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Technical service · High-volume B2B operations

AI System for Prioritising Field Service Requests by Urgency

A symptom that may indicate a safety risk is not left among routine service messages. The system uses equipment, location and contract context to suggest urgency and the expertise required; the service coordinator selects the team and determines the response order.

Representative technical service ops panel with a work queue, checks and an audit trail: field service priority queue
Field service priority queue Representative interface — contains no real data. Site and account names are masked. The panel does not decide the team or intervention.

The problem

When service messages, equipment, locations and contracts are held in separate sources, their fields must be matched by hand before a draft urgency level and required expertise can be prepared.

The service coordinator must compare every suggestion in the draft urgency level and required expertise with its source record; missing or conflicting evidence must not replace the final decision.

If the possibility that the same symptom indicates a safety risk or user error is not set aside for review, a critical fault could be delayed; the record must not proceed without a human decision.

What we set out to improve

  • Time taken for a draft urgency level and required expertise, together with their supporting evidence, to reach the service coordinator for review
  • How records set aside for human review because the same symptom may indicate a safety risk or user error are resolved
  • Number of suggestions corrected after identifying a risk that a critical fault could be delayed

How the system works

  1. 01 Retrieve the service message, equipment, location and contract from permitted sources using the record identifier
  2. 02 Link source, date and version information to the shared work record
  3. 03 Validate fields and units
  4. 04 Prepare the draft urgency level and required expertise with their supporting evidence
  5. 05 Check for an exception: the same symptom may indicate a safety risk or user error
  6. 06 Have the service coordinator accept, revise or reject the suggestion
  7. 07 Record the approved decision and its source in the processing log

Methods we used

  • A source-identified common data schema for the service message, equipment, location and contract
  • A decision structure that separates deterministic rules from AI suggestions
  • A review screen showing the draft urgency level and required expertise alongside the underlying source evidence
  • A mandatory human review queue when the same symptom may indicate a safety risk or user error
  • An event identifier that prevents duplicate processing

Where people stay involved

The service coordinator compares the draft urgency level and required expertise with the supporting record. The workflow pauses when the same symptom may indicate a safety risk or user error. The responsible person can revise or reject the suggestion, or request further information.

Data and security

Only the fields required for this decision should be processed from the service message, equipment, location and contract. Access to the source systems must be authorised separately from the service coordinator role, and personal data, commercial information and technical secrets must not be passed into the model context unnecessarily. Reading, suggestions and human decisions must be logged separately.

Who this suits

A good fit

  • Teams that regularly prepare draft urgency levels and required expertise
  • Organisations that combine service messages, equipment, locations and contracts by hand
  • Companies that want people to retain control of exception decisions

Not a good fit

  • Operations where source data is not current and consistent
  • Operations where responsibility for the service coordinator role has not been defined
  • Operations where AI suggestions would be implemented without review

Frequently asked questions

What data does the system use?

Service messages, equipment, locations and contracts can be received through an API or controlled file transfer. Fields whose source and version cannot be established are not treated as confirmed information.

When does the workflow stop?

The record goes to the service coordinator for review when the same symptom may indicate a safety risk or user error. If supporting evidence is missing, no action is taken in the target system.

Does AI implement the decision on its own?

No. The urgency level and required expertise remain drafts until the service coordinator approves them. Acceptance, revision and rejection are recorded in the processing log as human decisions.

Project led by:DijitalPi

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