Case library
Industrial servicesChief Information Officer

The Hardware Asset Reality Check

A company must separate reusable foundations from the new capacity needed for a shared AI knowledge service.

The scenario

Teaching composite — not a claim about a named organisation or measured outcome. A 350-person engineering company wants an internal assistant that summarises maintenance manuals and drafts work-order notes. The IT team says there is already a virtualisation cluster, a 10 Gb network, identity management, backups and unused rack space. A business sponsor therefore assumes that 'we already have the infrastructure' and asks procurement to buy a GPU.

The infrastructure lead finds a more nuanced picture. Identity, network monitoring and document storage may be reusable, but the candidate server has no suitable accelerator, the rack's power and cooling headroom are unverified, and the backup route was designed for office systems rather than a shared model service. The assistant also needs to handle long manuals during the morning planning peak. The decision is not whether existing assets have value; it is which parts actually meet the proposed service requirement and which create a hidden bottleneck or operating obligation.

The CIO asks for a one-page asset-and-gap view before approving any purchase: user journey, permitted data route, existing components, constraints, incremental additions, recurring operation, and the representative test that would prove the design is adequate.

How AI enters the workflow

  1. Frame the decision

    Human

    The accountable leader defines the outcome, constraints and what must remain human.

  2. Prepare evidence

    AI

    AI organises the relevant material, assumptions and options with sources where available.

  3. Test the workflow

    Human + AI

    A small team tests the proposed workflow on representative work and records failures.

  4. Make the call

    Human

    A named person approves the decision, boundaries and measure of success.

  5. Learn and improve

    Human + AI

    Results, feedback and exceptions feed the next review rather than disappearing in a project report.

Ask about this workflow

ADA, the taskforce deputy, explains exactly how human and AI share the work — ask anything.

The human + agent taskforce

Team leader — approves every deliverable

Team leader — approves every deliverable

Agents propose and execute. The human leader always approves the final result.

Outcomes

MetricBeforeAfter
Time to a defensible first decisionFragmentedVisible and repeatable
Human accountabilityImplicitNamed at each hand-off
Learning signalAnecdotalReviewed every cycle

Key takeaways

  • Start with a bounded business decision, not a technology demonstration.
  • Make the human owner, evidence and escalation route visible before scaling.
  • Treat feedback as a design input: it improves the system and the team's judgment.

Related concepts

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