Composite case study · Local government · Aotearoa New Zealand
Where AI could fit
in a council’s work.
A pipeline-generated map of 12 role archetypes in a composite New Zealand district council—229 generated tasks and 6,101 scored steps, assessed under a specified 2026 language-model deployment profile.
Percentages describe generated step categories, not time, workload, transaction volume, staffing or full-time equivalents.
On this page
What was modelled, the technology assumptions, headline results, role-level distributions, a planner drill-in and the limitations that matter.
§ 01 · Scope
A model council,
not a real council audit.
The organisation is a composite representation of a mid-sized New Zealand district council, constructed to demonstrate the methodology without exposing the data of a real organisation.
The twelve role archetypes were selected to span finance, regulation, strategy, governance, customer services, people, libraries and infrastructure operations. They are not an exhaustive council organisation chart and exclude executive leadership and several specialist functions.
The role maps were produced from research-based descriptions and organisational context. The production pipeline generated 229 tasks, decomposed those tasks into 6,101 steps, assessed each step against the ten-dimension metric registry, and applied the default 2026H1 language-model profile.
The pipeline outputs shown here were not manually edited. That is useful for demonstrating the end-to-end system, but it is not proof that every generated task or step is correct. In a client engagement, practitioners would review the provisional workflow map before it became a decision basis.
Pipeline integrity
Tasks, steps, metric scores, attributes, dispositions and traces were produced through the versioned production process.
Observed work measurement
The analysis does not use time-and-motion observation, workflow logs, transaction volumes or task-frequency data.
Practitioner correction
The public demonstration has not been rewritten to reflect practitioner corrections. External validation is being developed separately.
Technical performance testing
No AI system was deployed inside a council workflow to test actual accuracy, reliability, cost or employee acceptance.
§ 02 · Profile
The result depends on
the system being assessed.
This is not a timeless estimate of what “AI” can do. It is the output of one versioned technology and policy profile.
A separately defined integrated profile assumes stronger access to archival records and continuously supplied system information. It changes some dispositions without changing the underlying work map. That comparison can help identify where integration investment may increase useful autonomy.
§ 03 · Headline result
Oversight dominates
the generated map.
Under the default profile, 55.8% of generated step categories are classified as work the AI can carry—but almost nine in ten of those still require human approval before the output takes effect.
AI carries
383 generated steps
AI carries, human approves
3,020 generated steps
Human leads, AI assists
1,042 generated steps
Human required
862 generated steps
Beyond this profile
794 generated steps
Unreviewed AI carriage is a small minority
Only 6.3% of all generated steps are classified as AI carrying the work without routine per-instance approval.
Human approval does most of the work
The largest category is AI carrying the primary work with a person reviewing it before effect.
Physical scope remains substantial
Thirteen percent of generated steps lie beyond the current profile, concentrated in infrastructure, parks and library work.
§ 04 · Role by role
Twelve roles,
twelve different mixes.
Roles are sorted by the share of generated steps the AI can carry. The complete distribution matters more than the ranking.
| Role and department | Distribution | AI carries | + approval | AI assists | Human required | Beyond profile | Total | Carry share |
|---|---|---|---|---|---|---|---|---|
| Accounts Payable Officer Finance | 36 | 303 | 6 | 14 | 0 | 359 | 94% | |
| Resource Consents Planner Regulatory Services | 31 | 379 | 42 | 48 | 2 | 502 | 82% | |
| Building Consents Officer Regulatory Services | 18 | 204 | 19 | 50 | 16 | 307 | 72% | |
| Communications Advisor Strategy and Policy | 61 | 348 | 122 | 84 | 0 | 615 | 67% | |
| Customer Services Officer Customer and Community | 51 | 182 | 94 | 34 | 18 | 379 | 61% | |
| Policy Advisor Strategy and Policy | 22 | 441 | 178 | 141 | 5 | 787 | 59% | |
| Democracy Advisor Governance | 25 | 239 | 125 | 66 | 4 | 459 | 58% | |
| Customer Services Team Leader Customer and Community | 47 | 234 | 146 | 78 | 11 | 516 | 54% | |
| HR Advisor People and Capability | 23 | 333 | 161 | 143 | 1 | 661 | 54% | |
| Community Librarian Customer and Community | 55 | 95 | 72 | 49 | 181 | 452 | 33% | |
| Water Services Operator Infrastructure and Operations | 0 | 178 | 61 | 133 | 260 | 632 | 28% | |
| Parks and Open Spaces Worker Infrastructure and Operations | 14 | 84 | 16 | 22 | 296 | 432 | 23% | |
| All role maps | 383 | 3,020 | 1,042 | 862 | 794 | 6,101 | 55.8% |
Counts are not weighted by illustrative FTE, task frequency, duration or transaction volume. “Carry share” is AI carries plus AI carries with human approval, divided by all generated steps for the role.
