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Finance teams reasonably worry: "What if AI is wrong?"
This is the final piece in our series on why CFOs don't trust AI. We've explored expectations and communication but concerns about accuracy are more deep-seated. Finance teams stake their credibility on numbers they defend to boards and investors and getting conclusions wrong has significant consequences.
In traditional enterprise software, verifiability comes from determinism. The same inputs reliably produce the same outputs, and when something looks off, users can trace the logic step by step. Errors are debuggable after the fact, which gives teams confidence even when the process itself is tenuous.
AI systems work differently. They are not fully deterministic in their outputs, but they introduce a new kind of transparency. Instead of relying on a fixed chain of hard-coded rules, AI can expose the data sources it drew from, the calculations it performed, and the assumptions it applied. Users may not be able to predict the exact answer in advance, but they can interrogate how the answer was produced.
In one case, Sapien flagged a $12 million spreadsheet error that had gone unnoticed for years because it could identify patterns across millions of rows simultaneously. The finance team's initial reaction was that the AI must be wrong. But as they reviewed the system's work, they could examine which data sources, which calculations, and which assumptions the AI had employed.
They discovered a formula error in a spreadsheet one person had maintained for years. Nobody had detected it because everyone was too focused on individual processes to step back and look at the bigger picture.
The value of financial AI isn’t just accuracy, it’s verifiability. AI systems can be verified in ways that manual processes, even when technically traceable, functionally cannot at scale.
When you click on a number in Sapien, you see its full lineage. You can see which database table it came from, which calculation was performed, and which filters were applied. You can drill deeper into any component. The AI's reasoning process is exposed at every step, not just the final answer. This is possible because the AI isn't doing the calculations itself, it's using those verifiable tools we mentioned earlier. Each tool leaves a structured trace that can be displayed back to you in plain language.
This verifiability becomes clear in demos. For example, after someone asks about account penetration rates, the AI starts analyzing, pauses, realizes the "account" column refers to general ledger accounts rather than customer accounts as the question implied, finds the correct field, and adjusts accordingly.
If the system interpreted the question literally and used the wrong column, it would have generated an incorrect answer. But because users can observe what AI is doing in real-time, they can catch these moments. And typically, the system identifies the correct interpretations independently.
This is a trade-off that is hard to grasp without direct experience. AI cannot guarantee 100% accuracy. But it can explain and expose its reasoning in ways traditional software simply can’t. AI won't work identically every time but users can trust that it will show its work every time.
For financial operations specifically, verifiability matters more than deterministic accuracy. The critical question isn't just "Is this correct?" but instead "Can I defend this to the board and explain why it's true?"
This series has explored three interconnected trust barriers. Part 1 showed how expectations impact adoption as teams seek turnkey automation when AI actually delivers acceleration. Part 2 examined communication gaps as systems learn business-specific context and language. This piece addressed why verifiability matters more than deterministic accuracy.
As teams move beyond these barriers and begin to execute, it’s important to understand that the most successful implementations avoid attempting overnight transformation. They begin with one particularly painful process like management reports that take a week or analyses that demand dozens of data sources.
After AI automates one process, teams begin considering additional applications. Eventually, use cases emerge that nobody initially planned for.
Finance AI is at an inflection point, though not the one most people in the industry expect.
The technology works. The real shift is industry recognition that the features making AI intimidating (its flexibility, its learning requirements, its probabilistic nature) are exactly what make it a valuable tool for financial operations.
Finance teams who understand this are implementing AI that explains its reasoning, learns business-specific contexts, and starts with focused applications. They're building trust gradually.
Not despite AI's differences from traditional software, but because of them.
- Ron Nachum & The Sapien Team
This is Part 3 of a 3-part series on building trust in financial AI.
Link to Part 1 | Link to Part 2
