Published 5 August 2026
AI governance in Australia is entering a new phase. For the past two years, much of the AI conversation has focused on the technology itself. Which model is best? What can it generate? How quickly is it improving? Which jobs will it change?
Recently Prime Minister Anthony Albanese announced the creation of a dedicated Office of AI within the Department of the Prime Minister and Cabinet, alongside plans to introduce clear, consistent mandatory Australian standards for AI.
This marks a significant turning point as Australia’s AI conversation is beginning to shift from experimentation towards accountability.
So what does this mean for organisations? I think the practical takeaway here is that there is no reason to wait for regulation in order to understand how to implement and use AI well. Let’s get back to the foundations of ensuring that you have trusted information, clear ownership, practical governance and a way to measure whether AI is creating real business value.
In this blog, Linda explores Australia’s shift from AI experimentation to accountability, and why AI governance, workforce readiness and trusted information are becoming critical foundations for successful AI adoption.
Why this matters?
To me, the most important part of this announcement was the recognition that AI success will depend just as much on governance, people and organisational readiness as it does on the technology itself.
I’ve seen this repeatedly in the organisations I work with as they introduce AI and rethink how work gets done. Giving people access to a new platform or capability is often the easiest part. Creating the conditions for them to use it confidently, responsibly and in a way that delivers real business value is much harder.
Microsoft’s 2026 Work Trend Index reinforces this. Its research found that organisational factors such as culture, manager support and talent practices account for more than twice the reported impact of individual mindset and behaviour on AI outcomes.
Governance is what innovation sustainable.
This brings me back to something I have written about in previous posts: governance is too often framed as the thing that slows transformation down, when in reality, without trusted information, clear ownership and practical guardrails, AI can scale confusion just as quickly as it scales productivity.
When governance is treated as an enabler, organisations can move forward with greater confidence. People can trust the information being used, understand who is accountable, know how risks will be managed and recognise where human judgement still needs to sit.
Done well, governance provides a clear framework for moving AI initiatives beyond experimentation and embedding them as sustainable business capabilities.
Trusted information, clear ownership and well-designed processes allow AI to enhance productivity and support better decision-making. By contrast, duplicated, outdated or poorly governed information, can enable AI to accelerate confusion at scale.
When employees understand how to use AI, validate its outputs and apply it appropriately, it can strengthen organisational capability. When AI tools are introduced without clear expectations, training, or oversight, they can amplify risk.
Governance will not remove every risk. What it does is give organisations a clear way to identify, manage and respond to risks as the technology and the way we use it continues to evolve.
National standards will not replace organisational readiness
The proposed Australian Standards may create greater clarity and consistency, but it cannot decide which business problems are worth solving, which use cases will create meaningful value, who should own an AI-enabled process or whether employees are equipped to use the technology responsibly.
Those decisions require bringing together business leaders, technology, data, cyber security, legal, risk, HR, procurement, knowledge management and change, and not treating AI as a standalone IT program.
Trusted AI starts with trusted information
One of the most important, and sometimes least exciting, parts of AI readiness is the quality of the information underneath it. AI cannot compensate for unclear, outdated or contradictory content. It can only interpret what already exists.
This is why information architecture, content ownership, access controls, review cycles, metadata and knowledge governance have become even more important.
An organisation may have access to an excellent AI ptools, but if employees cannot identify which policy is current, who owns a document or where authoritative information should live, AI will not magically fix that.
Governance therefore needs to extend beyond the AI model. It must include the information, processes and decisions the technology relies upon. For more on this check out Thomas’ blog here
AI literacy must go beyond prompting
The other essential capability is people.
AI literacy is not simply teaching employees how to write better prompts. It means understanding when AI is appropriate, when human judgement is required, how to verify an answer, how to protect sensitive information, how to recognise bias or uncertainty and how to escalate concerns.
Training also needs to reflect the work people actually do. A generic prompt library will only take an organisation so far. Employees need practical, role-based support that helps them apply AI within their own workflows and responsibilities.
The biggest productivity gains will come from redesigning work, removing unnecessary friction and using AI deliberately within tasks, decisions and services. For more on this, check out Kasey’s blog talking all things change and adoption in the AI era.
What this looks like in practice
We saw this with Cancer Council NSW when it developed Genie, a policy agent built using Microsoft Copilot Studio. Approved policy content was established as the source of truth, ownership was assigned to a content manager, and citations, feedback, analytics and escalation pathways were built into the experience from the beginning. Governance was not added after the agent was built; it was part of what made the service trustworthy and sustainable.
At Catholic Healthcare, Copilot adoption was supported by security reviews, practical role-based training, ongoing support and a network of champions. Within the first month, the program achieved an 85% adoption rate, while more than 80% of participants reported increased confidence following training.
The examples are different, but the lesson is the same. Sustainable AI adoption comes from combining the technology with trusted information, clear ownership, relevant training and a deliberate way to measure value.
What organisations can do now
The establishment of the Office of AI is an interesting move but the announcement itself will not create productivity, trust or competitive advantage. That will depend on implementation.
For organisations, the work starts with practical questions:
- Which business problems are genuinely worth solving with AI?
- Is the information supporting those use cases accurate, current and clearly owned?
- Do employees understand how to use AI responsibly?
- Can we demonstrate whether AI is producing measurable value?
How Engage Squared can help
Wherever your organisation is on its AI journey, our team can help you assess your identify the right next step and turn experimentation into measurable value.
About the author
Linda Brunetti is a Senior Consultant in Engage Squared’s Modern Work & Security practice, helping organisations build digital workplaces that are ready for the future of work. With extensive experience in intranet strategy, information architecture, governance, and employee experience, Linda works with organisations to create digital environments that are intuitive, scalable, and designed around the needs of their people. Passionate about the intersection of technology, culture, and AI, she helps clients build strong foundations that improve collaboration today while preparing for the opportunities of tomorrow.
