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A council should approve an AI project for live use only when it can show, with documented evidence, that the system solves a clearly defined public problem, uses data lawfully and appropriately, performs acceptably for affected groups, has accountable human owners, and can be monitored, paused, or stopped safely. A supplier demonstration or promising pilot result is not enough.

Use the readiness gate below as a go/no-go test. If a required control is missing or the remaining risk is unacceptable, pause the project, redesign it, or stop it rather than launching and hoping monitoring will reveal problems later.

What “ready for deployment” means

Readiness is an evidence gate, not a statement of confidence. The council should be able to answer, in writing:

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  • What public-service problem is being addressed, who is affected, and what measurable improvement is expected?
  • Why is AI preferable to a credible non-AI option, such as a rules-based workflow, better staffing, clearer guidance, or improved data management?
  • Is the data available, representative, accurate, retained lawfully, and permitted for this use?
  • Who is accountable for the system, the data, supplier performance, and each human decision influenced by an output?
  • What testing shows about overall performance, errors, security, accessibility, and outcomes for relevant demographic groups?
  • Can the council explain, audit, correct, override, roll back, or shut down the system?
  • Are staff trained, residents informed where appropriate, and the whole-life cost funded?

The approval record should identify evidence for every answer, the owner of any residual risk, acceptance thresholds, and the conditions that trigger a pause or stop.

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1. Define the problem, users and public benefit

Start with the service need rather than a model or product. The UK government’s procurement guidance says public benefit should anchor decisions and that teams should explain why AI is relevant to the problem. Record the current process, its failure points, affected groups, service users’ needs, and the outcome the council is trying to change.

Write a testable problem statement

A useful statement names the service, population, decision or task, baseline, and intended outcome. For example, “reduce the time required to triage housing-repair requests while preserving priority for residents with urgent health or safety needs” is testable; “use AI to modernise repairs” is not.

Define measures before selecting a system. Depending on the use case, these could include processing time, missed urgent cases, successful resolution, staff workload, complaints, accessibility, or resident satisfaction. Specify how each measure will be calculated and over what period.

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Keep non-AI alternatives open

Compare the proposed system with realistic alternatives. A simpler change may deliver the same benefit with less risk: redesigned forms, a searchable knowledge base, a deterministic rules engine, additional staff training, or improved data quality. AI is relevant only when its expected public benefit justifies its additional complexity, cost, and risk.

Identify users and people affected

Map direct users, such as call-centre staff or planning officers, and people affected by outputs, such as applicants, tenants, carers, children, or people with disabilities. Include residents who may be unable to use digital channels. Their needs determine accessibility, notice, human contact, appeal, and correction requirements.

2. Establish people, ownership and accountability

Set up a multidisciplinary team before procurement or deployment. The government procurement and ethics guidance points to coverage across domain expertise, service design, data, engineering, security, ethics, commercial, legal, equality, and delivery. A small council may use shared personnel or external advisers, but each responsibility still needs a named owner.

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Assign the minimum accountable roles

  • Senior responsible owner: accountable for the business case, public benefit, risk acceptance, and go/no-go decision.
  • Service owner: accountable for how the process works in practice, including resident communication and appeals.
  • Data owner: accountable for data quality, provenance, access, retention, lawful basis, and approved uses.
  • System owner: accountable for configuration, integrations, releases, logging, resilience, and operational controls.
  • Supplier owner: manages contract obligations, service levels, changes, subcontractors, and exit planning.
  • Human decision-maker: named for every consequential decision; defines when staff must review, override, or disregard an output.

Document decision rights and escalation routes. “The supplier is responsible” is not an adequate allocation of public accountability: the council remains responsible for the service it operates and the decisions it makes.

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3. Prove data fitness and legal authority

Complete data discovery before committing to a system or supplier. The procurement guidance warns against proceeding blindly when required data is unavailable. Create an inventory showing each data source, fields, owner, format, age, completeness, known errors, access controls, retention period, and permitted purpose.

