Measure and Improve the Business Impact of AI Workflows
An AI agent completing a task doesn’t tell you if the result was right, trusted, or valuable.
- 01 Is it working? Which AI workflows are producing value?
- 02 What's failing? Where are AI behavior and user outcomes breaking down?
- 03 Is it improving? Are fixes actually moving quality and business outcomes?
PROVE IT ON ONE WORKFLOW
Start with one production AI workflow and the outcomes it was built to achieve, for ex. completion rate, rework, escalations, or cost per successful task. DataFramer shows whether it's meeting them, why it isn't, and whether fixes improve the result.
A 4-week pilot, at no cost, against a target on the primary metric agreed before we start.
TEST & LEARN METHODOLOGY
01 Baseline Instrument the workflow and existing success metrics.
02 Find Uncover recurring failure patterns and poor outcomes.
03 Review Bring domain experts into the loop.
04 Improve Address priority failures and add checks to prevent recurrence.
05 Validate Measure quality and business outcomes again.
WHAT YOU GET
- Implementation & team enablement
- Executive outcome dashboard
- Prioritized failure analysis
- Expert-reviewed ground truth
- Reusable eval / regression assets
- Before-and-after impact readout
WHAT THIS LOOKS LIKE
Your outcome metric might be different
Workflow: support-ticket triage agent. Escalation rate fell from 19% to 12% of tickets over a 4-week pilot, measured against the pre-pilot baseline.
START THE PILOT
Start with one AI workflow
Establish a baseline, find what's breaking, improve it with your experts, and measure the impact.