Imagine boarding a plane where the pilot says, “Good news: we have a new robot copilot. Bad news: it occasionally improvises.” You’d get off that plane immediately. That, in essence, is how many finance leaders feel about AI today.
Finance has always had a trust problem. Not because finance teams are cynical, but because they have to be cautious. When you are responsible for revenue recognition, quote-to-cash, and the numbers that ultimately guide a company’s decisions, you do not get to run on vibes. You need controls. You need consistency. You need a clear audit trail back to the source.
That’s why it’s not surprising that, according to our latest research conducted with the Harris Poll, a full 91% of finance and accounting decision-makers say they have concerns about using AI for core financial processes.
And those concerns are not abstract. They are painfully specific. The top concern is cybersecurity and data privacy. Right behind it is the lack of appropriate human oversight. Then comes the issue of data quality, integrity, and reliability.
In other words, finance leaders are not afraid of AI because it is new. They are afraid of AI because the stakes are high. A hallucination in a marketing brainstorm is awkward. A hallucination in a revenue workflow can be catastrophic.
This gets to the heart of the issue: trust really comes down to where the AI lives.
Until recently, most finance teams experimenting with AI were forced to use point solutions layered on top of their systems of record. Data gets exported, manipulated somewhere else, and then pushed back in. That may sound manageable, but it creates exactly the kind of gaps finance teams hate: broken audit trails, new integration risks, and more exposure around security and privacy.
These tools also tend to lack the full context of quote-to-cash. They do not really understand revenue rules, contract terms, or the operational logic of finance. They’re undifferentiated LLMs. They’re random. They are the finance equivalent of a Tinder date. No wonder trust has been so hard to earn!
So what does trustworthy AI look like in finance? It looks a lot less like a clever sidecar, and a lot more like intelligence embedded directly into the systems finance already uses. In fact, over half of finance and accounting decision-makers say they would trust AI features embedded in existing solutions most, far more than an internally built AI tool or a standalone AI-native solution.
That makes perfect sense. Trustworthy AI in finance should run on complete, real-time data from the system of record. It should inherit existing roles, permissions, and controls. It should produce outputs that are explainable and auditable all the way down to the underlying transaction. And it should operate on finance logic, not generic machine learning guesswork.
That is the opportunity in front of us. The future of AI in finance will not be built outside the system; it will be built within. No one wants a “creative” copilot.