Test whether the data is fit for this use

  • Availability: Can the service access the data reliably at the required time and volume?
  • Completeness and accuracy: Which fields are missing, stale, duplicated, or known to contain recording errors?
  • Representativeness: Does the data reflect the people and cases the service will encounter, including less visible or digitally excluded groups?
  • Provenance: Where did each dataset originate, what transformations occurred, and who may verify them?
  • Bias and labels: Could historical decisions, under-reporting, or unequal access encode unfair patterns?
  • Supplier use: Will the supplier retain prompts, records, or outputs, use them for training, or disclose them to subcontractors?

Record known limitations and decide whether they can be mitigated. If a required variable does not exist or is too unreliable, do not conceal the gap behind a more complex model; change the service design, collect better information lawfully, or stop.

Complete privacy and data-protection work

Identify the lawful basis, purpose limitation, data minimisation, retention, access, and data-subject rights that apply in the council’s jurisdiction. Where high-risk personal-data processing is likely, complete a data protection impact assessment (DPIA) before processing begins. The government ethics guidance also recommends considering publication of the DPIA so residents can understand the risks and safeguards.

Check whether the system makes or supports decisions with legal or similarly significant effects. Define meaningful human involvement, explanations, review, and contestability rather than assuming that a nominal sign-off is sufficient. Obtain legal advice on local privacy, equality, records, procurement, accessibility, and sector-specific duties; the exact requirements vary by jurisdiction.

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4. Assess human, equality, privacy and environmental impacts

Impact assessment is iterative. The Local Government Association’s 2025 guidance expects checks at design, procurement, pilot, go-live, material model or data change, and contract-renewal gates. Reopen the assessment when the purpose, population, supplier, model, data, interface, or workflow changes.

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Cover the full impact set

  • Human and socio-economic impact: Could errors remove support, delay a service, increase staff burden, or affect people unevenly by income, language, age, disability, race, sex, or other protected or vulnerable status?
  • Equality and accessibility: Can people use the service with assistive technology, alternative formats, interpretation, or a non-digital route? Are error rates and outcomes different across relevant groups?
  • Privacy and security: What personal, confidential, or sensitive information could be exposed, inferred, or retained?
  • Service impact: What happens to queues, hand-offs, professional judgement, and emergency cases if the tool is wrong or unavailable?
  • Environmental impact: Consider energy and resource use across training, hosting, inference, storage, and disposal where material.
  • Whole-life cost: Include integration, data preparation, assurance, accessibility, training, monitoring, incident response, upgrades, and exit—not just the licence.

For each material harm, specify prevention, detection, mitigation, an owner, and a resident remedy. An assessment that merely lists risks without decisions or controls is not evidence of readiness.

5. Require production-grade technical and operational evidence

Ask for an evaluation plan and results, not marketing claims. A vendor demo normally uses selected examples and cannot establish production readiness.

Evaluate with representative cases

Use a test set that reflects real service volumes, languages, formats, edge cases, seasonal variation, and difficult cases. Keep test data separate from development data where possible, document the sampling method, and preserve an auditable version of the data and code or configuration used.

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Report the measures that matter for the task, such as false positives, false negatives, ranking quality, extraction errors, time saved, escalation rates, and the consequences of each error. There is no universal accuracy threshold: the council must set acceptance levels according to the harm of being wrong. A system that drafts low-risk text has a different tolerance from one that prioritises safeguarding work.

Check differential performance

Break down results for relevant demographic, geographic, language, disability, age, and service-user groups where lawful and statistically meaningful. Investigate both performance gaps and differences in access or referral rates. If a subgroup result cannot be measured because data is missing, treat that as an evidence limitation and define a safe interim control rather than assuming equal performance.

