Most AI training programs produce decks. This one produces running automations. The Bootcamp is built on the premise that the fastest way to develop internal AI champions is to have them build and ship something real during the training itself — not in a sandbox, not as a prototype, but in production against their actual HubSpot or Salesforce stack. Two weeks. Two or three automations live. A runbook the ops team owns on day fifteen.
We scope the three highest-ROI workflows before the first session, build in public with the ops team, and hand off with monitoring and rollback procedures in place. The honest question to ask beforehand: what does the ops team do with the freed-up time? That question deserves an answer before the Bootcamp starts, because the systems only compound if someone is steering them. The Bootcamp delivers the capability and the first backlog; the team’s appetite for iteration determines what happens next.
The two-week shape is deliberate. Day one and two: kickoff, KPI baseline, and workflow selection — we confirm which two or three automations have the clearest ROI and the lowest data risk, and we document the current manual state so there’s a before to compare against. Days three through seven: build sessions with the ops team present and contributing, not watching. Each session ends with a working component in the stack, not a plan for one. Week two begins with QA and a controlled deployment against live data. Days ten and eleven: the team runs the systems themselves with us in the room. Day twelve: runbook review, monitoring walkthrough, rollback drill. Day fourteen: handover and the automation backlog — the next ten items ranked by effort and expected ROI, ready for the team to sequence without us.
The change-management pattern in a two-week engagement is compressed but real. The people who resist are usually not opposed to automation — they’re worried they’ll be the one blamed when something breaks. Building in public addresses part of that: when the ops lead has seen every step of the build, the system stops being a black box and starts being something they understand well enough to troubleshoot. We also name the internal champion explicitly during the Bootcamp — not as a title but as a responsibility. That person gets the monitoring alerts, owns the runbook, and is the first call when the automation does something unexpected. The freed-up time conversation happens on day two, before anyone has seen the automations run, because the answer needs to come from the team rather than from us.
The Bootcamp ends at two weeks, but what that means in practice is that our active build role ends. The monitoring is live, the runbook is written, and the rollback procedures are tested. If something breaks in week three, the team has the tools to diagnose it; if they want a second pair of eyes, we’re reachable. Some clients run a second Bootcamp three months later to work through the backlog we handed off. Others use it as the starting point for a longer RevOps OS engagement. The hard stop is a feature, not a bug — it forces the documentation to be complete enough that someone else can own it.
Realistic outcomes: two to three automations in production at day fifteen, speed-to-lead below five minutes in the scoped workflows, measurable reduction in manual ops time for the specific tasks the automations replaced. Meeting conversion lifts are common but not guaranteed — they depend on offer quality and lead quality upstream of the automation. What we won’t promise is that the whole ops team changes how they work in two weeks. The people who were in the build sessions tend to adopt quickly. Others on the team adopt more slowly as they see the systems running reliably over the following weeks. That lag is normal. The Bootcamp creates the capable core; the core gradually shifts the surrounding practice.