Two weeks is enough time to evaluate an AI answering service in production. It's enough for one weekend, one weekday cycle, and at least a few after-hours events. The trick is structuring the 14 days so you collect the right data to make the decision at the end, not the wrong data that tells you what you want to hear.
This post walks through a working 14-day evaluation protocol.
What you're testing
Three things, in order of priority:
- Does the script handle the worst calls correctly? Safety-branch calls, emergencies, edge cases.
- Is the captured intake usable? Does your dispatcher have what they need to assign the job?
- Does the handoff land? When the AI flags an emergency, does your on-call tech actually get the call?
Quality of voice, latency, and brand polish matter, but they're second-order. The first-order tests are above.
Day 0: Pre-flight
Before forwarding anything:
- Confirm script configuration with the vendor. Walk through the safety branches.
- Set up the on-call rotation. Test that contacts are reachable.
- Configure the CRM/dispatch feed (email, SMS, webhook, or portal).
- Get the vendor's dashboard access. Make sure you can pull recordings and transcripts.
- Set your own success criteria. What number of emergencies handled correctly is a pass?
If pre-flight isn't done, day 1 isn't day 1. Don't rush this.
Day 1: After-hours forward only
Forward your after-hours line to the AI. Keep daytime as it was.
At end of day 1:
- Check the dashboard for inbound calls.
- Listen to every recording.
- Confirm intake data landed in your system.
Most likely outcome: 2-8 calls, mostly routine, intake mostly clean.
Day 2: Script tuning based on real calls
Find one or two calls where the AI handled it sub-optimally:
- Missed a question your dispatcher would have asked.
- Took a long time on something it should have done quickly.
- Routed something wrong.
Send those calls to the vendor with notes. Ask for a script update by end of day 3.
Day 3-4: Watch the script tuning land
Verify the script update took effect on new calls. Listen to fresh recordings. Compare to day 1.
Day 5: First emergency test
Place a configured emergency test call to your forwarded line. Choose a realistic scenario for your trade (gas smell, panel burn, active leak).
Watch:
- Does the AI run the safety branch?
- Does it disengage and hand off?
- Does the on-call tech accept within the SLA?
- Does the transcript land?
If any step fails, you've found a fix-before-go-live issue. If all four pass, you have working safety handling.
Day 6-7: First weekend
The weekend is your highest-leverage test window. Saturday and Sunday after-hours volume is usually 2-3x weeknight volume. Real emergencies are more likely.
At end of weekend:
- Listen to every weekend recording.
- Verify every emergency handoff (if any).
- Check that intake routed correctly to your Monday queue.
Day 8: Daytime overflow test (optional)
If you have a busy office line where some calls go unanswered during peak hours, consider forwarding overflow during business hours for a few days.
Forward only "when busy" or "no answer in 3 rings", not always. This gives you data on daytime handling without risking your primary brand experience.
Day 9: Edge-case audit
Pull 10 recordings from days 1-8 at random. Score them:
- Was intake complete?
- Was the routing correct?
- Did the AI handle off-script questions gracefully?
- Did anything sound broken (latency, misunderstanding, wrong information)?
If 8+/10 are clean, the script is working. If 5-7/10 are clean, you have script gaps. If under 5/10, the fit is probably wrong.
Day 10: Second emergency test
Different scenario than day 5. Confirm the safety branches generalize, not just the one you tested before.
Day 11: Failover test
Place an emergency test call. Have the primary on-call deliberately not accept. Verify the rollover to secondary fires within SLA.
Day 12: Customer experience survey
If you've had real customer calls during the trial, reach out to 3-5 customers who interacted with the AI. Ask:
- Did the call answer your question?
- Did anything feel wrong or off?
- Would you call us back?
This is the qualitative signal that numbers can't show.
Day 13: Cost projection
With the data from the trial, project monthly cost:
- Total inbound calls in the 14-day period.
- Estimated monthly count (extrapolate).
- Plan tier needed.
- Likely overage at peak month (use your worst month from the past year as the baseline).
- Total monthly cost vs your current alternative.
Day 14: Decision criteria
The decision tree:
- Did the safety branches handle every emergency correctly? Required pass. If no, do not roll out.
- Did the on-call handoff land every time within SLA? Required pass. If no, fix the failover before deciding.
- Is the captured intake usable by your dispatcher? Required pass. If no, refine the schema or find another vendor.
- Is the monthly cost acceptable? Soft pass. Cost can be optimized later.
- Is the customer experience acceptable? Soft pass. Refine the script for any patterns you noticed.
If all four required passes are clean, expand the forwarding (add weekends, add holidays, add daytime overflow). If one required is failing, decide whether to extend the trial 7-14 days for script refinement, or to walk.
When to extend vs walk
Extend when:
- Failures are script-shaped (specific intent missing, wrong wording, easily fixable).
- Volume in the 14 days was too low to evaluate the safety branches.
- Vendor responded to feedback during the trial.
Walk when:
- Failures are model-shaped (latency, misunderstanding, wrong information from the AI even with the right script).
- Vendor was slow or unresponsive on script changes.
- Cost projection at peak month is unacceptable.
- Customer experience signal is negative.
A 14-day trial that gets extended once is fine. A 14-day trial that gets extended three times is a vendor you should leave.
For more, see the answering service setup checklist, the contractor virtual receptionist buyer's guide, and the AI answering service product page.
FAQs
Can I run two AI services in parallel during the trial?
Technically yes, by splitting forwarding between business numbers. In practice, the comparison data is muddier because the call mix isn't apples-to-apples. Better to run one service for 14 days, then if needed run the other for 14 more.
What if my call volume during the trial is too low to evaluate?
Extend by 14 days or pilot during a higher-volume season. A trial during your slowest week isn't a real test.
Should I tell my customers I'm using AI?
You don't have to announce it. But if a customer asks "am I talking to a person?", the AI should answer honestly. Honest disclosure on demand is the right policy.
How do I handle the bad call I might get during the trial?
It will happen. A misrouted call, a script gap, a moment of latency that sounded weird. Document it, send to the vendor, get the fix. One bad call isn't a verdict; a pattern of bad calls is.