Field guide 06 · Small business
Triage customer inquiries without putting trust on autopilot
Sort common inquiries, retrieve approved answers, and draft replies while people handle complaints, commitments, exceptions, and sensitive situations.
The direct answer
What is a low-complexity AI use case for a BC small business?
A bounded inquiry assistant can be a reasonable first pilot when it drafts from approved information and never sends automatically. Start with a narrow queue, exclude sensitive or high-impact topics, and measure correction work as well as speed.
Good fit when
- The same questions recur across email or forms
- Approved answers, policies, and escalation rules exist
- A person reviews drafts before sending
- Sensitive categories can be excluded
Keep outside the boundary
- Autonomous complaint resolution, refunds, pricing exceptions, or commitments
- Employment, health, legal, credit, or other high-impact decisions
- Pretending a customer is speaking with a person
The workflow
Assist the work. Keep accountability visible.
A team member reads each message, identifies the topic, searches for the right policy or answer, drafts a response, and routes exceptions to the owner.
AI may assist
- 01
Classify the inquiry into an approved category
- 02
Retrieve relevant information from the approved answer library
- 03
Draft a reply with links and no invented commitment
- 04
Flag complaints, sensitive information, urgency, or low confidence for a person
People must retain
- 01
Review the original inquiry and proposed response
- 02
Handle complaints, exceptions, prices, refunds, promises, and sensitive situations
- 03
Approve the answer library and keep it current
- 04
Disclose automated interaction when customers interact directly with a system
- Customer message
- Approved FAQ and policy library
- Escalation categories
- Tone and response template
- Current hours, service area, and contact routes
A review queue with category, source-linked answer, draft response, confidence flag, and escalation reason.
30-day pilot
Small enough to inspect. Real enough to learn.
Use historical or low-risk work first. Preserve the original inputs and current-process result so the comparison remains honest.
Set the boundary
Choose one owner, one customer-inquiry queue, approved inputs, and a human approval point.
Test side by side
Run a small historical sample through the current process and the assisted process. Keep the original records.
Measure the exceptions
Track correction time, missed facts, escalations, and whether the proposed output was actually useful.
Decide with evidence
Document the result, update the rules, and continue only if the workflow is safer or meaningfully better.
Measure
Count the corrections, not just the speed.
- Time to a reviewed response
- Percentage of drafts accepted with minor or no edits
- Incorrect claims or commitments
- Escalations correctly identified
- Customer complaints linked to the assisted workflow
Stop the pilot if
- The tool invents policy, price, availability, or commitments
- Sensitive messages are handled without human review
- The answer library is not kept current
- The customer experience becomes less clear or less respectful
A failed pilot is useful evidence. It may point to a narrower workflow, better data, stronger review, or no AI at all.
Common questions
Clear answers before the pilot.
Should the assistant reply automatically?
Not in the first pilot. Draft-only operation makes errors visible and provides the correction data needed for a responsible decision.
What inquiries should be excluded?
Complaints, disputes, sensitive personal information, exceptions, refunds, commitments, and any topic with a meaningful impact on a person.
How do we know if it works?
Measure reviewed response time, correction effort, incorrect claims, escalations, and customer outcomes—not just how many drafts it produces.
Source trail
What informed this field guide
This is a practical workflow synthesis, not reported ROI or a claim that the use case has been validated for your organization.
- Office of the Privacy Commissioner of CanadaPrinciples for responsible, trustworthy and privacy-protective generative AI
Supports necessity, proportionality, transparency, accountability, and privacy review before personal information enters an AI workflow.
- Canadian Centre for Cyber SecurityITSAP.00.041: Generative artificial intelligence
Supports controls for sensitive information, account security, output verification, and organizational guidance for generative AI use.
- National Institute of Standards and TechnologyAI Risk Management Framework
Provides a voluntary structure to govern, map, measure, and manage AI risk throughout a pilot and deployment.
General operational education only. Confirm applicable legal, privacy, cybersecurity, professional, safety, contractual, and workplace requirements for your organization.
Make it specific
Turn this pattern into your Project Brief.
Define the owner, real inputs, approved output, success measure, and constraints before evaluating a provider.