AI is being used to flag activity in public spaces, support emergency warnings, monitor industrial processes, analyze battlefield data, and check the behavior of deployed software. A September 28, 2026 snapshot of AI Weekly’s catalogue lists 27 examples across eight sectors, but it mixes operating systems with pilots, announcements, reported outcomes, and work that was halted or reversed. The examples show what organizations are trying—not that these systems are equally mature, effective, or safe.
What the 27-deployment count means
AI Weekly’s September 28, 2026 catalogue snapshot lists 27 deployments. It classifies 18 as in production or having results, 13 as having a reported outcome, and two as halted or reversed. These are catalogue labels, not an independent audit of every system. An earlier rendering of the same page showed 21 entries, so the total should be read as a dated snapshot rather than a stable or exhaustive count. AI Weekly’s catalogue is a discovery source that brings together reporting and claims from different sources.
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The catalogue’s sector breakdown is its own classification, not a definitive taxonomy:
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| Sector | Entries in the September 28, 2026 snapshot |
|---|---|
| Government and public sector | 9 |
| Software and technology | 7 |
| Military | 5 |
| Automotive | 2 |
| Transportation | 1 |
| Manufacturing | 1 |
| Nonprofit | 1 |
| Science and research | 1 |
The systems also differ in purpose and consequence. A camera that flags a possible fight, a machine-learning tool for nuclear inspection, a flood-warning system, and a software safeguard that checks an AI model’s actions do not share one meaningful measure of “safety.”
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Examples in public safety and government
The catalogue includes public-space cameras, license-plate readers, facial recognition, smart glasses, and AI-supported emergency warnings. Their presence in the same category does not make their uses or risks equivalent. Surveillance systems may affect people who are not suspected of wrongdoing; warning systems are intended to help identify hazards. Both still need monitoring, but for different errors and consequences. AI Weekly’s entries describe the cases below.
| Example | What the catalogue reports | How to interpret it |
|---|---|---|
| Western Australia Police mobile facial-recognition trial | More than 131,000 faces scanned, 33 alerts, and a reported contribution to 19 arrests. | These are catalogue-reported activity and outcome figures, not a measurement of accuracy, crime reduction, or causal impact. |
| Alpharetta roadway cameras | 1.6 million images and 50,200 vehicles photographed in 21 days. | This is reported surveillance volume, not evidence that the system made roads safer. |
| Berlin police cameras | Cameras are described as flagging loitering, fighting, and abandoned packages. | The catalogue does not establish a comparable performance measure or an independently verified impact. |
| Florida Highway Patrol and a Flock license-plate-reader match | The catalogue describes an investigation in which a match reportedly led to a wrongful arrest. | The example highlights the consequences of an incorrect match; it should not be treated as evidence about all license-plate-reader deployments. |
| Other listed government examples | Western Australia Police’s trial, Flock readers in Troy, New York, DHS agency surveillance uses, Chinese police smart glasses, and South Africa’s AI-supported flash-flood warnings also appear. | The catalogue’s examples span surveillance and emergency response, with different affected people, failure modes, and oversight needs. |
The same catalogue says documented DHS AI use cases grew from 20 in 2022 to 238, attributing the figures to linked reporting. That is a reported count of documented use cases, not a count of proven safety benefits. More generally, scans, images, alerts, and arrests are not interchangeable measures: each needs its own denominator and context before it can support a claim about effectiveness.
AI safeguards in software and technology
Not every safety-monitoring system watches the physical world. The catalogue includes age-assurance classification, automated checks on AI tool calls, model safety classifiers, and other forms of runtime or deployment monitoring. These systems aim to identify a condition or behavior that warrants review or restriction, rather than detect a road hazard or inspect a component.
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The catalogue also includes work that was halted or changed. Such cases matter because safety controls can shape deployment decisions, but a catalogue record is not an independent audit of a safeguard’s coverage, false-positive rate, or ability to prevent harm. Specific claims about OpenAI, Anthropic, Meta, or other named companies should be understood as reports about particular systems and responses, not as general endorsements of their products. The catalogue’s dated entries provide the case-level descriptions.
Military and emergency sensing
Military examples include a UK Ministry of Defence contract announcement for a surveillance-drone system and Ukrainian battlefield-data platform pilots involving drone AI, analytics, and fibre-optic sensors. These examples span procurement and pilot activity; an announcement or pilot does not by itself establish mature operational performance.
The catalogue attributes to Avengers AI Labs’ platform a volume of more than 100,000 drone video feeds processed monthly and a claim of roughly 70% real-time enemy-target identification. Those figures are reported through a secondary catalogue and concern a high-consequence military application. They should not be read as independently validated performance: the available description does not provide a comparable validation method, denominator, or operating conditions. AI Weekly’s catalogue links the case and its underlying reporting.
