When the work is too complex for AI, regulators are proving otherwise

By Sam Daley

AI is helping regulators across Australia move from enforcing compliance to enabling it. This article explores how early pilots are evolving into scalable, sovereign platforms that save time, strengthen oversight and rebuild trust, showing how human-centred design and technology together create the next generation of confident, transparent regulation.

How regulators are using AI to support human judgement

For years, AI was viewed as something suited to simple tasks rather than work that depends on judgement, context and fairness. Many regulators proved that AI could help with productivity but not with the deeper decisions that define public confidence. That belief is now changing. Around the world and across Australia, regulators are discovering that AI can support high stakes decisions based on complex evidence and shifting risk.

This shift is happening as regulatory complexity grows. The federal government has acknowledged that interpreting regulation is difficult for people and businesses to navigate. Leaders are expected to reduce burden while protecting the community, simplify systems without weakening oversight, build awareness and understanding of obligations, and respond quickly to emerging issues while maintaining fairness.

This is the context in which regulators are rethinking what AI can do.

Why early pilots underestimated the role of AI in complex regulation

Australian regulators have been experimenting with AI for years. Many of the more successful examples are in data analysis, case sorting and early risk detection. Yet most of these tools were created for general use and did not reflect the nuance and accountability required in regulatory decisions.

In doing so, early pilots often reinforced the belief that AI belonged at the margins of regulatory work. They demonstrated efficiency in basic tasks, but not how AI could support judgement, evidence and accountability under real regulatory conditions. Pilots focused on generalised tools, lacked explainability or alignment with decision pathways needed for complex decision-making. So you'd be forgiven for thinking AI couldn't be trusted with more complex work.

The Mandarin and Liquid’s Let’s get real about AI survey highlighted this challenge. Many public servants felt ready to use AI personally, while fewer believed their organisation was prepared to support them. In regulatory environments where consistency, fairness and transparency matter, this gap creates understandable caution.

Regulators worldwide are showing that AI can support complex work

Regulators in Australia and overseas are now starting to use AI in ways that show how it can support complex, judgement-heavy work. In several Australian agencies, AI is now preparing evidence for licensing decisions by reviewing large volumes of submitted material and drawing attention to details that require expert review. This allows staff to focus on assessments that involve interpretation and discretion, while routine checks run in the background.

In food and commodity oversight, AI systems monitor global information sources and surface issues that are relevant to Australian supply chains. Officers gain earlier insight into overseas events and can adjust planning and intervention with greater confidence.

Similar shifts are happening internationally. The United States Securities and Exchange Commission uses AI to analyse trading data and detect unusual activity. Analysts review the alerts, work through the flagged cases and focus effort on matters that signal higher risk. Singapore’s National Environment Agency uses AI to highlight likely hotspots for environmental non-compliance. Officers can direct inspections to areas with stronger signals and respond to issues at a more effective moment.

Together, these examples show how regulators are using AI to work with large volumes of unstructured information, detect early changes in complex systems and prepare clearer material for human decision makers. AI improves visibility and helps staff concentrate on decisions that require expertise, while maintaining the transparency and accountability that regulatory work demands.

A new model of regulatory intelligence

Regulation has long relied on retrospective information. Reports and complaints arrive after the fact. Analysts reconstruct events to understand what occurred. This approach is careful, but it limits the ability to act early.

AI designed for public accountability presents a different model. It processes information earlier, connects signals across systems and highlights emerging concerns that may otherwise remain unnoticed. Human judgement remains central and AI improves the clarity with which that judgement can be applied.

Regulators can compare submissions at scale, detect inconsistencies in real time and focus resources where they make the greatest difference. Every AI assisted insight can be examined. Staff can understand how the conclusion was formed and adjust it when necessary. This level of visibility strengthens both internal confidence and public trust.

The shift from experimentation to enablement

As regulatory AI moves beyond pilots, several principles are emerging.

