What Australia's new public sector Chief AI Officers need to get right

By July 2026, every Australian Government department and agency will need to appoint a Chief AI Officer (CAIO) from their existing senior leadership ranks, a first for the APAC public sector.

More than a new title in an org chart, it’s a line in the sand for whether the Australian Public Service (APS) can move from cautious pilots to confident, scalable adoption of AI and AI agents.

However, CAIOs in government will face a very different reality to their private sector counterparts. In business, AI leaders can often optimise for speed, competitive advantage and shareholder returns. In the APS, CAIOs must optimise for citizens’ rights, transparency, security, procurement constraints, cross-agency consistency, and the political and legal scrutiny that comes with spending public money.

In 2026, three priorities will define whether incoming public sector CAIOs can turn a mandate into momentum, or fall back into an AI adoption deadlock.

Governing AI agents in a high-trust, high-scrutiny environment

‘Ship to learn’ is an important part of the innovation cycle in the private sector. It moves the question from ‘what do we ship?’ to ‘how can we learn fastest?’ The public sector operates under a very different risk profile and does not always get to move fast and break things, particularly for critical systems that underpin things like welfare payments, immigration pathways, health services or national security workflows.

Private sector AI leaders can often accept some opacity if performance is strong; APS CAIOs cannot. They will need to define clear accountability for human oversight, approval gates, logging and evaluation, especially as more teams experiment with agentic workflows.

The opportunity is significant, but so is the risk of what leaders are increasingly calling ‘agent chaos’: fragmented tools, inconsistent controls, duplicated spending, and uneven safety standards across agencies.

Because of this, CAIOs will be expected to build an integrated, collaborative and authorising environment that gives teams necessary AI controls, ensuring agent tasks are reviewable, accountable and explainable, and ultimately aligned to whole-of-government expectations under the APS AI Plan and related guidance. Governance has to be built in, not added later.

Data, privacy and security constraints that are fundamentally tougher than enterprise

Every CAIO will inherit the hardest version of the data problem: legacy systems, inconsistent data quality, sensitive information, and complex sharing constraints across jurisdictions and portfolios. The practical question will be less ‘what can AI do?’ and more ‘what data can we safely use, where can it be processed, and how do we prevent leakage?’

This is where the APS differs sharply from private enterprise. A bank may choose to quarantine certain datasets, but a government agency may have entire categories of information that are politically, legally or ethically impossible to expose to external models or unmanaged environments.

As a result, CAIOs will need to champion secure, well-governed platforms and patterns that allow innovation without compromising sovereignty and security. For example, strict data residency rules have typically held back the public sector from embracing the cloud and modernising at the same speed as private enterprises. Getting governance frameworks in place and meeting data residency requirements should be a core priority for CAIOs because cloud is fundamental to AI; it’s a fundamental driver of efficiency. Doing so will set the course for greater productivity, competitiveness and long-term innovation in the public sector.

Capability, change management and proving ROI without losing talent

CAIOs are being appointed to drive transformation, not just compliance, and that means bringing people on the journey.

The APS AI Plan includes expectations around uplifting AI literacy and training at scale, alongside senior leader capability-building. Government has also signalled that consultation with staff is a priority as AI adoption increases.

But CAIOs will be operating in an environment where AI capability is not a ‘nice to have’. It’s quickly becoming the baseline expectation for technical talent, with the 2025 GitHub Octoverse report showing that nearly 80% of new developers on GitHub use Copilot in their first week, signalling how quickly AI-native workflows are becoming normal.

This is why I believe an agentic future can make government a talent magnet. AI-powered workflows can attract developers who have historically avoided the public sector due to legacy systems and slow delivery cycles; it could also supercharge existing talent to deliver better services.

The CAIO’s challenge will be to make this measurable: clearer throughput, reduced backlogs, faster delivery, higher quality, and better citizen outcomes, while keeping controls tight and confidence high. This is critical given the public sector is burdened with fragmented legacy systems and outdated codebases, like COBOL, which are both maintained by experts who are exiting the workforce and foreign to entry-level talent. This is where CAIOs can have a tangible impact: using AI to bridge the knowledge gap for early career developers and accelerating modernisation across the public service.

Australia’s moment for CAIO leadership The July 2026 deadline sets a precedent for the rest of the APAC market. If done successfully, Australia has an opportunity to lead in AI public sector adoption. The CAIOs who emerge as leaders will treat governance as an enabler, not a blocker. They will standardise safe foundations, unlock practical AI agent use cases, and build a culture where experimentation is encouraged inside clear guardrails. That is how the APS moves from AI deadlock to large-scale transformation and delivers the modern public services Australians expect.

Top image credit: iStock.com/miniseries