Why AI Accuracy Isn’t Business Reliability
A polished AI answer can create more risk than value when a business needs work it can safely rely on.
Developed from a conversation between Pete Winn, Paul Itoi and Andy David
Paul argues that the useful test for enterprise AI is not whether a model can produce a correct answer, but whether its work is dependable enough to use. An error may not matter in a Blender demo or a home coding experiment. The standard changes when an attorney must file a memo in court or a car dealership must decide whether to refund the cost of a vehicle.
A 30-page legal memo makes the distinction concrete. It may look complete while leaving the lawyer with two radically different possibilities. The analysis could be sound, or it could contain invented citations and expose the lawyer to sanctions. If the memo arrives at 11 am for a noon deadline, asking the model to reflect on its answer does not make the uncertainty disappear. The lawyer still has to establish whether the work is safe to use.
That gap between occasional accuracy and required reliability helps explain why apparent AI capability does not always produce a return for businesses. If a person must inspect or redo the output closely enough to catch every consequential error, the finished-looking answer has shifted the work rather than completed it. Paul therefore values the visible reasoning and intermediate artefacts, such as the spreadsheets and inflation schedules behind a damages calculation, over a final figure that amounts to ‘trust me’.