finance leadership networks tracking the trend. The issue has become significant enough that CFO Leadership Council, a membership organization of 2,500+ finance executives, is now addressing it as a priority concern among its community.
The problem stems from what industry insiders are calling "demo-to-delivery gap": AI vendors showcasing highly polished demonstrations of automated reconciliation, intelligent forecasting, or natural language financial analysis, only for buyers to discover the actual product requires extensive manual configuration, produces unreliable outputs, or simply lacks the demonstrated features entirely.
For finance teams already stretched thin, the consequences extend beyond the initial software investment. Implementation projects that vendors promised would take weeks have stretched into quarters. Finance staff who were supposed to be freed up for strategic work instead spend their time troubleshooting AI outputs or building workarounds. And the opportunity cost of delayed automation—in an environment where competitors may have found tools that actually work—compounds the financial damage.
The dynamic reveals a fundamental tension in the current AI market. Vendors face enormous pressure to ship AI-enabled products quickly, while the underlying technology often isn't mature enough to handle the precision and reliability that finance operations demand. A general ledger that's 95% accurate isn't 95% useful—it's a liability.
What makes this particularly galling for CFOs is the information asymmetry. During procurement, finance teams lack the technical expertise to distinguish between a working AI system and an impressive demo running on curated test data. By the time the gap becomes apparent, contracts are signed and implementation has begun.
The issue is forcing a broader reckoning about AI procurement practices in finance. Some organizations are now requiring proof-of-concept periods with their own data before signing contracts. Others are demanding contractual language that ties payment to specific, measurable AI performance metrics rather than feature lists.
For finance leaders evaluating AI investments, the lesson is clear: the burden of proof has shifted. The question is no longer whether a vendor can demonstrate impressive AI capabilities in a controlled setting, but whether they can deliver those capabilities reliably in the messy reality of production finance systems. And increasingly, CFOs are learning to wait for evidence before writing the check.
Responses (0 )