Agencies can begin with the problems that matter most. AI creates value when it reduces administrative load or provides earlier insight into emerging issues. Evidence preparation and high-volume document handling are practical starting points.

Governance needs to support responsible progress. Clear accountabilities, well-structured assurance and risk tiered frameworks allow agencies to adopt AI with confidence.

Shared capability can strengthen the whole sector. Many regulators face similar challenges and can benefit from common platforms for data preparation, validation and oversight.

Transparency should be embedded from the outset. When every AI assisted outcome is open to explanation and review, and impacted parties have clear channels to contest decisions, confidence grows inside agencies and across the community.

The same AI capabilities that can help regulators in their work can also be adapted for customers, supporting a more preventative approach to compliance, further reducing burden on regulators and customers alike.

A more confident and resilient regulatory future

AI is beginning to support work that once seemed beyond reach. It prepares clearer evidence, surfaces critical signals and helps staff concentrate on the decisions that demand expertise. It supports proportionate action and strengthens the way regulators engage with industry and community.

When design and technology work together, regulators gain the clarity they need to act with confidence. Pilots have shown what AI can achieve. Platforms shaped for complexity will show what it can sustain.

Australia now has an opportunity to move from experimentation to enablement and build a regulatory system that is more consistent, more resilient and more trusted. Importantly, giving regulators the confidence to use AI as a tool to support human judgement when faced with complex evidence, shifting risk and high stakes decisions.

Learn how Liquid supports modern regulation
See how we help regulators introduce AI that fits real workflows and strengthens oversight.
Visit https://www.liquidinteractive.com.au/regulators

CTAs

Register for the upcoming webinar
Join Liquid and The Mandarin for a practical session on making AI real for regulators today.
Save your place (link TBC)

Express interest in the Executive Roundtable for Regulators
A small, invitation-only discussion for senior leaders exploring responsible AI adoption in high-stakes environments.
Submit an EOI (link TBC)

OLD VERSIONS (scaling safely focus)

Scaling AI safely in the regulatory sector: From experimentation to enablement

Australia’s regulators are entering a new phase of AI maturity. The challenge is no longer whether the technology works, but how to embed it into the fabric of regulation in ways that strengthen trust, transparency and accountability. The next chapter of regulatory transformation is about culture as much as it is about capability.

From pilots to platforms

Across Australia’s regulatory sector, artificial intelligence is no longer a concept on the horizon. It is already reshaping how agencies process applications, assess evidence and monitor risk. Early pilots have proven that AI delivers measurable improvements, although many have also highlighted the limits of isolated progress. The future lies in establishing the platforms that allow AI to scale safely and consistently.

For regulators, scaling AI responsibly means building it into the systems that shape how regulation operates. It also means reframing AI as an enabler that helps both regulators and the regulated achieve better outcomes together.

The problem with pilots

The Let’s Get Real About AI survey, a collaboration between Liquid and The Mandarin, revealed a pattern that is particularly relevant to regulators. While most public servants feel personally ready to use AI, fewer than half believe their organisation is equipped to support them.

This readiness gap is especially visible in regulation, where trials have demonstrated AI’s capability but rarely how it can endure. Across Australia, agencies have tested AI to automate document handling, triage cases and analyse risk. Each proof of concept delivers useful insight, yet many fail to establish the shared infrastructure needed for scale. Without consistent data models, assurance processes and governance pathways, lessons are learned repeatedly rather than embedded.

Pilots play an important role in discovery, but lasting progress depends on government converting that learning into sustainable capability.

The platform difference

A platform approach brings stability and scale. It creates a foundation that can support multiple applications across regulatory functions, from licence processing to risk analysis. Shared data pipelines, model management, audit controls and security standards replace duplication with consistency and control.

This shift is already delivering results across several sectors. In agriculture and primary industries, AI now automates the processing of thousands of licence submissions. Applications that once required manual verification and site inspection are completed in minutes. Staff can dedicate their expertise to complex decisions that demand discretion and professional judgement, while regulated businesses benefit from quicker turnaround times.