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Vehicles, transport, industry, conservation, and public health
Outside government and military uses, the examples range from driver assistance to wildlife monitoring and public-health investigations. Their reported numbers describe different things and cannot be ranked as if they were results from a common test.
| Area and example | Reported use or figure | Evidence boundary |
|---|---|---|
| Automotive: aftermarket driver-assistance devices | The catalogue reports crashes, deaths, and injuries in connection with these devices. | That association does not show that the AI alone caused those incidents. |
| Automotive: Toyota’s Woven City | Listed as a pilot. | The catalogue does not establish a comparable safety outcome for the pilot. |
| Transportation: NATS and Google | Described as testing AI contrail forecasts to inform flight-altitude decisions. | A test is not evidence here of a measured reduction in contrails or other validated outcome. |
| Manufacturing: Agility Robotics and NVIDIA | The catalogue names Agility Robotics’ adoption of NVIDIA’s robot-safety system. | The listing establishes the reported adoption, not an independently verified safety result. |
| Conservation: EarthRanger | Reported to span 900 sites in 90 countries. | This is a scale figure attributed to the catalogue, not a measured conservation outcome. |
| Science and research: ESA EcoPulse | Reported accuracy of 87% for detecting animal panic in satellite imagery. | The available description does not provide the validation set or measurement conditions, so the percentage cannot be compared with other AI accuracy claims. |
| Public health: TowerScout | CDC names TowerScout’s computer-vision work in investigations related to Legionnaires’ disease. | CDC’s success-story summary does not quantify the example’s impact. |
CDC’s AI Success Stories also notes that CDC maintains an annual HHS AI Use Case Inventory. The CDC page identifies TowerScout’s role but does not supply a numerical outcome for it.
What official guidance says about monitoring AI after deployment
Deployment is not the end of safety work. In a March 9, 2026 announcement, NIST said post-deployment monitoring remains a fragmented field and matters because AI systems can introduce variability and behave unpredictably in real settings. Its AI 800-4 report organizes six common categories of monitoring. The announcement directly describes two:
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- 【Tried-and-True Safe Guard】This one-stop security solution can work with TVI, AHD, CVI, CVBS & IP cameras, the kit includes 1080P cams. The 8CH 3K lite DVR can hook up with 1080P@30fps or 3K/5MP@20fps cams. Therefore, you can also DIY it with other cameras in your home.
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- Functionality monitoring: measuring a system’s functions, capabilities, and features to check that it works as intended.
- Operational monitoring: measuring infrastructure components to check service consistency.
NIST’s broader point is that monitoring can include incident monitoring and field studies, not just a one-time pre-deployment test. Its announcement does not prescribe one universal monitoring recipe for every system. NIST’s overview of AI 800-4 explains the motivation and report scope.
The UK Office for Nuclear Regulation (ONR) offers a sector-specific example. Its May 2026 account describes a regulatory-sandbox project that examined two supervised machine-learning applications for analyzing and interpreting computer-vision data, with potential use in monitoring, inspection, and safety. More than 20 people from 10 organizations discussed four industry examples through technical workshops. ONR explicitly says the report reflects project discussions and does not represent a formal regulatory position; participation in the sandbox should not be mistaken for regulatory approval of a system. ONR’s account describes the project and its limits.
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How to judge a deployment report
When an organization says AI is making a process safer, ask what was measured and what happened after the system flagged something. A useful comparison needs more than a headline number:
- Purpose and hazard: What harm, failure, or condition is the system intended to detect or prevent?
- Status: Is the system operating, being piloted, newly announced, or discontinued? Does the reported evidence come from a trial or routine use?
- Inputs and signal: Does it analyze images, video, text, sensor data, or model behavior? What specifically triggers a warning or alert?
- Response: Does a person review the alert, does software act on it, or does the system only inform a decision? What happens when a person disagrees with or is harmed by the result?
- Evidence: Who reports the outcome, when, and with what denominator? Is the figure a measure of system volume, an operational outcome, or evidence of a causal effect?
- People and governance: Who is monitored or affected? What errors, privacy risks, oversight, and routes to challenge a decision matter in this setting?
- Changing conditions: Could different field conditions, infrastructure problems, or system updates reduce performance after deployment?
These questions follow from the differences among the catalogue’s cases and from NIST’s emphasis on monitoring systems in use. A count of images, alerts, deployments, or sites may describe scale; it does not alone establish that harm was reduced. An accuracy percentage also needs the task, dataset, denominator, and validation method before it can be compared with another system.
There is no single cross-sector effectiveness score in these examples. Their figures measure unlike things, and the catalogue combines claims and outcomes from varied source types. Use it to find cases, then read the linked source for the specific deployment before drawing a conclusion about safety or performance.
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