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Compliance ROI is the half of the AI investment case that usually gets left off the slide. The cost of audits, evidence gathering, and regulatory response is real β€” and automating it has measurable financial impact.

What this captures

The net is (audit_saved + risk_mitigation_value) βˆ’ continuous_control_cost.

Audit labour saved

For each evidence type, Observatory tracks:
  • Number of automated-evidence records produced
  • Estimated manual-collection hours that would have been needed
  • Loaded hourly rate for the role that would have collected them
Multiplied together, this is the audit-labour saved per period. Default assumptions (configurable):

Risk mitigation value

Compliance failures have expected losses. The dashboard estimates two numbers:
  • Expected loss before Observatory β€” based on framework severity and your risk-tier mix
  • Expected loss after Observatory β€” same calculation with current gap status
The difference is the risk mitigation value attributed to Observatory’s compliance work.
Risk mitigation is the most assumption-heavy part of the calculation. Show it alongside the audit-labour number β€” the latter is closer to ground truth.

Where this rolls up

Compliance ROI is one of the inputs to the per-project Financial ROI. For projects where compliance work is substantial (banking, insurance, healthcare), it’s typically 15–30% of the total ROI case.

Reporting

Two reports executives ask for:
  • Quarterly audit-labour saved β€” the audit-hours chart broken out by framework
  • Compliance-risk exposure trend β€” month-over-month change in expected loss
Both export to PDF and CSV from ROI β†’ Compliance.

Evidence

Where the audit-labour numbers come from.

Compliance

The frameworks that frame the risk-mitigation calculation.
Last modified on June 1, 2026