AI verification eats up 26% of finance teams’ workweek: Datarails

Finance teams are spending 26% of their workweek verifying or correcting AI outputs, which means AI is creating meaningful productivity drag even as adoption accelerates. For CIOs and technology leaders, the key implication is that scaling AI in finance cannot be treated as a feature rollout; it requires strong governance, auditable workflows, data quality controls, and human-in-the-loop processes to build trust and avoid compliance and accuracy risks. IT organizations should expect pressure to expand AI licenses while also being asked to prove control, reliability, and measurable business value before mission-critical deployment.