How to ship your first AI to production (without dying trying)

80% of AI POCs die before reaching production. Not from lack of technical talent, but from repeated process mistakes. Here are the 10 I see most often. Read them before starting your next project.

1. 1

   Pick ONE process. Not five.

   Mistake #1 I see: trying to automate 5 things in parallel. Result: 5 half-working things. Better to ship 1 that works.

   💡 Tip — Score each process by (frequency × pain × repetitiveness). Start with the top one.

2. 2

   Verify your data is usable. BEFORE coding.

   80% of AI POCs fail because the input is bad. Incomplete data, inconsistent formats, broken access. If the data isn't ready, no model will save you.

   💡 Tip — Before coding anything, export 100 real samples. Read them yourself. If you don't understand them, AI won't either.

3. 3

   Define ONE measurable success metric.

   "Improve the workflow" is not a metric. "Cut invoice processing time from 12 min to <2 min" is. Without a metric, no ROI and no clear decision to scale.

   💡 Tip — Metric = time, money, errors, or volume. Just one. Measure BEFORE starting (baseline) and weekly after.

4. 4

   Agent vs automation: depends on the decision.

   If the task always follows the same rules → automation (n8n, Make, Zapier). If it needs judgment or shifting context → AI agent. Confusing them = extra cost, zero value.

   💡 Tip — Ask: are there 2+ possible outputs for the same input? If yes → agent. If not → automation.

5. 5

   Design for failure from day 1.

   The LLM will hallucinate. The API will go down. Users will input garbage. If your agent only works in the happy path, it's not in production — it's a demo.

   💡 Tip — For each step in the flow: what happens if it fails? List 3 failure modes and their mitigation. Logging + retry + human fallback.

6. 6

   Explicit human-escalation rules.

   An agent that never says "I don't know" is lying. Client trust is built on the cases the agent does NOT solve, not on the ones it does.

   💡 Tip — Explicit confidence threshold (e.g. if answer has <85% confidence → escalate). Metric: escalation % should be 5-15%.

7. 7

   Test with real production data. No cheating.

   Synthetic data lies. Your team built test cases that pass. But real data has weird formats, errors, edge cases. If you don't test against it, you'll find the bugs at the client.

   💡 Tip — Take 200 real random cases from the last 30 days. Run them through your agent. Have a human expert review each output. That's your acceptance test.

8. 8

   Roll out gradually: 10% → 50% → 100%.

   Going 100% on day 1 is ego, not strategy. If something fails, you fail for everyone. And something always fails.

   💡 Tip — Week 1: 10% of traffic. Week 2: 50%. Week 3+: 100%. Compare metrics against control group at each step.

9. 9

   Document the human handoff.

   The agent will fail or escalate. The human picking up needs: context, what the agent did, what failed, what to do now. Without this, you lose 80% of the productivity gain.

   💡 Tip — Handoff template: 1 line of context, 3 lines of what the agent tried, 1 line of "suggested action". Attached to the ticket, not in another system.

10. 10

    Measure ROI weekly. If after 4 weeks it doesn't pay for itself, pivot or kill.

    AI projects die from inertia. "It's not working yet but someday...". 4 weeks is enough to see signal. If there's none, it's not "wait longer" — it's "change approach".

    💡 Tip — Weekly 30-min review: metric vs baseline, escalation %, anomalies. Binary decision end of week 4: continue / pivot / kill.