TL;DR: An AI proof of concept shows that a model can do the task on good examples with a friendly user. Production has to show it does the task every day, for every user, within budget, with someone accountable for each answer. The work in between is engineering: evaluation sets, guardrails, permissions, audit logs, cost control, and availability when the model provider slows down or fails. With a clear scope, most teams can cross that gap in about 90 days. A production AI system with Globalbit typically costs $50K-$150K.
Why AI POCs stall after a good demo
Gartner saw this coming. In July 2024 it predicted that at least 30% of generative AI projects would be dropped after the proof of concept by the end of 2025, and blamed poor data quality, inadequate risk controls, escalating costs and unclear business value. In June 2025 it predicted that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls.
Every one of those reasons is something a demo hides.
A typical POC runs on 50 examples someone picked by hand. One enthusiastic user tries it. It reads every document in the folder, because nobody set up permissions. Nobody counted the cost per call. And the model provider never went down during the demo.
Then the pilot group gets access. Real users ask questions nobody expected. Legal asks who approved an answer. Finance asks why the monthly bill tripled. The CISO asks where the data goes. The project stops while the team scrambles for answers it should have planned for.
If you are still at the POC stage, start with how to run a POC that answers the right question. This article covers what comes after.
The production checklist
| Area | What the POC skipped | What production needs |
|---|---|---|
| Quality | A few good examples | An evaluation set of real cases, with a pass bar agreed before launch |
| Guardrails | Trust in the prompt | Input and output checks, limits on what the AI may do |
| Permissions | One shared key that sees everything | Each user sees only the data they may see |
| Audit | Nothing | A log of every request, answer, source and action |
| Human review | The developer checks | A review queue for low-confidence or high-impact answers |
| Cost | Ignored | A cost per request, budgets and alerts |
| Latency | "It answered" | A target response time per use case |
| Availability | One provider, one region | Fallback, retries, queues and a plan for outages |
| Monitoring | Nothing | Quality, cost and drift tracked every week |
Build the evaluation set first
Collect a few hundred real cases with the correct answer, including the awkward ones. Agree with the business owner what score is good enough to launch. Every change to a prompt, model or data source runs against this set before it ships. Without it, every discussion about quality becomes a matter of opinion.
Guardrails, permissions and audit
The OWASP Top 10 for LLM applications lists prompt injection as risk number one and "excessive agency" as risk number six. In practice, the fix is architecture. The AI acts with the permissions of the user who asked. Retrieval returns only documents that user may open. Any action that changes data goes through a narrow, checked function. Every request and answer lands in a log your security team can read.
We described one full security design, with seven layers from rate limiting to CSRF protection, in how we built an AI chat that passed security review.
Human review where it matters
Decide which answers go straight to the user and which go to a person first. Low-confidence answers, customer-facing messages and anything that moves money usually wait for review. As the system proves itself, you widen what passes automatically.
Cost per request
Measure the tokens, calls and tool steps behind one typical request, then multiply by real volume. Set budgets per team and alerts before launch. OWASP lists "unbounded consumption" in the same top 10 for a reason: a loop in an agent or one heavy user can burn a month's budget in a day.
