Closing the reliability gap: the AI practices Australian organisations need now

Australia’s National AI Plan lays out an ambitious whole-of-economy vision. Achieving its goals depends on whether organisations can make AI reliable, contextual and consistently governed, something many deployments still struggle to deliver.

The plan, launched late last year, has three pillars at its heart: capturing the opportunities of AI, spreading the benefits, and keeping Australians safe. Together, they represent the makings of an optimistic future for AI in this country.

However, we need to take a step back and reflect upon the current state of AI adoption in Australia if we’re to achieve the goals laid out in the plan. For organisations, unlocking AI’s potential means building on a unified, modern data foundation and embedding purpose‑built AI that’s continuously evaluated and responsibly deployed.

AI built for real workflows

The Australian Government’s ability to achieve many of its major goals relies on established AI use cases that demonstrably drive business and economic outcomes. This requires AI applications informed by, and designed for, real workflows.

Enterprises are complex, and their workflows typically have many steps. AI deployments built for real workflows should support and reflect this. AI agents capable of handling discrete tasks within these workflows can help enterprises deploy AI in ways that fit their operations.

This is supported by multi-agent orchestration, which aligns with one of the nine actions the government outlined in its National AI Plan — Action 4: Scale AI Adoption. Multi-agent orchestration provides a solid on-ramp for businesses across Australia to integrate AI seamlessly into workflows.

Bringing AI into real workflows also means making it more accessible. The National AI Plan communicates the need to support SMEs and upskill the workforce which recognises that AI’s value depends on participation from everyone, not just technical specialists.

Allowing employees to interact with trusted organisational data using natural language tools can remove the key barrier to use for non-technical teams while maintaining strong governance and security.

Customised AI models for business-specific outcomes

For business-specific needs, AI applications trained solely on public internet data rarely perform well. To achieve meaningful outcomes from AI adoption, domain-specific agents, customised with proprietary and sovereign data and managed under strong governance and security controls, offer a promising path forward.

The rise of these domain-specific agents is one of the major trends emerging in 2026. These systems go beyond automation, interpreting internal rules and compliance requirements while upholding sovereignty standards. Having this control over data and models supports compliance of ethical and legal guidelines, minimises risk and safeguards competitive advantage.

The data controls necessary for domain-specific agents supports the plan’s Action 2: Backing Australia’s AI capability. To construct sovereign AI capability for Australia, it will depend on sovereign digital infrastructure and data sets as AI outcomes are shaped by local context.

The upside of this is that organisations have the opportunity to design AI systems anchored in their own unique context. Databricks’ 2026 State of AI Agents report found companies using AI governance put over 12 times more AI projects into production. Success depends on embedding local rules, data and constraints into systems that can be trusted to perform reliably in real-world environments — turning AI from experimentation into sustained productivity gains.

Continuous evaluation for better results

All of the advances AI has the potential to drive are worth little if they can’t be achieved safely or securely. Promoting responsible practices and mitigating harms are two of the three actions outlined by the government in support of keeping Australians safe, one of the three pillars outlined in the National AI Plan. And for good reason: AI is powerful, but it opens up risks.

Models that may look reliable in training sometimes degrade when live data is fed into them or drift as inputs change. Reliability can erode quickly without constant evaluation. By adopting an evaluation‑centric approach, testing AI systems against real tasks, compliance standards and evolving datasets, organisations can manage these risks and ensure consistency.

This also aligns with expectations from the forthcoming AI Safety Institute. As more enterprises embrace ongoing testing and benchmarking, they’ll build trust and reduce uncertainty in deployment.

Together, these practices will put Australian businesses on the path to optimal AI implementations that are reliable, safe and actually result in tangible outcomes and, as a result, contribute to the government’s AI goals. Strong governance, domain-aware agents and a cycle of evaluation can set the country on a trajectory of AI leadership.

Image credit: iStock.com/Just_Super