MethodologyJanuary 2025 · 12 min read
AI Agent ROI Calculator Methodology
The model behind Nagent's ROI benchmarks — inputs, assumptions, calculation logic, and how to apply the framework to your specific workflows and team structure.
Key Insights
- The four ROI drivers: time saving, error reduction, throughput, and decision quality
- Why "hours saved" is incomplete — the compounding value of agent learning
- Industry-specific benchmarks for marketing, support, and operations
- How to build a CFO-ready business case for agentic AI
What's inside
01
ROI framework overview
The four categories of value agents generate — and how to measure each.
02
Input variables
Team size, task frequency, error rates, and workflow complexity parameters.
03
Benchmark data
Measured outcomes from 50+ enterprise deployments across industries.
04
Compounding value model
How KARMIC continuous learning changes the ROI curve over 12–36 months.
05
Building the business case
Framing for finance and executive audiences — with template language.
ROIFinanceBusiness CaseMethodology
AI Agent ROI Methodology
January 2025 · 12 min read