FIELD NOTE / LINKEDIN
Seat adoption is not AI ROI.
The short film, the complete written thought, and the evidence behind it.
The LinkedIn edition will be linked here after its public post is verified.
Seat adoption is not AI ROI.
Day 03 · 2026-09-30 · LinkedIn
Short video caption
Agent seats and draft volume are weak ROI measures. Most recovery is not hand recoding: it is re-prompting, retesting, redeploying and probing what else is wrong. Compare durable accepted outcomes per qualified owner hour for matched tasks. Management framework, not a prevalence estimate. #EricFieldNotes
Full written post / accessible read
A company can buy agents and watch code, plans and demos multiply. That does not tell leadership how much qualified attention it took to get each result accepted, deployed and still working later.
When an agent-built feature fails, the lead usually sends it back: clarify the requirement, rerun the agent, retest, redeploy, then inspect neighboring behavior. The hard cost is deciding what else may be wrong while other product decisions wait.
Choose one repeatable task class and prewrite its quality threshold. Record every owner intervention, test and deployment cycle, time to verified acceptance, unresolved risks and thirty-day reversals. Compare with the current workflow on matched work.
Add agent capacity when durable accepted outcomes per qualified owner hour improve without growing the decision backlog. Pause when repeated loops or reversals erase the gain. Do this because drafts are cheap; accountable delivery consumes scarce judgment.
#EricFieldNotes
Evidence and boundary
On-screen boundary: BUSINESS DECISION FRAMEWORK. The sources below support documented mechanisms and specifications; illustrative scenarios are not presented as measured incidents.