The demo went great. Leadership was impressed. Budget was approved.
Six months later, the project is still "almost ready."
This happens to most AI projects. According to Gartner, only 53% of AI projects make it from prototype to production. My experience suggests that's optimistic.
Here's why AI projects get stuck—and how to break free.
The Demo Trap
Demos are seductive. In a controlled environment with curated data, AI looks magical. But demos hide the problems:
| Demo | Production |
|---|---|
| 50 test queries | 50,000 queries/day |
| Handpicked examples | Adversarial users |
| "It mostly works" | "It must always work" |
| Developer testing | Real customers |
| Minutes of latency okay | Seconds matter |
The gap between "works in demo" and "works in production" is where projects die.
The Five Killers
1. Edge Cases Multiply Exponentially
Your demo covered 20 scenarios. Production has 2,000. Each edge case seems small, but they compound:
- •User asks in a different language
- •Input contains special characters
- •Request times out mid-response
- •User uploads a 50MB file
- •Two users ask the same question simultaneously
You can't anticipate all edge cases. But you can build systems that fail gracefully when they occur.
2. The "One More Feature" Loop
After the demo, stakeholders have ideas:
- •"Can it also handle X?"
- •"What if we added Y?"
- •"It should integrate with Z."
Each addition seems reasonable. But scope creep kills more AI projects than technical challenges. The project that does one thing well ships. The project that does ten things poorly doesn't.
3. Data Quality Isn't a One-Time Fix
The demo used clean, curated data. Production data is messy:
- •Inconsistent formats
- •Missing fields
- •Duplicates
- •Stale information
- •Contradictory sources
Data quality isn't a problem you solve once. It's an ongoing operational concern that needs dedicated attention.
4. Latency Compounds
In a demo, 5-second response times are fine. In production, they're not.
But it's not just the AI call. It's:
- •Network latency to your API
- •Database queries for context
- •LLM inference time
- •Response processing
- •Network latency back to client
Each step adds 100-500ms. By the time you're done, you're at 10 seconds. Users leave.
5. Nobody Owns It
Demos are built by excited engineers. Production systems need:
- •On-call rotations
- •Monitoring and alerting
- •Incident response procedures
- •Documentation
- •Ongoing maintenance
Without clear ownership, production systems decay. And AI systems decay faster than traditional software because models change, APIs deprecate, and data drifts.
How to Actually Ship
Strategy 1: Shrink the Scope Ruthlessly
Your AI doesn't need to solve every problem. Find the smallest useful capability and ship that.
Instead of: "AI assistant that handles all customer inquiries" Ship: "AI that answers questions about order status"
The smaller scope ships faster and teaches you what production actually requires.
Strategy 2: Build for Day 2
Day 1 is launch. Day 2 is when things break.
Build from the start:
- •Logging for every request/response
- •Metrics for latency, error rates, token usage
- •Fallback paths when AI fails
- •Easy rollback mechanisms
These aren't nice-to-haves. They're how you survive production.
Strategy 3: Ship to Internal Users First
Your production environment isn't production until real users touch it. But those users don't have to be customers.
Ship internally first:
- •Customer support team uses the AI tool
- •Sales team tests the demo
- •Engineering tests edge cases
Internal users find bugs without hurting customers. They also become advocates when you ship externally.
Strategy 4: Set a Ship Date and Cut Scope to Meet It
Parkinson's Law: work expands to fill the time available. Without a hard deadline, projects drift.
Pick a date. When you realize you can't ship everything by that date, cut features—don't move the date. You can always add features after launch.
Strategy 5: Accept Imperfection
Your AI will make mistakes in production. That's okay if:
- •Users can report errors
- •Errors are logged and reviewed
- •Critical paths have human fallback
- •You iterate based on real usage
Waiting for perfection means never shipping. Ship, learn, improve.
A Real Example
One of my projects was stuck in demo hell for months. The AI was "95% accurate" but that 5% was unacceptable for production.
The solution: Human-in-the-loop for uncertain cases.
When the AI's confidence was below a threshold, it flagged for human review instead of guessing. This let us ship with 100% "accuracy" (humans caught the errors) while the AI handled 80% of volume automatically.
Within a month of production usage, we had enough data to improve the model. The 5% error rate dropped to 2%. We never would have collected that data without shipping.
The Only Metric That Matters
Is it in production?
Not "is it accurate?" Not "is it feature-complete?" Not "is it optimized?"
Is it in production, handling real requests, from real users?
Everything else is a distraction until you can answer "yes."
Stuck in demo hell? Let's figure out how to ship.