Find your
kind of AI.
A good demo should leave you with a clear decision. Bring a few familiar cases and use these questions to find out what fits.
Try the work
you know best.
Choose a conversation your team has handled. You’ll know what a helpful reply looks like, where the answer lives, and when a person should step in.
Show me the source.
Select a measure or record and ask a contextual question. Follow the answer’s citations and check that the selected scope was actually applied.
What if it can’t tell?
Try missing history, an unlinked customer, or an incomplete reporting period. Look for the gap instead of a substitute number or invented explanation.
What actually ran?
Compare shadow decisions, held approvals, and action receipts. Ask which external effects are verified and which actions only change the product’s own records.
Can we recover a waiting customer?
Inspect an AI handoff with no person’s reply. Claim it, open the conversation, and understand what evidence will close the item.
Does the review improve the work?
Check an AI draft against the conversation, bring a moment into coaching, and assign a recurring issue a fix. Keep customer outcomes separate from people rankings.
Can we test the customer result?
For repeat-buyer recovery, compare observed orders after human outreach with a held-back group. Historical exposure is not recovered revenue, and a service failure is not proof of churn.
Questions,
answered.
Should I bring a customer conversation?
Use an authorized example and agree how it will be handled before sharing it. A representative fictional case can work too.
What should we leave with?
A clear understanding of the workflow, its sources, approval rules, and the next step for your team.