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USE CASE · CUSTOMER ISSUES

Resolve customer issues with verifiable context before promising an outcome.

This use case owns the resolution workflow: identify the customer and resource, check authorised sources, apply policy, coordinate owners and escalate exceptions. The customer-service landing keeps broad automation intent and the Customer Support AI profile keeps role intent.

Short answer

Customer issue resolution with AI works when the system can verify identity, order or service, policy and real status before proposing an action. Missing data, contradictions or higher-impact compensation move the case to a person.

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OPERATIONAL PROBLEM

What this pattern is trying to solve

The problem is not simply replying. It is assembling reliable context across conversation, CRM, order, ticket, policy and operations instead of copying data or guessing states.

WORKFLOW

Responsibility step by step

Automatable does not mean autonomous without limits. Every step depends on data quality, permissions and rules.

  1. 01
    Assisted

    1. Receive and classify

    Identify intent, urgency and available context without asserting unverified facts.

  2. 02
    Automatable

    2. Verify the resource

    Match the customer, order, booking or service against an authorized source before acting.

  3. 03
    Assisted

    3. Coordinate the work

    Hand structured work to orders, administration or billing when the case crosses processes.

  4. 04
    Human

    4. Resolve the exception

    A person intervenes for compensation, uncertain identity, policy exceptions or consequential impact.

  5. 05
    Assisted

    5. Close and record

    Communicate the outcome, update the case and preserve the required history.

COLLABORATION

Which roles can participate

Clara

Customer Support AI

Orchestrates the conversation and preserves context.

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Alex

Administrative AI

Corrects data or documentation when needed.

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Sofía

Accounting & Billing AI

Reviews financial discrepancies without unlimited authority.

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Reference teams
EcommerceTravel

SYSTEMS

Sources and tools to evaluate

This list describes typical context, not universal connectors available by default.

CRMTicketingEmail / chatERPEcommerce or booking systemBillingKnowledge base

ESCALATION

When a person should intervene

  • Unconfirmed identity or resource.
  • Compensation and refunds above threshold.
  • Policy exceptions.
  • Contradictory or sensitive information.

WHERE IT FITS

Related industries

The same pattern changes according to the industry’s sources of truth, regulation, volume and exceptions.

EcommerceIn ecommerce, AI Employees fit high-volume workflows where support, orders and back office need to share context without granting unlimited authority to automation.Travel and reservationsTravel is a strong fit when many handoffs exist across customer service, reservations, suppliers, administration and billing, provided availability, price and confirmation remain tied to authoritative sources.Professional servicesIn professional services, the best entry point is usually the operational layer: inboxes, documentation, CRM, billing and follow-up. Professional, legal or high-impact decisions require specific boundaries.

VALIDATION

How to measure whether the redesign creates value

Measure against the current process. Do not turn a reference example into a promised KPI.

Resolution timeRepeat contactsHandoffsEscalationsReworkHuman time per case

LIMITS

What this use case does not prove

  • It does not guarantee fully automatic resolution of every issue.
  • It does not invent states or policies when an authorized source cannot confirm them.
  • Available actions depend on actual integrations and permissions.

FAQ

Frequently asked questions

Is this just a chatbot?

Not necessarily. Conversation may be the entry point, but the pattern continues through systems, handoffs, actions and controls.

What if it cannot find the order?

It should request more information or escalate rather than guess a match and continue.

NEXT STEP

Compare this pattern with your real process before talking about automation.

Mark bottlenecks, keep the human control points you need and estimate potential value with your own data.

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