From AI POC to Production: What Usually Goes Wrong

A working proof of concept is the easy part. The gap between "it works in a demo" and "it works for real users every day" is where most AI initiatives stall. These are the patterns behind that gap.

1. The POC used clean data

Demos run on curated documents and tidy examples. Production has duplicates, outdated policies, scanned PDFs and permission boundaries. Retrieval quality collapses and nobody planned for data cleanup.

2. Nobody defined "good"

Without an evaluation set built from real tasks, every change is a guess. Teams argue about anecdotes instead of measuring. Build the evaluation set before the POC ends.

3. Integrations were mocked

The POC called a stub. Production has to authenticate, handle rate limits, deal with partial failures and write back safely. Integration work is routinely the largest slice of a production build.

4. Latency and cost were not measured

A demo that takes 20 seconds and costs a dollar per query is fine for five people. It is not fine for a thousand. Measure early and design retrieval and model choices with a budget.

5. No human fallback

Production systems need a path for uncertain cases: escalation, review queues, approvals for risky actions. Systems designed without a fallback either get switched off after the first bad incident or quietly ignored.

6. Security review started too late

If the system touches customer or regulated data, the review is on the critical path. Start it in week one with the hosting model, data flows and subprocessor list.

7. The team that built the POC left

Contract prototypers or an internal hack team move on, and nobody owns prompts, evaluation and integrations. Ownership after launch must be decided before launch.

8. Rollout was all-or-nothing

Successful teams roll out to a user group, watch the metrics, fix, and expand. Big-bang launches convert small issues into organisation-wide loss of trust.

What experienced deployment teams do differently

They start from the production constraints and work backwards: real data, real integrations, evaluation set, fallback design, security review, staged rollout, handover. If you are choosing a provider, ask how they handle each of these eight points. The ones with production scars will have specific answers.