A successful AI pilot does not automatically mean the use case is ready for production. Production must work with real data, connect to real systems and follow your organisation’s security rules.
Check common production blockers
- Data: Is the information accurate, current and owned by someone? Does each customer’s data remain separate?
- System access: Are AI connections limited to what they need, and are actions logged and reversible?
- Human review: Do people approve risky actions before they happen?
- Testing and release: Can you run quality checks on changes before customers see them?
- Security and sign-off: Can reviewers see where data goes, what AI can change and who is accountable?
- Cost and performance: Can you track cost per customer and feature, and set budgets or alerts?
What to do next
Use these questions to identify gaps between the pilot and the production environment. The Knowledge Elephant’s AI readiness assessment reviews these areas and provides ranked risks, target architecture and a 90-day roadmap.
Troubleshooting: the demo used sample data
Check how the use case behaves with the real information and system connections it will need in production. Also confirm that data quality, customer data separation and review controls are addressed before launch.