Checklist · 4 minute read
AI Support Readiness Checklist: Before Users Can Contact You
Support teams inherit AI systems they did not build and cannot diagnose without tooling. Before launch they need the known issues, access to the decision record for a given interaction, a clear escalation path, response guidance, and capacity sized for the contact volume the launch will generate.
Support teams inherit AI systems they did not build and cannot diagnose. This checklist covers what they need first, drawn from FISTA Solutions' AI enablement operational work.
What does support need?
Six things, all before the first ticket.
| Need | Consequence if missing |
|---|---|
| Diagnostic access | Every ticket escalates |
| Known issues list | Agents learn from customers |
| Escalation path | Tickets stall |
| Response guidance | Overclaiming or blaming users |
| Capacity | Queue grows at launch |
| Feedback route | Quality signals lost |
Diagnostic access
The single most valuable item.
- Support can retrieve the record for a specific interaction
- Input, retrieved context, and output all visible
- Model and prompt version visible
- Actions taken by the system visible
- Access appropriately restricted given the data shown
- Lookup possible from information the customer can supply
- Tooling usable without engineering involvement
Known issues
Write them down before customers find them.
- Known limitations documented in plain language
- Known defects listed with status
- Cases the system handles poorly described
- Workarounds documented where they exist
- Expected resolution timing where known
- List kept current rather than written once
- Agents trained on the list before launch
Escalation
A named path with a response expectation.
- Escalation criteria defined for support agents
- Named engineering contact or rota
- Response time expectation agreed
- Severity levels defined with examples
- Out-of-hours path defined if relevant
- Escalation volume monitored
- Feedback to the agent when their escalation resolves
Response guidance
Agreed wording prevents the two common failure modes.
- Approved wording for explaining a wrong output
- Guidance on what not to promise
- Guidance against blaming the user's phrasing
- How to describe the system's limits accurately
- Disclosure position on AI involvement agreed
- Compensation or remediation authority defined
- Escalation wording for regulated or sensitive cases
Capacity
Launch volume exceeds steady state.
- Expected contact volume estimated for launch
- Staffing sized for the launch period specifically
- Handling time per contact estimated
- Queue monitoring with alert thresholds
- Plan for a volume spike
- Coverage across the relevant time zones and languages
- Capacity reviewed after the first fortnight
Feedback route
Support sees quality problems first.
- Routine channel from support to the build team
- Contact reasons categorised to reveal patterns
- Quality-related contacts tagged and trended
- Support input into the evaluation suite
- Regular review of support themes with the team
- Agents told when their feedback produced a change
- Support metrics reported alongside system quality metrics
What are the most common failures?
Launching without diagnostic tooling. Known issues undocumented. Escalation with no named owner. Agents inventing explanations. And capacity sized for steady state rather than launch.
Who should own this?
Support leadership owns readiness; the build team owns the tooling and the known issues list. Readiness assessed only by the build team consistently overestimates it.
How often should it run?
Before every launch, refreshed on material change, and reviewed after the first month against actual volume and contact reasons.
What evidence should it produce?
The readiness sign-off, the known issues list with dates, escalation response times, and contact volume against forecast.
What if support cannot be given diagnostic access?
Then engineering absorbs every ticket, which is expensive and slow. Where data sensitivity prevents full access, build a restricted view showing enough to triage without exposing the content.
That is a design decision worth making before launch rather than after the escalation queue forms. See observability for web apps.
What should you do first?
Ask a support agent to explain why the system gave a particular answer yesterday. If they cannot, the tooling is the gap.
How FISTA Solutions helps
FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: support given diagnostic access to the decision record before launch, and a routine channel routing quality patterns back to the build team, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.
To adapt this checklist to your environment, message FISTA on WhatsApp, or read how to staff an AI support rotation.
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Clear answers
Questions raised by this field note.
Straightforward guidance for evaluating scope, fit, and the next step.
01What does support need most?
The ability to see what happened for a specific interaction — the input, what was retrieved, what was produced. Without it every ticket becomes an engineering escalation.
02Why document known issues before launch?
Because the team already knows about them, and a support agent discovering a known limitation through a frustrated customer is an avoidable failure on both sides.
03How much volume should be expected?
More at launch than at steady state, concentrated on confusion about what the system does rather than on defects. Size for the launch period rather than the eventual rate.
04Why does response guidance matter?
Because the two failure modes are overclaiming — promising it will work next time — and blaming the user for phrasing. Both damage trust and both are avoidable with agreed wording.
05What should flow back to the team?
Patterns. Support sees quality problems before any dashboard does, and a routine channel from support to the build team is one of the most valuable quality signals available.
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