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AI Automation ROI: A Framework That Finance Will Sign Off On
Nanostack1 min read
Move beyond vanity metrics. Use this step-by-step framework to quantify AI automation ROI with baseline costs, error rates, and payback periods.
Stop selling "efficiency" — sell dollars and risk reduction
CFOs approve projects with clear baselines. Start by measuring fully loaded cost per transaction today: labor, rework, SLA penalties, and compliance overhead.
Four numbers that matter
- Volume: Tasks per month (stable seasonality-adjusted).
- Touch time: Minutes of human effort per task.
- Error rate: Percent requiring rework × cost per rework.
- Cycle time: Revenue or cash impact of delays (where applicable).
Model the AI layer honestly
Include inference cost, monitoring, retraining/eval cycles, and a 15–20% contingency for edge cases. Most teams break even in 6–14 months on document-heavy workflows when error rates drop materially.
Need help building the business case?
Nanostack runs discovery workshops that produce a board-ready ROI model tied to your actual process maps — request a consultation.
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