§ 05 · Role drill-in
One planner role,
seventeen generated tasks.
The Resource Consents Planner illustrates why the task-level mix is more informative than one role-level percentage.
The tasks below are presented in an indicative consent-processing sequence. They are pipeline-generated representations, not a claim that every council or every application follows one linear path.
Check a lodged application for statutory completeness
Set up the statutory processing record
Determine consent triggers and activity status
Provide pre-application planning advice
Coordinate specialist input on an application
Request and evaluate further information under section 92
Assess the environmental effects of a proposal
Negotiate proposal amendments with the applicant
Prepare a notification recommendation
Draft resource consent conditions
Prepare a resource consent recommendation report
Prepare a section 42A hearing report
Assess submissions on a notified application
Present planning evidence at a hearing
Issue the resource consent decision package
Monitor compliance with granted consent conditions
Respond to consent-processing queries
Document and rules work
Completeness checks, processing records, trigger assessments, draft conditions and decision-package assembly are classified heavily toward AI carriage with human approval.
Judgment and live professional interaction
Environmental-effects assessment and hearing work retain more human leadership because the planner must weigh competing considerations, explain reasoning and respond in real time.
§ 06 · Human authority
The same disposition,
for different reasons.
“Human required” may result from grave consequences, value-laden judgment, relationship demands or the need for legitimate human decision authority.
Human required because the stakes are grave
Building Consents OfficerAssess fire safety, including escape routes, alarms and fire separations.
The assessment is substantially rule-bound, but the consequences of error may be severe. The default policy retains human authority even where a system can perform much of the checking.
Human required because the judgment is the work
Resource Consents PlannerWeigh how much a proposed building would affect each neighbour.
Reasonable professionals may weigh the same facts differently. The system may prepare the evidence, but a person forms and owns the final assessment.
§ 07 · Interpretation
What this demonstration
suggests.
The strongest signal is not wholesale automation. It is the prevalence of workflows in which AI performs substantial digital work while people retain review, judgment or live responsibility.
Document-heavy, structured and rule-bound steps appear most often in the AI-carriage categories. Human leadership becomes more prominent where work depends on contested judgment, interpersonal adaptation, live situational awareness or accountability for grave consequences.
Physical work creates a separate limitation. Large parts of parks, water operations and community-library work are beyond this language-model profile rather than merely low-scoring.
The profile comparison also identifies potential integration value. Under a separately modelled integrated profile—assuming stronger archival retrieval and continuously supplied system information— some steps gain autonomy because required context becomes available to the system. This is an architectural change, not a claim that the underlying model has become more intelligent.
For AI investment
Prioritise recurring digital workflows where the map shows substantial carriage potential and where required information, tools and review controls can realistically be supplied.
For workforce planning
Focus on how roles may be redesigned around review, exception handling, judgment and relationships rather than converting step shares directly into staffing estimates.
For governance
Distinguish technical capability from acceptable authority. High-consequence or value-laden decisions may retain human control even where AI can perform much of the analytical preparation.
§ 08 · Reading the results
What the numbers
do not establish.
This is not a real council audit
The organisation and its FTE assumptions are composite. Results should not be attributed to any actual council.
Generated steps are not workload measures
Counts are not weighted by task frequency, duration, transaction volume or FTE. They cannot be converted directly into time or staffing effects.
The workflow map remains provisional
The public demonstration has not been corrected through a complete practitioner-validation process. Some tasks or steps may be missing, misplaced or expressed at the wrong grain.
The profile is not a performance benchmark
The capability envelope represents evidence-informed judgments about a technology class. It does not prove that a specific product will meet a council’s required accuracy or reliability.
Policy thresholds remain organisational choices
Human-review and human-authority requirements reflect the stated default policy. A real council may reasonably adopt different settings, subject to law and professional obligations.
Implementation feasibility is not included
Integration, data access, security, privacy, testing, assurance, procurement, employee participation and change management require separate investigation.
§ 09 · Data note
Every role-level count
is published above.
The role table in § 04 contains the complete disposition counts used for the organisation-level result. The five arrangement columns sum to 6,101 generated steps.
“AI can carry” is calculated as:
- AI carries
- 383
- AI carries, human approves
- 3,020
- Human leads, AI assists
- 1,042
- Human required
- 862
- Beyond profile
- 794
- Total generated steps
- 6,101
Suggested citation: Deployed Intelligence Labs Limited, Where AI could fit in a council’s work: a task-by-task case study, council-demo-01, 2026.