✓ Small teams with a defined coverage window
✓ Operators comparing human-led website support
✓ Readers who can test a narrow workflow
— A promise of autonomous customer service
— Unreviewed sensitive-data workflows
— Guaranteed conversion gains
Choose a task with an inspectable result
Summarising an authorised fictional transcript or drafting wording from an approved fact is easier to review than allowing a system to decide policy exceptions. Start with a bounded task whose output a trained person can check. Do not upload private customer records to an unapproved service. Identify which tools and data uses your organisation has authorised before experimenting, even when an AI feature is available inside an existing subscription.
Keep the source and uncertainty visible
A fluent answer can still omit a condition or invent a fact. Ask the reviewer to compare the proposed response with the approved source, not merely judge whether it sounds professional. If the information is missing, the right outcome may be an explicit uncertainty and a human escalation. NIST’s AI risk framework offers voluntary risk-management context; it is not certification that a particular vendor or configuration is safe for your use case.
Test the refusal and handoff boundaries
Use fictional questions involving an unsupported product claim, a policy exception and a request for sensitive information. Observe whether the system stays within the approved material and whether unresolved work reaches an accountable person. Record the configuration and limitations. A correct answer to an easy question does not establish reliability for difficult ones. Do not describe an automated response as a completed resolution merely because it was delivered quickly.
Understand both maintenance and billing
AI features may introduce separate usage or outcome charges, as current vendor pricing models illustrate. Verify the applicable definitions and controls before live use; do not assume a base seat price includes unlimited automation. Someone also needs to maintain the knowledge and review failures. If those responsibilities or cost boundaries remain unknown, keep the feature off and use the human workflow. A cautious scope is more useful than an impressive demo with no durable owner.
Where the safety evidence stops
This guide draws on NIST voluntary AI Risk Management Framework — guidance, not a certification, Intercom current pricing models, Tidio current pricing models. No merchant-controlled record is identified here; verify provider-specific details directly. Other cited records provide additional context. A different publisher or a research, regulatory or certification label does not by itself establish independence, relevance or product validation.
Verify any current price, plan limit, label direction, compatibility rule, or commercial term that would materially change the decision. The dated source ledger shows the underlying records so this conclusion can be checked and updated.
Sources used for this page
These records support the facts and comparisons above. Merchant-controlled records are labelled so you can separate product claims from independent evidence.
- NIST voluntary AI Risk Management Framework — guidance, not a certification — Reference · nist.gov · Publisher independence not verified · checked 2026-09-19
- Intercom current pricing models — Alternative provider · intercom.com · Publisher independence not verified · checked 2026-09-19
- Tidio current pricing models — Alternative provider · tidio.com · Publisher independence not verified · checked 2026-09-19