Build the Pond Before You Teach Fishing: Killing Per-Person AI Tooling and Measuring What Replaced It

We stopped equipping humans and started automating their worst hour. Instead of teaching people to fish, we built the pond: precise background agents pointed at repeated, skill-independent time-sinks where ROI was measurable on day one. The first build was a bug-triage agent wired to GitHub, Jira, and Notion; every agent that followed was measured the same way — Time Saved Per Task (TSPT) per week — against an amortized cost model (build + inference + maintenance vs. hours saved × loaded hourly cost).

Structurally, the change was moving from a portfolio of per-person tool subscriptions to a portfolio of scoped, measurable background agents with a pre-build attribution conversation baked in. The decision rubric — Sharpness × Cadence × Skill Tax → Pond / Boat / Net / Wade — tells you when to automate fully, when to augment, when to keep a human in the loop, and when the math doesn’t work yet.

Attendees will leave able to: score their own candidate workflows on that rubric, instrument TSPT, build a defensible amortization story, and avoid the most expensive failure mode in AI enablement — “give everyone the tools” — by building the pond first.