Case library
Public servicesDigital Service Director

The Public-Cloud Pilot That Might Become a Service

An approved Azure or AWS tenant can speed a pilot, but the team must decide whether its data route, cost shape and operating model fit production.

The scenario

Teaching composite — not a claim about Azure, AWS or any named provider. A regional service director wants an assistant to prepare draft summaries from non-sensitive service requests before a human responder reviews them. The organisation already has approved public-cloud tenants and a procurement route for managed AI services. The product team proposes a six-week pilot using one of those tenants because it avoids buying hardware and can absorb uncertain demand.

Security accepts the pilot only if the team documents exactly what information moves, which region and retention terms apply, how access and logging work, and how the system falls back when the provider or integration is unavailable. Finance asks a different question: if the pilot succeeds and volume grows, which costs rise with usage, what may be charged for provider storage or network transfer, and when would a company-controlled route deserve evaluation? The answer cannot be a generic claim that cloud is cheaper or more expensive.

The director must approve a pilot that produces decision-grade evidence—not a cheap-looking demonstration. The team needs a baseline workload, a permitted sample set, normal and peak demand, a quality and waiting-time target, and a review date at which it compares the provider route with a controlled alternative.

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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