Many companies have an AI demo that impressed leadership and then went nowhere. The reasons are rarely about the model. They are about the work around it.
1. Define success before you build
Agree on one or two business metrics, such as resolution rate, handling time or hours saved, plus a quality threshold. Without them, a pilot can never be declared ready.
2. Use real data and real integrations early
Demos often run on clean sample data and skip the CRM, ticketing system or database. Connect to the real systems in the first weeks, with real permissions. That is where most of the effort is, and where surprises appear.
3. Build evaluation in from day one
- Create a test set of real questions or tasks with expected outcomes.
- Run it automatically on every prompt, model or data change.
- Track accuracy, latency and cost per task over time.
- Review a sample of live conversations every week.
4. Design for failure
Decide what happens when the AI is unsure or wrong: hand over to a person, ask a clarifying question, or decline. Add guardrails for sensitive topics and log everything needed for an audit.
5. Give it an owner
A production AI system needs an owner, monitoring, and a budget for improvement, like any other product. Plan the handover to the team that will run it before launch, not after.
A realistic timeline
With a clear use case and access to data, a first production release usually takes six to twelve weeks. Compliance-heavy domains such as healthcare or finance take longer, mainly because of review and audit requirements.