AI adoption speed won’t decide the winners, argues Daniel Hein of Informatica. Trusted, governed data will, and most businesses aren’t there yet.
The next phase of business transformation will not be defined by who adopts AI fastest. It will be defined by who can give AI the trusted, governed, and contextual data it needs to act responsibly.
For Australian companies, that challenge is becoming more urgent as regulatory pressure rises, from mandatory climate reporting to reforms under the Privacy Act, while cybersecurity risks and expectations from customers, investors, and the broader community continue to intensify. Against this backdrop, data has become far more than a back-office asset. It is now a strategic lever for decision making, compliance, and competitiveness.
Mastering data across its entire lifecycle is essential to understanding where it comes from, how it evolves, and how it is used. At the same time, the rise of agentic AI is opening up new opportunities for efficiency, and realising them fully depends on building the right foundations for autonomy, transparency, and compliance.
Trusted context: the foundation of data security and governance
Establishing a “trusted context” is therefore becoming a prerequisite. More than a governance framework, it is the data intelligence layer that sits between raw enterprise data and the systems, people, and AI agents that depend on it. It brings together data quality, lineage, metadata, access controls, and policy enforcement so organisations can understand what data is being used, by whom, for what purpose, and under which compliance requirements. For any organisation, that intelligence strengthens confidence in data and supports more reliable decision making. It also makes it possible to harness advanced technologies without losing control. This is what keeps innovation an asset, rather than allowing it to become another source of risk.
Every AI agent needs somewhere to land — trusted data, governance, and the business logic to act safely. Trusted context is what provides that landing place, which is why it becomes more valuable in the agentic era, not less.
Put simply, the challenge is no longer collecting data, but mastering it. Data now flows continuously across applications, cloud environments, business units, and external partners. In such a distributed landscape, maintaining a clear, consistent view of data quickly becomes complex. In practice, this requires aligning several dimensions that are too often managed separately.
Data definitions must be standardised to eliminate ambiguity.
Relationships between datasets must be clearly mapped to understand dependencies and downstream impacts. Quality rules must be enforced to prevent errors from propagating. Data governance policies must be applied consistently across systems and teams.
Finally, traceability mechanisms must allow organisations to track data from its origin to its point of use, essential in cases of suspected unauthorised access or misuse. When these elements come together, organisations gain a level of visibility that is critical in today’s risk landscape, in which businesses are expected to demonstrate that they manage their stakeholders’ data responsibly.
Trusted context is emerging as a major strategic lever because it turns fragmented data into usable data intelligence. It enables faster anomaly detection, stronger controls, clear evidence of compliance, and more proactive risk management. Above all, it forms the essential foundation for innovation. Advanced use cases, from analytics and automation to artificial intelligence, depend on reliable and trustworthy data. Without trust, their value is limited. With it, they become powerful, sustainable engines of performance and value creation.
Agentic AI: a catalyst for value, conditional on trust
Agentic AI marks a turning point in how organisations put data to work, which is why it’s no surprise the topic is dominating boardroom conversations worldwide. Unlike traditional systems, agents operate with far greater autonomy: interpreting complex situations, making decisions, and triggering actions in real time. This capability opens up significant opportunities for efficiency, from detecting and correcting operational anomalies, to personalising customer relationships at scale, to helping decision makers make sense of vast volumes of information.
But that same autonomy fundamentally reshapes the risk profile. If an organisation relies on data that is inaccurate, poorly governed, or lacking transparency, the likely result is flawed decisions being executed automatically, with potentially wide reaching impacts and serious compliance and accountability challenges. Data that meets the standard of a trusted context becomes essential here: by guaranteeing quality, traceability, and governance, it enables agentic AI to be used safely and with full auditability.
Adopting these technologies, then, is not simply a technical evolution, it’s a wholesale transformation of how organisations approach data, governance, and accountability, one that needs to be built in from the start if AI is to become a genuine driver of value.
Financial crime and fraud prevention: a defining use case for trusted data
Few challenges depend on trusted data as directly as the fight against financial crime. Australia’s AML/CTF regime is undergoing its biggest overhaul in a generation, with new AUSTRAC obligations phasing in through 2026 and a much wider range of businesses coming into scope. For institutions already running transaction monitoring and due diligence programmes, the discipline is nothing new, but what must now be captured, verified and reported has expanded considerably.
The complexity stems from customer and transaction data that is scattered across systems and rarely reconciled into a single view. Without structure, these gaps slow investigations, generate false positives and weaken the evidence trail regulators expect. Establishing a trusted context, built on standardisation, quality and traceability, and underpinned by strong master data management, makes it possible to produce data that is reliable, auditable, and defensible in the eyes of regulators, auditors, and customers.
Access to trusted, well-governed data changes what’s possible in the fight against fraud. Combined with artificial intelligence, it helps detect anomalies faster, reduces the false positives that burden investigation teams, and strengthens confidence across the value chain, turning a growing regulatory burden into a driver of resilience and trust.
Making trust the engine of transformation
As transformation accelerates with agentic AI, data is shifting from a supporting role to a core pillar of strategy. The challenge is no longer just having access to data, but being able to trust it and build on it. Establishing a climate of trust is therefore essential, to manage data securely, ensure compliance, and fully harness the potential of emerging technologies like agentic AI.
Organisations that succeed will be the ones that recognise trust, governance, and intelligence not as isolated priorities, but as tightly interconnected dimensions of the same transformation. By bringing them together, they can turn data into a genuine strategic lever, one that drives resilience, innovation, and lasting competitive advantage. In the AI era, trusted data is not just the foundation for transformation. It is the infrastructure that makes transformation possible.
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