Key point
Many enterprise AI projects stall at PoC not because the model lacks capability, but because the demo validates "can AI do it" while the production system faces "can AI get it done long-term, stably, and safely".
Before pushing an AI project from demo to production, 71 SI recommends checking at least seven conditions:
1. A clear business task
Do not start from "we want to use AI"; instead define:
- ●Which part of the work does AI own?
- ●What is the input?
- ●What is the output?
- ●What counts as done?
- ●Who is accountable for the final result?
If the task itself has no boundary, even the best model cannot deliver results stably.
2. Does AI get enough scenario context
Real business judgment often relies on more than a prompt. AI also needs to understand: data · process · rules · roles · systems.
For example, handling a refund requires not only understanding what the customer said, but also knowing order status, refund rules, membership, current process, and operator permissions. Without this context, AI can answer questions but hardly get things done.
3. Can it connect to real business systems
Demos usually use static data. Production needs connections to: CRM, ERP, OA, order systems, inventory systems, databases, WeCom, and various internal APIs.
Only when AI can read real-time status and call systems to act does it truly move from "content generation" to "business execution".
4. Are permissions and boundaries clear
A production system must answer:
- ●What can AI read?
- ●What can it modify?
- ●Up to what amount can it execute?
- ●Which operations require human confirmation?
- ●How to roll back after an error?
Enterprise AI is not "the more capable the better"; capability must match the boundary of responsibility.
5. Is there a human fallback mechanism
Production inevitably has exceptions. Low confidence, high risk, rule conflicts, missing data—all should be able to enter human-in-the-loop.
A mature AI system is not one without any human; rather:
Let AI handle routine cases; let humans handle judgment, exceptions, and accountability.
6. Can the runtime be observed
After go-live, at least the following should be continuously visible:
- ●Task completion rate
- ●Error rate
- ●Human intervention rate
- ●Response and processing time
- ●Single-task cost
- ●Exception types
- ●User feedback
Without runtime data, you cannot tell whether the system is truly creating value or merely looking intelligent.
7. Is there a continuous optimization mechanism
Enterprise business keeps changing. Products change, prices change, policies change, processes change, and employee duties change too.
Therefore an AI system is not "train once, use forever" but should update continuously: context → rules → process → model → result feedback.
The benchmark from PoC to production
PoC can ask "can AI do it?"; a production system should ask "can AI finish this long-term, under real data, real rules, and real responsibility boundaries?" Between the two lies the layer most easily overlooked in enterprise AI adoption.