The Demo-to-Production Gap
Your AI demo works great in the meeting room. It impressed investors and got the team excited. But deploying it to real users? That's a different challenge entirely.
The gap between "it works on my laptop" and "it works reliably at scale" catches many teams off guard. Here's what you need to consider.
The Checklist
1. Reliability & Error Handling
Questions to answer:
- What happens when the AI model fails or times out?
- How do you handle edge cases the model wasn't trained on?
- Is there a fallback for when AI confidence is low?
Action items:
- ☐Implement graceful degradation
- ☐Set up model confidence thresholds
- ☐Create human escalation paths
- ☐Build retry logic with exponential backoff
2. Performance & Latency
Questions to answer:
- What's your latency budget?
- Can you batch requests or do you need real-time?
- How does performance change under load?
Action items:
- ☐Benchmark response times at expected load
- ☐Implement caching where appropriate
- ☐Consider async processing for non-urgent tasks
- ☐Set up auto-scaling if using cloud infrastructure
3. Cost Management
Questions to answer:
- What are your per-request costs?
- How do costs scale with usage?
- Where can you optimize without sacrificing quality?
Action items:
- ☐Calculate cost per user/transaction
- ☐Set up usage monitoring and alerts
- ☐Implement rate limiting
- ☐Consider model optimization or smaller models
4. Monitoring & Observability
Questions to answer:
- How do you know if the model is performing well?
- Can you detect model drift?
- What metrics matter most?
Action items:
- ☐Log all model inputs and outputs
- ☐Track accuracy metrics over time
- ☐Set up alerting for anomalies
- ☐Build dashboards for key metrics
5. Security & Privacy
Questions to answer:
- What data is being sent to external APIs?
- How do you handle sensitive information?
- Are you compliant with relevant regulations?
Action items:
- ☐Audit data flows
- ☐Implement PII detection and handling
- ☐Review vendor security practices
- ☐Document compliance measures
6. User Experience
Questions to answer:
- How do users know AI is working?
- What feedback do users need?
- How do you handle AI mistakes gracefully?
Action items:
- ☐Design loading states for AI operations
- ☐Create clear error messages
- ☐Build feedback mechanisms
- ☐Consider AI transparency requirements
The Bottom Line
Production AI is harder than demo AI. Plan for it from the start, and you'll save yourself significant pain later.
Need help getting your AI from demo to production? That's exactly what we do. Book a Discovery Call to discuss your project.