Key takeaway
AI delivers measurable value when treated as a strategic capability grounded in governance and trusted data. Leaders should start by eliminating repetitive work, move to supporting judgement with evidence, and only then extend into new capabilities that shift the organisation’s performance baseline.
Table of Contents
What mindset shift do leaders need to make about AI - now, not next year?
Across government and critical industries, most leaders are still evaluating AI as if it were a software purchase: something that can be implemented, licensed and deployed. That framing misses the point. AI is not a tool to buy; it is a capability to cultivate—one that reshapes how data, policy and people interact to deliver outcomes.
For officials, this means anchoring AI programs within organisational strategy, not IT strategy. When executives lead with intent—tying AI use cases directly to service improvement, risk reduction or mission readiness—the results endure beyond election cycles or leadership changes. The foundation is data that is accurate, accessible and governed properly. As Curtis West observes, “AI isn’t magic—it’s maths. If you can’t explain the benefit in plain English, the fit isn’t clear yet.”
Sequencing also matters. Organisations that follow the Eliminate → Support → Extend model achieve momentum quickly and safely. They begin by removing repetitive, low-value work; next, they use AI to support professional judgement with reliable evidence; and finally, they extend into new capabilities once trust, oversight and skill are in place.
Where does AI remove friction officials actually feel day-to-day?
For many senior officials, friction takes the form of slow briefs, incomplete records and constant compliance demands. Every decision requires evidence drawn from fragmented data sources—emails, PDFs, spreadsheets—and each round of ministerial review compounds the delay. It’s not that people lack the will to deliver faster; it’s that systems weren’t built for the tempo of modern government.
AI can directly target those pinch points. In a briefing workflow, for example, AI can reconcile inputs from multiple sources, extract the key facts, and align them to existing policy positions. In record-keeping, models can tag and classify content in real time, closing gaps that usually appear only under audit. For compliance-heavy agencies, AI can screen every transaction or interaction—rather than a token sample—against relevant guidelines. This not only strengthens assurance but frees human reviewers to focus on anomalies and improvement, rather than paperwork.
The point is not to automate judgement but to accelerate it. When context and facts are assembled in seconds, not hours, decisions improve because staff spend more time assessing options and less time chasing information.
How can a health network check every call for compliance - without new headcount?
Imagine a large hospital network where thousands of calls flow through patient support and billing lines each week. Each conversation carries potential risk—privacy breaches, consent lapses, or missed escalation triggers—but only a fraction are ever reviewed. AI makes full coverage possible.
By transcribing calls into text and matching each interaction against policy and regulation, the system highlights only the exceptions that require human attention. Supervisors receive neatly packaged evidence sets—audio snippets, transcripts, and relevant clauses—while compliant calls feed directly into training material. Privacy and security teams maintain oversight because every step of the process, from de-identification to access logging, is transparent and traceable.
The result is not just faster auditing but smarter improvement. Managers can see where patterns of non-compliance emerge and design targeted coaching rather than generic retraining. Risk reduces, performance lifts, and the compliance team finally moves from firefighting to prevention. As West notes, “You can review every call, check decisions against policy, and surface breaches at scale.”
How can defence procurement cut evaluation time and strengthen assurance?
Procurement in defence and national security environments has always been complex—layered with export controls, sovereignty rules, and multi-year performance data scattered across silos. The process can stall under its own weight.
AI-supported analysis changes that. When data from previous contracts, supplier assessments and market soundings are brought into one secure environment, models can build side-by-side comparisons that highlight not only cost and capability but compliance posture and sovereign risk. Queries like “Which suppliers have prior cyber incidents?” or “What equipment meets AUKUS interoperability standards?” produce sourced evidence rather than speculative summaries.
Evaluation panels gain time, not just convenience. Instead of weeks of manual collation, they begin from a common evidence base, formatted for audit and ready for records management. Ministerial offices see transparent reasoning, and policy advisers can explain the decision path with confidence. The technology doesn’t replace due diligence—it concentrates it where it counts.
To explore this theme in depth, listen to our episode of the Intelligence; Optimised Podcast, where Llew Jury and Curtis West join Todd Crowley to discuss how treating AI as a capability – rather than a quick technological fix – can strengthen compliance, accelerate service delivery, and improve operational readiness across defence, health and infrastructure.
How can infrastructure operators coordinate incidents without the scramble?
In sectors like energy and transport, operational tempo can shift from calm to crisis in minutes. During a major weather event, for instance, control centres may face thousands of simultaneous alerts—sensor failures, social media reports, crew updates and public inquiries. Historically, this meant hurried triage, inconsistent messaging and patchy records.
