Everyone says they’re “using AI.” Ask them what’s in production. Watch the pause.
TL;DR: Production-ready AI means it runs without you. It handles failures. It’s monitored. It’s documented. Your team owns it. If you’re still babysitting prompts in a chat window, you’re demoing—not shipping.
The Demo Problem
Here’s how most AI projects go:
Week 1: Someone builds a demo. It’s impressive. Leadership gets excited.
Week 4: The demo is still a demo. It works when the person who built it runs it. It breaks when anyone else tries.
Week 12: The demo is abandoned. “We’re exploring other options.”
This pattern repeats constantly. 91% of mid-market companies say they’re using AI. Only 11% have anything in production.
That’s not an adoption gap. That’s a shipping gap.
The demo worked. The production system was never built.
What Production-Ready Actually Means
Production-ready has a specific definition. It’s not “works on my machine.” It’s not “impressive in the meeting.”
Production-ready means:
1. It runs without the person who built it.
If the demo only works when one person runs it, it’s not production-ready. Production systems run autonomously. They don’t need a human in the loop to function.
2. It handles failures gracefully.
AI models fail. APIs timeout. Inputs get weird. Production-ready systems anticipate this. They retry. They fallback. They alert. They don’t crash silently.
3. It’s monitored.
You know when it’s working. You know when it’s not. You have metrics on usage, latency, error rates, and output quality. You’re not guessing.
4. It’s documented.
Someone who didn’t build it can understand it. They can modify it. They can fix it. The knowledge isn’t locked in one person’s head.
5. Your team owns it.
After handover, your team runs it. Maintains it. Improves it. The vendor or consultant who built it has exited.
| Demo | Production-Ready |
|---|---|
| Works in a meeting | Works at 3am |
| Needs the builder present | Runs autonomously |
| Breaks on edge cases | Handles edge cases |
| No monitoring | Full observability |
| Tribal knowledge | Documentation |
| Vendor-dependent | Team-owned |
The gap between demo and production is enormous. Most companies underestimate it.
The Checklist
Before you call something production-ready, run through this:
Reliability
- It runs without human intervention
- It handles API failures and retries appropriately
- It has fallback behavior for model errors
- It’s been load-tested at expected scale
- There’s a rollback plan if it fails
Observability
- Usage metrics are tracked
- Error rates are monitored
- Latency is measured
- Output quality has some form of validation
- Alerts exist for critical failures
Security
- Data handling follows your policies
- API keys are properly secured
- Access controls are in place
- Logs don’t contain sensitive data
- Audit trail exists for compliance
Maintainability
- Documentation exists for how it works
- Documentation exists for how to modify it
- More than one person understands the system
- Dependencies are tracked and updatable
- There’s a process for prompt/model updates
Independence
- Your team can run it without vendor support
- Your team can fix common issues
- Training has been completed
- Runbooks exist for operations
- Knowledge transfer is documented
If you’re checking fewer than half of these, you have a demo—not a production system.
Where Most Companies Get Stuck
Three failure modes kill AI projects:
1. The Demo Trap
The demo gets buy-in. Then everyone moves on to the next demo. Nobody does the unglamorous work of making it production-ready. It’s not exciting. It doesn’t look good in a presentation. But it’s where the value is.
2. The Skills Gap
Building a demo requires different skills than shipping to production. The person who prompts well in ChatGPT isn’t necessarily the person who builds reliable systems. Production requires engineering discipline: error handling, monitoring, testing, documentation.
3. The Handover Failure
A consultant builds something impressive. They leave. Nobody on your team knows how to maintain it. The system degrades. Within six months, it’s abandoned. All three failures have the same root cause: treating AI like a magic trick instead of like software. AI systems are software. They need the same rigor. They need the same discipline. They need the same investment in operations.
The 100% Threshold
Here’s something most people miss:
There’s a 10x difference between 90% AI adoption and 100% AI adoption. If 10% of your team is using traditional methods, everyone leans back into those methods. The integration points become manual. The handoffs require human translation. The benefits don’t compound. Production-ready AI isn’t about one system. It’s about organizational capability. Your team knows how to ship AI. They do it repeatedly. Each project is easier than the last. That’s the goal. Not one impressive demo. Repeatable capability.
First Thing Tomorrow
Take your most promising AI project. The one that’s “almost ready” or “working in pilot.”
Run it through the checklist.
- Score it honestly. How many checkboxes can you tick? Be realistic.
- Identify the biggest gap. Is it reliability? Monitoring? Documentation? Independence? Find the category where you’re weakest.
- Make a plan for that gap. Not for everything. Just for the biggest weakness. What would it take to close it?
- Set a deadline. When will this move from “pilot” to “production”? Put a date on it. Vague timelines are where demos go to die.
- Define ownership. Who runs this after it’s shipped? Name the person. Make sure they know.
The checklist isn’t meant to slow you down. It’s meant to ensure that when you ship, it stays shipped.
The Bottom Line
Production-ready AI means it runs without you. It handles failures. It’s monitored. It’s documented. Your team owns it. If your AI project needs the person who built it to function, you have a demo. Demos are fine for getting buy-in. They’re not fine for getting value. Close the gap.
Need help getting from demo to production? That’s the gap we close. Not more demos—systems that run. Let’s talk.