Check off what's already true about your business today. Eighteen questions, about two minutes, no email required to see your score.
AI agents are good at repeatable, well-scoped tasks with clear data access and a plan for when things go wrong. They're a poor fit for undocumented, judgment-heavy work with no oversight plan.
This checklist covers the six things that actually predict a successful first pilot: process repeatability, data and systems access, human oversight, risk tolerance, team readiness, and how you'll measure ROI.
"The businesses that get burned by AI pilots almost always skipped the oversight and measurement questions, not the technology questions."— Why we built this checklist
Your score and takeaway will appear here as you go.
Most successful pilots start narrow — one task, one system, cleaned up just enough to work. Perfect data across the whole business isn't the bar.
A human-in-the-loop agent that saves an hour a day is still a win. Full autonomy is a later milestone, not a starting requirement.
Most pilots need a few iterations to tune. Budgeting for 60-90 days of adjustment is normal, not a sign something's wrong.
Our focus is staffing and running the people and process side of your operation. If part of your gap is getting processes documented and data organized before a pilot, a modular pod or an EOR hire dedicated to that cleanup is often the practical first step.
Not necessarily — it usually means the groundwork (documentation, data access, an oversight plan) needs to happen first, not that automation is off the table long-term.
Something high-volume, rules-based, and low-risk if it makes a mistake — think first-pass triage or data entry, not final decisions on customer accounts.
Many clients pair a small human pod with a narrow automation pilot — the pod handles exceptions and oversight while the agent takes the repeatable volume.