With AI embedded into operations, information flows become coherent. Incoming data from multiple channels feed a single situational view, where models cluster related events, identify likely causes and draft updates within defined policy boundaries. Communications teams receive pre-filled templates that include only verified information, while regulators access a timestamped log of decisions and actions.
The gain is not just speed—it’s composure. When frontline staff see the same accurate information and share one version of the truth, response quality improves. After the event, the captured record forms the basis for transparent reporting and lessons learned, replacing hours of reconstruction with confidence built in.
What does a low-drama implementation look like in public and critical sectors?
The agencies and organisations that succeed with AI share a simple philosophy: start small, design deliberately and measure relentlessly.
- Frame the decision and output. Identify a decision that consistently runs late or causes frustration, and define the exact output—an executive brief, dashboard or decision log—that will demonstrate improvement.
- Inventory data and gaps. Catalogue what data already exists, where it resides and who controls it. For missing elements, specify the minimal dataset needed to start and the approvals required to access it.
- Start tiny. Form a small, cross-functional team with authority to experiment. Document the workflow on a single page and use familiar tools to reduce procurement drag.
- Wrap with governance on day one. Apply privacy, security and records controls from the outset. Align to the Australian Privacy Principles, the Protective Security Policy Framework and the Information Security Manual. Record every prompt and output with clear human checkpoints.
- Scale deliberately. When the pilot proves its value, embed it into core systems through APIs and structured workflows. Train staff on both capability and limits before broad deployment.
This measured approach avoids both hype and paralysis. It demonstrates value early, builds confidence in oversight, and turns AI from concept to competence.
Join the Early Access List
Secure first access to Vaxa Bureau and turn external chaos into precise, actionable insight for your organisation.
How do you keep it safe, auditable and policy-fit?
Public trust depends on governance as much as innovation. The risks of using AI in sensitive environments are well known: hallucinated facts, privacy breaches, and decisions made without accountability. Each can be mitigated with discipline and transparency.
Fact integrity comes first. Outputs should always include cited sources or confidence indicators so reviewers can validate claims. Human oversight remains essential, particularly for high-impact or externally visible decisions. Procurement contracts should define where data is processed, how it can be deleted, and what exit options exist if a provider changes terms.
Records management must be explicit. Prompts, model versions and outputs form part of the official record and must be stored in systems that meet Freedom of Information and audit requirements. Cultural change is equally important. Staff must see AI as an enabler, not a surveillance tool. As Llew Jury stresses, “Defence and health need AI governance and trusted data infrastructure. Top-down governance is critical.”
What should you measure from day one?
Measurement anchors credibility. The most effective programs start with a small set of metrics tied to outcomes, not activity. Time-to-brief, incident triage latency, procurement cycle time and rework rates are practical indicators of improvement. Others, like data readiness or adoption rates, track capability maturity over time.
Metrics must be observable and owned. Each should have a responsible manager and a review cadence that links to business planning or performance cycles. When numbers stagnate, the response should be to refine the workflow or data, not to add new slogans or technology layers. Transparency around results—what worked, what failed—builds the trust necessary for scaling across agencies and partners.
Why does this matter for Australia in the Indo-Pacific?
Australia operates within a region defined by complexity and interdependence. Supply chains cross multiple jurisdictions with distinct privacy and security regimes, while defence and infrastructure partnerships rely on information exchange that must be both rapid and controlled.
AI capability strengthens this balance. By enabling faster, evidence-based decisions, Australian agencies can respond to regional disruptions with agility and confidence. For defence, it supports readiness and interoperability without overexposing sensitive data. For infrastructure and health, it ensures continuity during crises while maintaining public accountability.
Most importantly, it reinforces resilience—the ability to act decisively without adding dependency risk. In a contested Indo-Pacific, that balance of speed and assurance is a strategic asset in its own right.
What should leaders do this week?
For government leaders, begin with one decision you own that always runs late. Gather the people who make it happen—analysts, records officers, risk managers—and run a short, structured trial to eliminate a single piece of manual work. Document the impact in time and confidence gained.
For industry executives, form a small taskforce across operations, IT and legal to test AI in a supportive capacity. Produce sourced summaries or evidence packs for your next board or regulator briefing and track how much faster the process becomes.
Both paths start the same way: by turning conversation into capability.
“The biggest misconception is that AI is a magic fix. It’s a strategic capability.” — Llew Jury
“Eliminate, Support, Extend reframes the work. Start with real problems and quick wins.” — Curtis West
Join the Early Access List
Secure first access to Vaxa Bureau and turn external chaos into precise, actionable insight for your organisation.
