Ownership model: the AI Reliability Lead

An evaluation system that works at launch can stop working six months later without anyone deciding anything. The judges never get recalibrated, the regression suite misses the new query types, nobody watches the dashboard, and the thresholds end up in a Slack thread. Each person assumed someone else had it.

The lesson starts with that audit on the AI Data Analyst. You predict what happened to a SQL judge’s false positive rate after the system moved to a new model, and work out why nobody can answer without recalibrating it.

Then you build the ownership model. Someone owns the data (the rows), someone owns what each metric means and whether it is measured correctly (the columns), and someone owns the decision to ship, ramp, hold or roll back. The AI Reliability Lead looks after the health of the whole evaluation system and tells the ship review how much the evidence can be trusted.

A RACI matrix writes the ownership down, with one Accountable owner per activity, never zero and never two. An evaluation debt register tracks the maintenance that did not happen, each item with a risk level, an owner and a target date. Escalation triggers set in advance what gets escalated, to whom and how fast.

The practice is an ownership model for your capstone: the lesson’s RACI extended with at least three new activities and one A on every row, at least five debt items from the audit and your own Week 2 to 5 work in a register, and at least three review types and three escalation triggers.