Cost-of-Inaction Calculator
One wrong number from an AI used to get caught by an analyst. Now it ships at machine speed. Estimate your annual exposure — and see how small the cost of governing it is by comparison.
An illustrative range for the potential annual cost of ungoverned AI in your reporting — undetected error, restatement, exam finding, and remediation — composed transparently from published industry benchmarks. Not a prediction or a quote.
Illustrative only — built from published benchmarks below, scaled to your inputs. The governance figure is directional; exact pricing is on the pricing page.
Discuss your numberThis is a transparent, benchmark-anchored model — not a proprietary black box and not a prediction of your specific loss. It starts from a published incident cost, then scales it by how much ungoverned AI surface you have.
0.25×, Regional ≈ 0.6×, Large / national ≈ 1.3× — bigger balance sheets, bigger reporting surface, bigger downside.None 0.35× → 6+/agentic 1.4×. Agentic workflows score highest because they act with no analyst in the loop and often sit outside model risk (SR 11-7).Mature 0.45× → Ad hoc 1.5×. MIT Technology Review (2024) found governance/security/privacy is the #1 barrier (59%) to scaling AI — maturity here is the lever.+0.18× per regime selected (SOX restatement, CFPB/fair-lending, OCC/Fed exam findings, EU AI Act penalties).The composite drives an annual exposure range (we show roughly the −30% to +35% band around the central estimate, framed as low–high), a representative single-incident figure, and the ratio of annual exposure to a typical cost of governing it. Round numbers are intentional — this is for directional discussion, not a financial projection.
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