AI Production Readiness Checklist for Plant Leaders
An AI production readiness checklist built for plant leaders: 7 gates covering data, accuracy, ownership, failure modes, and ROI before you go live.
This AI production readiness checklist is the one I wish I'd had before I put the first agent in front of a production line at a $250M manufacturer. We had a working pilot, a happy demo, and an executive who wanted it live by month-end. What we didn't have was a single honest answer to "what happens when it's wrong at 2am on second shift?" That gap is where pilots become incidents.
Production readiness at a plant is not a software question. It's an operations question that happens to involve software. You already run readiness checks before you commission a new line: safety, capability, maintenance plan, operator training. An AI agent going into production needs the same rigor. Here are the seven gates, in the order I run them.
Gate 1: The number is defined and baselined
Before anything technical, you need the metric and the before-state. If the agent is supposed to cut quote turnaround, you measured current turnaround for at least two weeks. If it's flagging scrap, you have the current scrap rate by line and shift.
- Target metric named and tied to OEE, yield, OTD, or labor hours
- Baseline captured for 2+ weeks under normal conditions
- Success threshold written down (e.g., "reduce manual touches from 6 to 2")
- Break-even math done: cost of the agent vs. dollars recovered
No baseline, no go. You can't manage what you didn't measure, and finance will defund what you can't prove.
Gate 2: Data is production-grade, not demo-grade
The pilot probably ran on a clean export. Production runs on the real feed. Before go-live, confirm the agent has been tested against the actual mess.
- Tested on live production data, including nulls, dupes, and free-text fields
- Source systems documented (ERP, MES, SCADA, spreadsheets, email)
- Data refresh frequency matches the decision speed (real-time vs. nightly)
- Schema-change alerting in place, because someone will change a field
Gate 3: Accuracy is measured the way operators experience it
A 92% accuracy number is meaningless until you know what the 8% costs. Misclassifying a non-critical defect is a shrug. Missing a critical one ships bad product. Split your accuracy by consequence.
| Error type | Frequency | Cost per miss | Acceptable? |
|---|---|---|---|
| False positive (over-flag) | 6% | 4 min operator review | Yes |
| False negative (miss minor) | 1.5% | minor rework | Yes |
| False negative (miss critical) | 0.2% | escaped defect, recall risk | No — needs human gate |
If the expensive errors aren't rare enough, the agent runs in suggest mode with a human approving, not act mode, until it earns autonomy.
Gate 4: Failure modes are designed, not discovered
This is the gate plant leaders get and software teams forget. Every machine on your floor has a defined failure behavior. Your agent needs one too.
- What happens when the model is unsure? Define a confidence threshold that routes to a human.
- What happens when a source system goes down? The agent should fail safe and alert, not guess.
- What happens when output is obviously wrong? Operators need a one-click override and a way to flag it.
- What's the manual fallback? If the agent is offline, can the line still run? It must.
An agent with no defined failure mode isn't production-ready. It's an outage waiting for a trigger.
Gate 5: A named human owns it
Every production agent needs an owner with allocated hours, the same way every line has an owner. Not the integrator. Not "IT." A named person.
- Owner named, with 2-4 hours/week allocated for monitoring
- Weekly review of accuracy, exception rate, and override rate
- Escalation path defined for when metrics drift
- Retraining trigger defined (e.g., override rate above 20%)
Gate 6: Operators are trained and bought in
The best agent on the floor fails if the people next to it don't trust it. I've seen a perfectly good quality agent get ignored because nobody explained what it did or how to override it.
- Operators trained on what the agent does and doesn't do
- Override mechanism is one click and well understood
- Operators know how to flag bad output and see it gets acted on
- A skeptic on the floor has been walked through it (convert your loudest critic)
Gate 7: Monitoring and ROI tracking are live before launch
You don't commission a line and check on it next quarter. Same here. The dashboard goes live before the agent does.
- Live dashboard: accuracy, throughput, exception rate, uptime
- ROI tracked against the Gate 1 baseline, updated weekly
- Alerting on drift, downtime, and threshold breaches
- A 30-day review scheduled with finance to confirm the dollars
How to use this AI production readiness checklist
Run it as a gate, not a survey. Every item is pass/fail. Any fail at Gate 1, 2, or 4 is a hard stop, those are the ones that cause incidents and defunding. Gates 3, 5, 6, 7 can sometimes launch in suggest mode while you close them, but put a date on each.
The whole point is that production is a different animal than a pilot. The pilot proves the agent can work. This checklist proves it will keep working when the demo is over, the integrator's gone, and it's second shift on a Tuesday.
Get your agents ready faster
If you've got a pilot that demoed well and you're staring down this AI production readiness checklist wondering which gates you'll fail, we can help. Our free "First 5 Agents" teardown runs your top workflows against these seven gates and tells you, plainly, what's production-ready and what isn't. Book a 30-minute call and we'll pressure-test your readiness before you put anything in front of the line.
Let's see what's worth building first.
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