Short answer
Before deploying an AI chatbot, confirm ten things: the business goal, users and boundaries, approved knowledge, human handoff, data permissions, permitted actions, integrations, testing, monitoring, and ownership. A chatbot is ready when the business can control what happens when its answer is uncertain.
1. What result should it improve?
Choose one first job: pre-sales questions, enquiry qualification, information lookup, or support routing. Define the next business action. “Use AI” is not a measurable objective.
2. Who uses it and what is out of scope?
List audiences, languages, operating hours, and questions that must be refused or routed. Make boundaries visible. A focused assistant with escalation is safer than a general bot.
3. Which knowledge may it use?
Approve the pages, documents, product data and policies it can rely on. Assign owners and review dates. When sources conflict or are incomplete, it should say so and offer a next step.
4. What happens when it is unsure?
Define rules for collecting contact details, creating a ticket, transferring to a person, or stopping. Test the handoff and the context received by the human.
5. What data can it see and retain?
Map personal and sensitive commercial data. Minimize collection, set retention, restrict access, and document what is sent to the model provider. OWASP highlights sensitive-information disclosure and improper output handling as LLM application risks.
6. Which actions may it take?
Separate answering from acting. Reading a public FAQ differs from changing an order, sending a message, issuing a discount, or writing to a CRM. Use least privilege, validation, and human approval for consequential actions.
7. How does it fit the existing workflow?
Specify website, CRM, help desk, calendar, email or WhatsApp connections, including fields, duplicate handling, error behavior, and ownership. The next process must receive the right data.
8. How will you test it?
Build tests from real questions, edge cases, multiple languages, ambiguous requests, instruction-override attempts and sensitive-data scenarios. Check answer quality, routing, permissions, logs and recovery. OpenAI’s production guidance also stresses reliability, monitoring and operational safeguards before scaling.
9. What will you monitor?
Track unanswered questions, escalations, integration failures, feedback, latency and cost. Review samples under an appropriate privacy process and turn recurring failures into updates.
10. Who owns it after launch?
Name content, technical, escalation and incident owners. Set a change process and rollback plan. Without ownership, a useful bot becomes stale and loses trust.
Go/no-go check
Do not launch if the team cannot explain where the bot gets information, what it may do, what happens when it is wrong, and who receives an escalation. Start narrow, observe real conversations, then expand.
Read our guide to deciding whether a business needs an AI chatbot and website lead-generation checklist. Security context: OWASP LLM risk guidance and OpenAI production best practices.