Operating use case
AI-assisted decision support with human authority and evidence
A use-case guide for organisations using AI to help people assess, prioritise or act without treating the model as the accountable decision-maker.
Trigger
When this use case becomes a real delivery question.
People must interpret large volumes of information, draft a response, prioritise a queue, compare options or identify a next action, and the existing process is slow or inconsistent.
A proposed AI capability appears persuasive, but the buyer needs to know what evidence a user sees, what uncertainty means, which decision remains human and how poor or harmful output is handled.
The organisation wants a controlled pilot or product component rather than a broad claim that AI will replace professional judgement.
People and work
Users, authority and the workflow around the technology.
Decision support is useful when it improves the context available to an accountable person. The first design names the user, the decision they are supporting, the permitted inputs, the suggested output, the information needed to assess that output and the action available when it is uncertain, challenged or unavailable. A recommendation without context or an override route is not meaningful human oversight.
The distinction between support and automation must stay visible. A system may summarise, retrieve, classify, draft, compare or flag information while a person remains responsible for deciding and recording the outcome. If a proposal affects people, eligibility, safety, rights, money, service access or another consequential area, the relevant owners should decide whether the intended use is appropriate and what specialist advice or controls are required.
Boundary
A credible first scope.
- 01
One bounded decision or review activity, not an unrestricted general assistant.
- 02
Defined permitted inputs, source authority, output format, user role and prohibited uses.
- 03
An evaluation question such as relevance, reliability, completeness, safety, latency, cost or user understanding.
- 04
A person with authority to review the evidence and decide whether the pilot scales, changes or stops.
Delivery
How the work can move from question to evidence.
- 01
Describe the current decision, evidence sources, failure modes, workload and user context before selecting a model, retrieval, rule or interface approach.
- 02
Define the intended support behaviour and explicit limits. This can include what the system must not decide, which source is authoritative, when a user must check the source and how a contested result is escalated.
- 03
Build the smallest testable workflow with a usable interface, relevant context, traceable limitations and feedback route rather than a detached model demonstration.
- 04
Evaluate against agreed cases, including ambiguous, incomplete, adversarial or failure conditions relevant to the use case, then record the evidence and decision for the next release.
Controls
Decisions that should remain visible in the product.
- 01
Purpose, users, affected people and prohibited-use boundary.
- 02
Input permissions, source attribution, data minimisation and supplier settings appropriate to the workload.
- 03
Human review, override, escalation and stop authority that is usable in the interface and operating process.
- 04
Evaluation cases, known limitations, monitoring signals and change record.
- 05
Project-specific privacy, security, legal or specialist review where the use case calls for it.
Acceptance
Evidence for the next accountable decision.
- 01
A named user can use the agreed evidence to assess an AI-assisted output rather than receiving an unexplained answer.
- 02
The agreed difficult and failure cases are evaluated, with limitations recorded rather than hidden.
- 03
A challenged, uncertain or unavailable output follows the defined human route.
- 04
The pilot owner has a record sufficient to decide whether to iterate, scale, pause or stop the use case.
Limits
What this route does not claim.
This use case does not state that AI is accurate, unbiased, legally compliant, appropriate for every decision or capable of replacing accountable professional judgement.
It does not offer legal, regulatory, employment, financial, clinical or other specialist advice. The relevant client owners and advisers determine the appropriate use and control level.
Direct answers
Questions about this use case
01What does human-in-the-loop mean in practice?
It means a person has the information, authority and time to review, challenge, override or stop the system in the relevant workflow. A nominal approval button without context or authority is not a useful control.
02Can the system use confidential information?
That is a workload-specific data and supplier decision. The proposed sources, permissions, provider account, endpoint, retention, access and contractual terms need review before confidential information is used.
03How do we test output quality?
Agree use-case-specific evaluation before build: relevant cases, difficult cases, failure conditions, reviewer criteria and a decision route. General demonstrations do not establish that the workflow is dependable.
04Can an AI assistant make recommendations to customers or staff?
It may be possible, but the purpose, affected people, decision impact, limitations, human review, escalation and applicable specialist obligations must be assessed. The right first release may be internal support rather than direct automated communication.
05Does responsible AI slow delivery?
Proportionate controls make the delivery decision clearer. For a bounded use case they can reduce rework by establishing purpose, inputs, human authority, evaluation and change route at the same time as the product workflow.
A practical next step
Turn this use case into a qualified brief.
Share the workflow, affected users, known systems, deadline, constraints and the decision the first release needs to support.
Discuss the use case