Tracks
AI economics
ROI baselines, adoption economics, cost curves, and benchmark instrumentation for production AI programs.
Blog
4 implementation notes in this track.
The economics of production AI programs
Estimate value with baseline volume, cycle time, exception rate, adoption curve, and operating cost instead of generic AI claims.
10 min read
AI economicsBenchmarks that matter after AI go-live
Track resolution share, reviewer load, exception recovery, unit cost, latency, and quality deltas once the system is live.
8 min read
AI economicsAdoption-led AI economics
AI economics depends on adoption curves, reviewer load, and workflow coverage, not just automation potential in a spreadsheet.
8 min read
AI economicsUnit economics for agentic workflows
Agentic workflows need unit economics that include model spend, tool calls, review effort, exception handling, and avoided operating cost.
9 min read
Knowledge base
4 references in this track.
AI ROI model worksheet
Worksheet for estimating automation value from volume, cycle time, labor mix, quality lift, and operating cost.
AI economicsBenchmark instrumentation checklist
Checklist for proving production AI value through measurable throughput, quality, cost, and risk signals.
AI economicsAdoption ramp model
Model for forecasting and reviewing AI adoption by cohort, workflow class, enablement event, and abandonment signal.
AI economicsUnit economics control sheet
Control sheet for cost per completed outcome across model spend, tool calls, retrieval, review effort, exceptions, and support.