Test robustness, security and integration

  • Test malformed, adversarial, ambiguous, and out-of-distribution inputs.
  • Assess prompt or input manipulation, unauthorised data disclosure, insecure outputs, and access-control failures where applicable.
  • Verify identity, permissions, encryption, secrets management, backups, resilience, and supplier incident notification.
  • Run integration and end-to-end tests for records, payments, notifications, accessibility tools, and downstream decisions.
  • Confirm that logs capture inputs or references, outputs, model or configuration version, user, timestamp, overrides, errors, and subsequent action without retaining unnecessary personal data.

Make explanations and overrides workable

Choose an explanation appropriate to the use: what the system does, which information it used, its limitations, confidence or uncertainty where meaningful, and how a staff member can challenge it. Define mandatory human review, override recording, and escalation for high-impact or low-confidence cases. An override that staff cannot realistically exercise is not meaningful oversight.

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Prepare incident response

Write playbooks for wrong outputs, data breaches, service outages, drift, harmful bias, supplier failure, and suspected misuse. Name who can disable a feature, who contacts affected people, how decisions are corrected, how evidence is preserved, and how the council reports and learns from the incident.

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6. Make procurement and contract controls explicit

During procurement, ask suppliers to describe the model or rules, important variables, training-data provenance, limitations, expected updates, evaluation methods, and support arrangements. Require enough information for the council and an independent assessor to audit the service; “proprietary” must not eliminate accountability.

Put these terms in the contract

  • Ownership and permitted use of council data, prompts, outputs, logs, and derived data.
  • Audit rights, evidence access, testing cooperation, and regulator or public-record obligations.
  • Security controls, breach notification times, location and transfer arrangements, and subcontractor approval.
  • Advance notice and approval for model, data, feature, hosting, or subcontractor changes.
  • Service levels, support hours, resilience, maintenance windows, and remedies for failure.
  • Intellectual-property responsibilities, indemnities where appropriate, and restrictions on re-use.
  • Data export, documentation, interoperability, transition assistance, deletion, and verified exit.

Define what counts as a material change and require a new impact, equality, privacy, and performance review before it reaches residents. Contract renewal is another assurance gate, not an automatic continuation.

7. Design the workforce and service around safe use

Train staff who operate, supervise, procure, support, or audit the system. Training should cover the tool’s purpose and limits, common failure modes, privacy and security, accessibility, when to seek professional advice, how to override, how to record a reason, and how to report incidents. Transfer enough knowledge that the council is not dependent on a supplier to understand its own service.

Specify which tasks AI may support and which decisions remain human. Protect professional capabilities by ensuring staff continue to understand the underlying service, can work when the tool is unavailable, and are not pressured to accept an output merely to meet a productivity target.

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The Local Government Association’s 2026 playbook states: “AI adoption should be treated as an opportunity to improve service design, decision-making, data ownership and governance, rather than simply automate existing ways of working.” Use the project to remove unnecessary steps and clarify ownership, not to automate a flawed process unchanged.

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8. Set go, pause, stop and monitoring rules

Before launch, approve measurable acceptance thresholds and a monitoring plan. Include leading indicators, outcome measures, subgroup checks, complaints, appeals, overrides, incidents, drift, uptime, cost, and accessibility. Set alert thresholds, review frequency, reporting recipients, and the person empowered to act.

Define the three decisions

  • Go: All mandatory legal, impact, security, data, performance, training, contract, and operational evidence is complete; residual risks are accepted by the named owner; and correction and appeal routes work.
  • Pause: An alert, material change, data problem, unexplained performance drop, or unresolved incident requires investigation. Restrict affected functionality or revert to a human-only process.
  • Stop: The system causes unacceptable harm, cannot be controlled or audited, breaches a legal requirement, or fails a critical safeguard. Shut it down safely, preserve records, notify affected parties as required, and do not resume until risks are sufficiently managed.

NIST AI RMF 1.0 puts the stop condition plainly: “In cases where an AI system presents unacceptable negative risk levels – such as where significant negative impacts are imminent, severe harms are actually occurring, or catastrophic risks are present – development and deployment should cease in a safe manner until risks can be sufficiently managed.”

