Australian businesses are under pressure to turn AI investment into measurable outcomes: higher productivity, faster decisions and a workforce prepared for a more autonomous way of working. Yet a significant number remain in pilots and proof-of-concepts, unable to move confidently into enterprise-scale deployment.
The barrier is rarely the technology. More often it is the quality, governance and readiness of the information the technology depends on. When organisations rush to scale AI without well-managed data, they risk multiplying their existing problems. Inaccurate outputs, security risks, and compliance breaches can derail even the most promising initiatives. The good news is that businesses don’t need to choose between innovation and information governance. They need a clearer path for connecting the two.
The AI Scaling Paradox in Australia
Australian organisations are being pushed to move from AI experimentation to enterprise-scale deployment, and the appetite is there. Research by AWS found 72% of businesses believe responsibly deployed AI will deliver greater economic growth and productivity. Yet most remain in experimentation or pilots rather than scaling AI across the organisation.
The same research found 37% are still exploring or experimenting with AI, while only 18% have fully integrated it into their operations and strategies. Readiness for the next wave is lower again: only 22% feel fully or very ready to adopt next-generation AI technologies. The appetite to scale AI is running well ahead of the readiness to do so.
Information readiness is a large part of the reason. An Insight’s report found more than half of Australian organisations (53%) said their data was only somewhat ready for AI, while another 20% said it was not ready at all.
The information required to power faster decisions and better customer outcomes sits across disconnected systems, legacy platforms and unstructured repositories, often with limited visibility of what is there or whether it can be relied on. AI cannot scale reliably on information that is poorly understood, inconsistently managed or inadequately protected.
AI Readiness Is a Data Governance Issue
Before asking what AI can do, organisations must understand the information it will draw on: what data they hold, where it sits, who is accountable for it, whether it is current and accurate, and whether it can be safely used in an AI environment.
Without that context, organisations risk scaling unreliable outputs, exposing sensitive information and undermining trust in AI before it has had the chance to deliver value. When information is fragmented, duplicated, out of date or inadequately protected, AI does not correct those problems. It scales them.
Our observations from Australia’s CIO Summit earlier this year point to the same conclusion. Organisations first need to understand what information they have, to determine whether they should still have it, who should have access to it and whether AI should be allowed to use it.
Trust needs to be built into the data and its lineage from day zero, before it is ingested and used by AI. In practice, that means cleansing legacy and unstructured information, applying retention and disposition policies before ingestion, and treating governance as a continuous discipline rather than a one-off project.
The next wave of AI raises the stakes. Globally, Deloitte’s State of AI in the Enterprise found 74% of organisations plan to deploy agentic AI within two years, yet only 21% report having a mature governance model for autonomous agents. When an AI agent retrieves information, makes decisions and triggers downstream processes, permissions, policies and auditability need to travel with the information wherever it is used.
Actionable Steps For Leaders
To move past stalled pilots without exposing the business to unnecessary risk, executive teams should focus on three initial priorities:
1. Pivot to a decision-first strategy. Stop chasing the technology. Too many organisations begin by asking, “Where can we use AI?” A more useful question is, “Where do we need better decisions?” Working back from those decisions, leaders can prioritise use cases with a clear business outcome, an accountable owner and defined information requirements.
2. Actively assess your organisation’s AI skill awareness. Before mandating tool adoption from the top down, leaders need to know where their people actually are: who is already using AI, who is confident and where the gaps sit. From there, create an environment where teams can safely build their knowledge and skills, and understand the responsibilities that come with AI-enabled decisions.
3. Ensure you have customer trust. Customers rightly expect organisations to understand where their information is, how it is being used and how it is protected. AI increases both the opportunity and the responsibility that comes with managing that information. One Australian organisation learned this after launching a customer-facing chatbot that worked technically but had to ask customers for contact details and answers to security questions. Customers were unwilling to hand those to an AI agent, adoption stalled, and the chatbot was withdrawn. Trust has to be designed into the way organisations adopt AI from the beginning. It cannot be added at the end as a compliance exercise. That means clear accountability, strong information governance, appropriate security controls and transparency about how information and AI are being used.
Skipping data governance is a false economy
As AI ambitions accelerate, there will be pressure to move quickly. Skipping data governance and validation to launch AI faster can look like the shorter route, but it is a false economy. It will cost more time and money to fix hallucinations and security breaches later than it would to govern the information in the first place. Not to mention the reputational damage your business will sustain.
The organisations that scale AI successfully will not be those with access to the most advanced models. They will be the ones that understand what information they hold, why they hold it, where it came from and whether it can be trusted. For leaders, trusted and governed information is the foundation that allows AI to scale with confidence. AI can only move as fast as your trusted data allows.
Dominic Del Giudice, is Managing Director ANZ, Iron Mountain