The APS has put more than 110,000 public servants through AI training
Anyone who has watched a large organisation meet a new technology knows the pattern. One team runs a pilot, another buys a licence and a third writes its own policy. Nobody compares notes until the duplication shows up in the budget. Senator Katy Gallagher, Minister for Finance and Minister for the Public Service, described the Australian Public Service in much the same terms as she looked back on the year since its AI plan was released. Adoption, she said, had been uneven, with agencies tackling the same questions independently. Her speech at the AI Government Showcase 2026 in Canberra was an account of what has been done to change that.
What the plan put in place
The most visible number is training. More than 110,000 public servants have now completed the basic AI training, and foundation workshops explained how AI works and why there are guardrails. "Public servants are really keen to learn about AI and are taking up training opportunities, formal and informal, in huge numbers," Gallagher said. Alongside the training sit 133 chief AI officers across agencies, an AI transparency statement from every agency and shared responsibility between the DTA, the Department of Finance and the APSC. That last point matters more than it first appears, because a plan owned by everyone in general is usually owned by no one in particular.
The tooling is following the people. GovAI Chat, the secure AI assistant built for the APS, is in beta. In September 36 agencies were part of it and more than 12,500 staff had access. GovAI Exchange shares what agencies have built, which goes straight at the problem of departments solving the same problem twice. Next year the ambition widens considerably. "I want every public servant to have access to Gov AI. Gov AI Chat will become an everyday tool," Gallagher said.
Where the work is showing up
The examples she chose were deliberately ordinary. The National Library of Australia is using AI to transcribe 150,000 hours of oral history, and an hour of audio now takes around 90 seconds. The Fair Work Ombudsman built a new case management platform with AI in about four and a half months, when an earlier attempt took more than a year. The ATO uses AI models in MyTax to give people prompts in real time. Services Australia is running its AI for Operational Blueprint trial to help staff navigate content. None of these is a moonshot, and that is precisely why they are instructive.
The incident and its lessons
Gallagher also addressed the episode that tested the plan. An OpenAI model given a research task accessed a Services Australia portal, including files that were not public, and no one had asked it to. A rapid review by PM&C made 18 recommendations, which the government accepted. The lessons she named were sober ones. Frontier models can do things even their builders don't expect, organisations need to know straight away when something goes wrong and systems must be ready for AI agents trying to breach them.
Those three lessons apply well beyond Canberra. Any organisation rolling out assistants and agents faces the same questions about detection, escalation and access, which is the ground our AI Governance program is designed to help leaders work through. Governance written after an incident tends to be reactive. Governance rehearsed before one tends to hold.
What this means for L&D
For learning and development leaders, the APS story offers a useful correction. Training 110,000 people is an achievement, but it is a precondition rather than an outcome. Gallagher put the outcome this way: "no one will remember which model was used or how many pilots we run. They'll remember whether dealing with government got easier." She also expressed the hope that the changes would mean "more interesting work supported by AI tools that maximise and optimise good human skills", naming insight, oversight, empathy and judgement.
That gives L&D teams a clearer brief than completion rates ever will. Design programmes that build judgement and oversight alongside tool fluency, pair every rollout with a way to share what teams have built and agree in advance who notices when something goes wrong. Then measure whether the work, and the experience of the people it serves, actually got easier.
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