Provide resident-facing safeguards

Tell people when AI materially assists a service where appropriate, explain the role it plays in plain language, provide a human contact, and offer a route to correct records, challenge an outcome, or request an accessible alternative. Publish enough information about purpose, accountability, data use, testing, and safeguards to make oversight meaningful without exposing security-sensitive details.

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Build, buy or adapt: compare the options

The choice of implementation does not remove the readiness obligations. Compare options against the same evidence gate.

Decision area Build Buy Adapt or configure
Public-benefit evidence Can be tailored closely to the defined service problem, but the council must prove the additional engineering effort is justified. Potentially faster, but a generic feature may not address the actual problem. May reuse an existing platform while requiring proof that its workflow and purpose fit the service.
Data and lawful use Full design control does not make unlawful or poor-quality data acceptable. Requires scrutiny of supplier processing, retention, training use, transfers, and subcontractors. Requires a fresh check of how configuration changes purpose, access, or retention.
Performance and subgroup impact Testing burden is entirely the council’s unless a partner is contracted. Obtain independent or reproducible evidence; do not rely on a sales demonstration. Re-test after local data, prompts, rules, or integrations are changed.
Explainability and auditability Design logs, documentation, explanations, and override paths from the start. Make audit access and usable explanations contractual requirements. Verify that platform logs expose the changes and decisions made in the local deployment.
Security, integration and resilience Engineering and operating responsibility remain with the council or its delivery partner. Assess architecture, access, incident response, uptime, portability, and exit rights. Test interactions with existing identity, records, accessibility, and continuity arrangements.
Workforce and lifecycle cost Requires specialist capability and long-term maintenance funding. Includes licence, support, upgrades, assurance, and switching costs. Can reduce build effort but may create dependency on a platform’s roadmap and skills.
Residual risk More control may mean more responsibility and a larger assurance workload. Supplier assurance can help but cannot transfer public accountability. Risk depends on what the local configuration changes; treat each material change as a new assurance event.

What the go/no-go board should receive

Assemble one decision pack with linked evidence, owners, dates, and unresolved issues. At minimum it should contain:

  1. A problem statement, affected groups, baseline, expected public benefit, success measures, and documented non-AI alternatives.
  2. The role and accountability map, including senior responsible owner, service, data, system, supplier, and human decision-maker.
  3. Data inventory, quality and representativeness findings, provenance, lawful basis, retention, access, and supplier-use assessment.
  4. DPIA where high-risk personal-data processing is likely, plus equality, accessibility, human, socio-economic, privacy, security, environmental, and whole-life-cost assessments.
  5. Evaluation protocol and results, test-data description, error analysis, subgroup performance, robustness and security tests, integration results, and known limitations.
  6. Human-review, explanation, override, logging, incident-response, rollback, shutdown, and resident correction procedures.
  7. Contract schedule covering data, audit, security, changes, service levels, subcontractors, portability, support, and exit.
  8. Training completion, operating procedures, staffing, budget, monitoring dashboard, alert thresholds, review calendar, and named residual-risk owner.
  9. A written go, pause, and stop decision with conditions, expiry or review date, and evidence required to clear each open issue.

Common reasons to pause or stop

  • The project cannot demonstrate a specific public benefit or why AI is needed.
  • Required data is missing, unrepresentative, unlawfully obtained, or being used for a new purpose without a lawful basis.
  • The supplier will not provide sufficient information, audit access, change notice, or an exit route.
  • Testing omits realistic cases, high-impact errors, accessibility, security, or relevant subgroup performance.
  • No person has authority to override, correct, appeal, or shut down the system.
  • Staff are untrained, the service cannot operate safely during an outage, or professional judgement is being displaced without a deliberate decision.
  • Monitoring is unfunded, thresholds are undefined, or an incident has no response owner.
  • Residual risk is unacceptable even after mitigation.

These are decision conditions, not paperwork defects. A council that cannot supply the evidence cannot demonstrate readiness, regardless of how impressive the technology appears.

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