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Information Technology

AI opportunities in IT

IT is usually the first function to deploy AI and the last to measure it. The opportunities are real — ticket volume, knowledge sprawl, incident load — but IT also owns the platform every other function will depend on, so its own choices set the enterprise pattern.

Service desk and request fulfilmentIncident and problem managementChange and releaseKnowledge managementSoftware engineeringAccess and identity administration

Candidate AI opportunities

  • Service desk deflection

    Knowledge Assistant

    Grounded self-service answers over runbooks and known errors, with clean handover to a human when confidence is low.

    Signal it applies to you: A third of tickets are answerable from existing documentation.

  • Incident triage and routing

    Predictive AI

    Classify, prioritise and route incidents on arrival, with similar-incident context attached.

    Signal it applies to you: Mean time to assign is a meaningful share of mean time to resolve.

  • Change risk scoring

    Predictive AI

    Score proposed changes against past failure patterns to focus CAB attention where it counts.

    Signal it applies to you: Change advisory reviews everything at the same depth.

  • Engineering assistance

    Productivity AI

    Code, test and documentation assistance inside the existing review and security gates.

    Signal it applies to you: Delivery is constrained by review capacity, not typing speed.

  • Access request automation

    Intelligent Automation

    Policy-driven provisioning for standard roles, with exceptions escalated to an approver.

    Signal it applies to you: Access requests wait days for a routine approval.

Discovery questions to answer first

  1. 1Are we building an enterprise capability or another isolated tool?
  2. 2Where do model calls, prompts and outputs get logged, and who reviews them?
  3. 3What is our position on data leaving the tenancy?
  4. 4Who owns evaluation once the assistant is live?

Where it goes wrong

  • — Assistants grounded on stale, unowned knowledge bases.
  • — Deflection measured in usage rather than resolved tickets.
  • — Every function building its own stack because IT never published one.

First three moves

  • — Classify a month of tickets by whether a documented answer already exists.
  • — Publish the enterprise pattern for grounding, logging and evaluation.
  • — Run the AI Architecture and Governance tools before the second use case.

Common questions

Should IT own AI for the whole enterprise?
IT should own the platform, guardrails and evaluation; the business owns the use cases and the value. Separating those two roles is what makes scaling possible.
How is service desk deflection measured honestly?
By tickets never created and by resolved-without-human rate against a baseline — not by chat sessions started.

Get the IT opportunity briefing

The Information Technology candidates, discovery questions and first moves in one short briefing. No account needed.

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Other functions

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