Question TEST 06

TEST-06 What results should guide the decision to grow, improve, pause or retire a capability?

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Why this matters

A service can keep earning trust by showing useful outcomes, learning from difficulties and matching growth to continuing support.

What to bring back

A review scorecard and decision record covering everyday value, information quality, help outcomes, reliability, safety, privacy, access, skills, costs, harms, staff workload, remaining faults and recovery or exit options.

What we know so far

• comprehensive rebuild plan — reviewed source; public copy can be added through the evidence process (Sections 9-11 and 13)
• Professional review and comparison of proposed improvements (When this work overlaps professional or public-authority responsibilities, compare the proposal and Luke's research with applicable industry/global best practice and relevant legal-engine sources. Record differences, reasonable improvements and deficiencies, with the responsible reviewer and response. Routine authorised drafting and wiki work can advance.)
• Council plan crosswalk and proposed Aura integration (Relevant question row identifies the management/recovery source section and printed/PDF page. AI-generated analysis, 21 October 2026; proposed checks and actual review/operating agreements retain separate status.)

AI-generated potential answers

For discussion and review · research checked 2026-10-20. These are AI-generated options and transfer assessments. The user's request and research method identify the source of this work. The source catalogue states what the references establish. The decision and approval record retains its own status.

Review everyday usefulness, disaster tasks, reliability, access, privacy, learning, staffing and whole-service cost together. Compare results with agreed thresholds and record the reasons to grow, improve, pause or retire each capability and support people through the change.

Existing-practice adaptation

AI-generated adaptation of an existing pattern. Use a recurring scorecard with task completion, information age, unresolved requests, faults, correction handling, staff effort and actual cost. Review after each exercise and meaningful incident, then assign an improvement with an owner and a later check.

Research basis: Digital Performance Standard: measure service success · Managing Exercises Handbook · Australia's AI Ethics Principles.

Future concept to explore

AI-generated future concept. An AI/SI learning system could identify trends across approved evidence and simulate improvement options. Auras could explain the alternatives to participants, with decisions preserving user choice, professional assessment and the history of why a capability changed.

Examples and local differences

• Digital Performance Standard: measure service success: AI transfer assessment: Use observed service outcomes to guide continuing improvement. Local difference to check: National service measures need local thresholds and context; popularity alone does not demonstrate resilience or justify expansion.

Evidence to bring back next

• Agree growth and exit criteria before a field pilot begins.
• Review whether benefits and workload are shared fairly and whether fixes work in the next exercise.

People and connected choices

Who reviews: Luke, as project initiator, coordinates proposed design changes and their supporting evidence. Actual operators, members, hosts and authorities review relevant requirements and decide continuation within their responsibilities, with user feedback, differences and the response to findings retained as evidence.

Connected questions: TEST-05, FIN-02, GOV-06

Work state: open. Assigned person or AI assistant: Ready to be assigned.

Research and alternatives can move ahead while connected decisions develop. Before acting on a choice, check the people, evidence and approved scope in People decide.