In export and food safety, AI-driven monitoring now scans millions of global data points each day, identifying only the issues relevant to Australia’s supply chain. Tasks that once consumed hundreds of hours of manual review now run continuously, allowing earlier intervention and greater resilience.

These systems are already operating within secure and sovereign government environments that meet strict standards. Every output is traceable and auditable. Each decision can be explained and reviewed. AI is strengthening accountability through visibility.

A new model for regulatory intelligence

Regulation has traditionally been defined by retrospective oversight. Information arrives through audits, reports or complaints, and analysts work backwards to reconstruct what happened and why. It is careful and deliberate, but it is also slow. In a world where risk evolves in real time, this approach leaves government one step behind.

A new model of regulatory intelligence is now emerging, driven by AI platforms built for public accountability. These systems bring together functions that were once manual and fragmented, such as data ingestion, classification, analysis and decision support, within a single secure environment. They are not designed to replace human expertise but to amplify it.

AI is now enabling continuous and proactive oversight. By connecting information across systems and data formats, regulators can identify trends and risks earlier, respond faster and target interventions with precision.

AI platforms can compare submissions, identify inconsistencies and surface patterns that signal emerging risks. They triage cases in real time and highlight where human review is most valuable. This creates a more agile, risk-based model of regulation that is grounded in evidence and guided by professional judgement.

Every AI recommendation is transparent and open to review. Audit trails record what information was used and how each conclusion was reached. Staff retain full oversight and can interrogate or override AI outputs whenever required. The technology supports judgement rather than displacing it.

Transparency sits at the heart of this new model. When the regulated community can see that decisions are based on consistent evidence, confidence in the system grows.

Building trust through enablement

The most successful regulatory transformations share a common goal: a shift from enforcement to enablement.

Liquid’s work across the regulatory landscape shows that trust is built when regulators change how people experience regulation. In one state agency, a major human-centred transformation project reshaped the organisation’s purpose. Through research and co-design, the regulator developed a more collaborative approach that focused on helping businesses meet their obligations. The result was a stronger, more cooperative relationship with the community and industry it serves.

That same principle applies to AI. When used well, AI helps regulators create clarity, consistency and responsiveness. It removes friction and uncertainty, giving people confidence that they are being treated fairly. The tone of regulation changes from punitive to constructive.

Trust grows through daily interactions where technology helps government act with openness and reliability.

From experimentation to enablement

To scale AI safely and sustainably, regulators need to align culture, capability and technology. Four principles define this shift.

Start with the problems that matter most. Focus AI on the friction points that consume time or constrain service delivery. Administrative burden, data reconciliation and document processing are high-value starting points that build trust through results.

Build enabling governance. Governance must provide both structure and support. Risk-tiered frameworks, clear accountability and well-defined assurance processes create the conditions for responsible innovation.

Share capability. Regulators can collaborate through shared platforms that deliver core services such as data validation and assurance, while still allowing local adaptation. Shared capability reduces duplication and strengthens collective maturity.

Embed transparency. Every AI-enabled decision should be explainable and traceable. Designing transparency into the platform from the outset strengthens legitimacy and public confidence.

Scaling safely, with purpose

AI is reshaping the meaning of effective regulation. It allows oversight to be faster, smarter and more consistent. But its true value lies in how it strengthens relationships between government, business and community.

When human-centred design and AI come together, regulators become partners in public value creation. They protect more effectively because they engage more constructively. They act faster because they are informed earlier. And they build trust because they use technology transparently, with purpose and care.

The pilot phase has shown what AI can achieve. The platform phase will prove what it can sustain. In this evolution, government has an opportunity to create a more collaborative, confident and trusted model of regulation for the future.

When the work is too complex for AI, regulators are proving otherwise

Sign up to our newsletter.