AI in Production Workflows: From Demo to Dependable
AI in Production Workflows: From “Demo” to “Dependable” Many AI features work in demos but fail in production because the hard parts aren’t the model—they’re the workflow, data boundaries, and failure...
AI in Production Workflows: From “Demo” to “Dependable”
Many AI features work in demos but fail in production because the hard parts aren’t the model—they’re the workflow, data boundaries, and failure handling.
What production AI needs
- Clear inputs and outputs: what data the AI can see, and what it must produce.
- Tool-connected actions: AI should trigger verified actions (not just summaries).
- Fallback paths: what happens when AI confidence is low.
- Evaluation and monitoring: drift detection, quality checks, and feedback loops.
Patterns that work
1) Retrieval-first, then generation
Use search and retrieval to ground responses in your data. Use generation to explain or format.
2) Human-in-the-loop approvals
For high-impact actions (financial postings, redactions, approvals), require confirmation steps.
3) Tenant-specific boundaries
Multi-tenant systems must isolate learning and data access. The AI layer must respect those boundaries at the architecture level.
4) Measurable outcomes
Define the metric: time saved, reduction in errors, higher completion rates, faster support resolution.
The outcome
“AI in production” means predictable behavior under messy real data. The model matters—but the workflow design matters more.