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CFOs aren't adopting AI because they don't trust it.
After nearly two years of deploying AI for financial operations, we’ve seen a clear pattern across the industry. Finance teams express genuine interest in AI’s capabilities but are hesitant to commit to implementation. The technology can analyze millions of transactions, identify patterns that escape manual review, and generate insights in seconds. But something consistently prevents finance teams from moving forward.
There are three key barriers to adoption. The first, and most fundamental, is expectations. Finance teams evaluate AI using frameworks developed for traditional enterprise software systems that are dated, predictable, and require minimal configuration. AI is probabilistic, adaptive, and continuously learning. This difference, although it requires some mental readjustment, is what makes the technology valuable for financial operations.
Finance teams approach AI with expectations shaped by experience with traditional enterprise software. They often expect end-to-end automation of BAU processes with minimal setup, using vague prompting to generate identical four-year forecasts from stale historical data or understand complex business hierarchies without providing full mappings.
These expectations reflect a misunderstanding of AI's value. Like human analysts, AI systems need substantial volumes of granular data to generate reliable insights. Once that data is provided, AI systems can begin to think like a human analyst.
Thus, the value of AI isn’t that it eliminates analytical work, but that it rapidly collapses timelines. Financial analyses that took two weeks can now be completed in hours, creating opportunities to explore the data and ask questions that weren’t practical before.
How does Sapien process data this quickly? It doesn't pull everything at once. Instead, it works with high-level summaries, descriptions of each table and column, and mapped relationships between them. When you ask a question, the system scans these summaries to figure out where to look, similarly to how a person might read table titles before clicking into them. But while a human can only process a few descriptions at a time, Sapien can examine thousands simultaneously. Then, Sapien writes optimized queries to pull only the data it needs. The math itself is simple. Sapien is adding, subtracting, and doing other basic operations. The complexity is in knowing what data to look at in the first place.
We worked with a finance team spending over 100 hours monthly on management reports because they were manually aggregating data across systems, building Excel models, and verifying calculations. The team started utilizing Sapien to conduct these processes and gained the expected time savings. Unexpectedly, with analysis suddenly taking minutes rather than days, the team began pursuing questions previously dismissed as too resource-intensive. They started breaking down data by individual SKUs rather than product categories, examining specific patterns across specific plants and time periods, and testing scenarios with a variety of data exclusions.
These capabilities technically existed before. Human teams could have done these analyses manually, but the effort made it impractical. When analysis takes minutes instead of days, exploration becomes feasible.
This enables different analytical approaches. Analysts who think geographically can instantly access regional breakdowns, product managers can examine category hierarchies, and operations teams can track time-series trends. Different people, with a range of different strengths and preferences, can engage with the data through lenses that match their preferred approach.
The judgment remains human. The speed of AI just unlocks approaches that weren't practical before. The expectations problem is solvable once teams recognize that AI’s value is radical acceleration of their existing analytical work, not turnkey automation.
But even as informed expectations begin to build trust, they’re not enough. The second barrier to adoption is communication. When finance teams don’t understand how to communicate with AI or how it interprets their queries, oversight can feel impossible. We'll explore that challenge in the next piece.
- Ron Nachum & the Sapien Team
This is Part 1 of a 3-part series on building trust in financial AI.
