AI Performance Benchmarking
Most companies measure AI like SaaS. Logins and usage tell you nothing about whether it's working.
Typical ROI
2-4x in Year 1 (prevents underperforming AI spend)
Implementation
2-3 weeks
Industries
Any business running AI workflows — whether built by Steele Nash or another provider +
The Problem
The default way to measure AI is to look at adoption metrics borrowed from SaaS: active users, sessions, feature usage. These tell you nothing about whether the AI is accurate, whether it's improving over time, or whether it's actually saving money. Companies invest in AI workflows and then fly blind — no baseline, no accuracy tracking, no cost-per-task data. When something goes wrong, nobody knows until it's too late.
What We Build
Baseline measurement — Before or just after deployment, we establish your manual baseline: time per task, error rate, cost per unit of work. This is the number every AI metric is measured against.
AI-specific KPI framework — We define the metrics that actually matter for your workflows: straight-through processing rate, human override rate, accuracy vs. baseline, error reduction, and cost per automated task.
Dashboard build — A live dashboard is built on top of your existing tools — no new software to buy. Metrics update automatically as your workflows run.
Anomaly detection — AI monitors for performance degradation: rising override rates, accuracy drops, or processing slowdowns trigger alerts before they become problems.
Monthly performance review — We deliver a plain-English performance report each month: what's working, what's drifting, and what needs tuning. No vanity metrics.
Continuous improvement loop — Insights from the dashboard feed directly into workflow optimization. Underperforming steps are identified and refined on an ongoing basis.
Common Integrations
Example Scenario
A professional services firm deployed an AI document processing workflow 6 months ago with a vendor. Leadership assumes it's working because 'the team is using it.' We run a benchmarking engagement: baseline the manual process, instrument the AI workflow, and build a dashboard. What we find: straight-through processing rate is 58% — meaning nearly half of documents still require full manual handling. Human override rate has climbed from 12% to 31% over 4 months, indicating model drift. We surface the issue, retrain the extraction model, and get straight-through processing to 84%. The workflow goes from marginal to clearly ROI-positive — and leadership now has the data to prove it.
Ready to Build This for Your Business?
Book a free discovery call. We'll map your workflow and give you a concrete ROI projection.
Discuss This Workflow