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Spark playbook scenarios · Spark: AI Store Assistant

Route support tickets to appropriate owners

Billing changes sit behind order-status questions because everything lands in one shared list with no rule saying who may act. In this case, the immediate task is to route support tickets to appropriate owners. The workflow should produce a support operating workflow and routed sample ticket outcomes governed by stated team roles and permissions.

Topic
Customer Support Operations
Target keyword
shopify support ticket routing
Copyable AI prompt

A prompt you can drop directly into Spark

First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.

ai-prompt.txt
Act as a Shopify operations specialist.
Help me route support tickets to appropriate owners.
My store context is [describe the business, products and target audience].

My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [current ticket types, team roles with permitted actions in the order, payment and shipping systems, per-channel service targets, and the fallback queue definition]. Map each request type to the role that can complete its action, add the facts an agent must confirm before escalating, and set a closure condition per type so no ticket returns to the queue without a recorded outcome.
Return a support operating workflow and routed sample ticket outcomes for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.
Use in Spark
How to solve

A recommended resolution workflow

Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.

01

Step 1

Collect the ticket types arriving now, the team roles and the actions each role is permitted to take in the order, payment and shipping systems, the service targets per channel, and the fallback queue that takes anything unmatched.

02

Step 2

Map each request type to the role that can complete its action, add the facts an agent must confirm before escalating, and set a closure condition per type so no ticket returns to the queue without a recorded outcome.

03

Step 3

Deliver the routing workflow with one sample outcome per type, then replay past tickets through the rules and check that each reaches a role able to act and that none ends in a queue without an owner.

FAQ

Common questions when running this scenario

Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.

How does routing differ from escalation?

Routing assigns the first owner by the action the request needs, while escalation changes the owner because the permitted action is exceeded or blocked; an escalation arriving without a new owner is a routing failure rather than a handover.

How is a permission conflict resolved?

The narrower permission governs and the ticket goes to the role that holds it, because a role acting beyond its permissions creates a change nobody can reverse; a colleague may not approve on another's behalf however routine the case looks.

What happens to a ticket type with no matching role?

It goes to the fallback queue with a named owner and is listed for review, so nothing sits unassigned while the gap is decided; leaving it in the shared list produces exactly the delay the routing rules exist to remove.

Related scenarios

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Next step

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