The title’s 41 deployments should be treated as a claimed scope, not a verified count: the available evidence does not establish a matching list or explain what qualifies for inclusion. AI is being used across different kinds of public-sector work, but a planned project, pilot and routinely used service are not interchangeable. This guide explains the use-case categories identified in a secondary catalog and how to check whether a reported system is actually deployed.
What “41 deployments” can—and can’t—tell you
A catalog page from UseCase.ai points to public-sector AI examples, but it is a secondary source and does not establish a verified, complete inventory of 41 systems. Without the underlying entries and an inclusion rule, the number alone cannot show how many systems are operational, where they are used, or who is affected.
For a useful comparison, distinguish the stage of each project:
- Announced or planned: an agency has described an intention, but routine use has not been established.
- Prototype or pilot: the system is being tested or used in a limited setting.
- Operational: the system is part of a regular process or service; the agency, scope and duration should be documented.
- Scaled: use has expanded beyond its original setting. Expansion should be evidenced, not inferred from a pilot.
The OECD reports that many public-sector AI use cases remain planned, piloted or in development, and that implemented cases often have not expanded beyond their original context. That makes maturity and scale essential details, not fine print. OECD, “Implementation challenges that hinder the strategic use of AI in government”
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What kinds of public-sector work appear in the catalog?
The catalog points to several broad functions. These are categories to investigate, not a verified list of named or operational deployments.
| Function | What to establish before calling it a deployment |
|---|---|
| Staff text assistance | Whether staff use it in routine work, what tasks it supports, and who checks its output before it is used. |
| Resident-facing information | Which agency service it covers, whether residents can reach a human for help, and how incorrect or incomplete answers are handled. |
| Accessibility documents | Which documents or formats are processed, whether people with disabilities can use the result, and how quality is checked. |
| Administrative work | Which process is affected, whether the system assists staff or influences decisions, and what review or correction route exists. |
These functions can carry very different consequences. A staff writing aid is not equivalent to a system involved in eligibility, enforcement or access to a public service. Compare like with like, and assess the stakes before comparing claimed benefits.
How to check whether a system is really in use
For each claimed example, look for a record from the responsible agency or another primary source. Capture the following before counting it as an operational deployment:
- Jurisdiction and agency: identify the government level and the office accountable for the system.
- Function and affected people: specify the task, service or internal process, and who encounters the system or its effects.
- System role and inputs: establish what the AI does, what information it uses, and whether it advises a person or acts within a workflow.
- Maturity, scale and duration: record whether it is announced, planned, a prototype, a pilot, operational or scaled, along with the setting and period documented.
- Outcome evidence: separate agency-reported results from independently documented evaluation; do not treat an announcement or intended benefit as a measured outcome.
- Oversight and redress: find out who reviews the output, how errors are corrected, and whether affected people have a route to question or appeal a decision.
- Governance and evaluation: look for documented controls and ongoing assessment, not just a launch announcement.
The OECD’s discussion of implementation and scaling supports checking maturity and context rather than counting every proposal as a deployment. The Ada Lovelace Institute likewise highlights limited visibility into public-sector AI pilots and deployments and calls for systematic, iterative evaluation and stronger governance. Ada Lovelace Institute, “Public sector AI”
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Why deployment takes more than a model
Government AI also depends on people and operating infrastructure. In a government AI lab case, the OECD describes an operating setup that included technical staff, delivery management, impact analysts and shared cloud infrastructure. Those details illustrate the organizational capacity involved in implementation; they are not a universal staffing formula or a benchmark for every agency.
Evaluation and governance matter throughout use, not only at procurement or launch. The Ada Lovelace Institute’s concern about limited visibility means a public announcement may not tell residents whether a tool remains a pilot, has moved into routine use, or has been evaluated over time.
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Policy requirements depend on jurisdiction and date
Rules and guidance should be checked against the relevant government and publication date. For example, the Australian National Audit Office says the Australian Government’s responsible-use policy version 2.0 was released on 15 December 2025 and the APS AI Plan in November 2025. These are Australian policy milestones, not universal requirements for public agencies elsewhere. Australian National Audit Office
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a reliable count would require
A defensible list of 41 would need to publish its entries and explain its inclusion criteria—for example, whether pilots count, how duplicate agency or supplier announcements are handled, and what evidence qualifies as operational use. Each entry would then need primary-source confirmation of its status, scope, outcomes and oversight. Until that information is available, “41” is a reported scope rather than a substantiated count, and the broad categories above should not be mistaken for 41 verified deployments.
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