Why most finance teams are not ready for AI
The blocker is rarely the model. It is that the underlying process has no stable definition, no clean data lineage, and no owner who can approve a change.
AI does not fail in finance because the technology is immature. It fails because it is pointed at a process that is not yet described well enough for a human to hand off — let alone a machine.
Six conditions that must be true first
- 01The process has a written definition that matches what people actually do.
- 02The inputs arrive in a consistent structure, from a system of record, on a schedule.
- 03There is an accounting policy that resolves the judgement calls, not a habit.
- 04Someone owns the output and can approve a change to how it is produced.
- 05Exceptions are logged as exceptions, not absorbed as manual work.
- 06There is a baseline you can measure the improvement against.
Teams that skip these end up with a pilot that produces plausible answers nobody will sign. Teams that meet them find that half the value arrives before the model does, because describing the process honestly is itself the improvement.
Start with policy, not tooling
Adoption in controllership should begin with accounting policy. The policy is what makes an output defensible; the tool only makes it fast. Reverse the order and you scale a number nobody can explain.
You do not roll out AI in finance. You rep it in — one process, one control, one signed output at a time.