In the previous unit, you mapped the employee journey and built personas that told you where AI intervention would outperform headcount — and where human judgment had to stay. Now comes the part where many teams stall: actually building and shipping. As Iris McQuillan-Grace walked through in the session, Oliver didn't spend months perfecting a bot behind closed doors. They assembled what they already had, gave it a persona and guardrails, tested it in rapid stages with stakeholders who were invited to break it, and launched it the same day as the process it was designed to support. The results — 19,000 feedback requests against an expectation of 3,000 — proved that speed and iteration beat waiting for perfect.

One of the most practical takeaways from Iris's session was how un-glamorous the bot's foundation was. Oliver's "Spybot" wasn't trained on proprietary datasets or custom-built models. Iris described feeding it "I would say, like 150-ish artifacts" — the organization's "mission, our vision, our values, all of our own IP around training, how to run a one-on-one, how to write" feedback — plus external data. As she put it, "We basically threw the kitchen sink at it." They chose the SBII feedback model as the bot's behavioral backbone (hence the name "Spybot") and then layered on "a series of constraints and considerations" to govern how it responded. The bot had a defined persona — not just instructions, but a character that shaped tone and boundaries. This matters because the artifacts you already have — your behavioral frameworks, training decks, values documents — are the raw material. You don't need new IP. You need to curate what exists, choose a model that aligns with how your organization already talks about the skill you're coaching, and set explicit constraints so the bot stays in its lane.