Audit’s reckoning is here. Is your data ready?
By Kristen Pimpini (pictured), VP and General Manager APJ, Workiva
Internal audit has a data problem, and the widespread adoption of Artificial Intelligence (AI) is making it impossible to ignore.
Across every industry, organisations are deploying AI tools at pace, only to discover a fundamental constraint: the quality of outputs is entirely determined by the quality of inputs.
“Garbage in, garbage out” is not a new principle, but at the speed and scale AI operates, the consequences of poor data are amplified dramatically.
The real competitive edge was never going to come simply from deploying AI. It comes from having AI built on robust data foundations that deliver measurable impact and trustworthy outputs.
Internal audit is a strategic advisor. One who understands getting the data foundations right is no longer a bonus or luxury, but a necessity – separating enterprises that guess from enterprises that act with confidence.
Flying blind in a polycrisis
As business environments grow more complex, the audit function has become central to how organisations understand and respond to change.
Yet many audit teams are still operating with tools that belong to a different era. Disconnected spreadsheets, manual processes, data siloed across departments with no clear line of sight between finance, risk, and compliance. These teams are producing outputs from incomplete inputs.
They’re working hard but unable to produce the coordinated, forward-looking insights modern governance demands. The cost isn’t just inefficiency; it’s also delayed identification of emerging risks and missed opportunities to realise the full competitive potential of AI.
Meanwhile, organisations are navigating a polycrisis – a simultaneous convergence of cybersecurity threats, geopolitical instability, sweeping regulatory change, and the rapid integration of generative AI into core business processes.
This reality is driving a significant shift toward unified, AI-powered platforms that connect finance, audit, and risk in a single, coherent view.
The goal is to turn fragmented data into coordinated, trusted insight and action. Delivering integrated, data-driven governance, risk, and compliance (GRC) that keeps pace with the speed of business.
GRC teams don’t need convincing that AI-powered platforms are valuable, they need platforms capable of delivering on that promise. Sitting at the intersection of finance, risk, and compliance, internal audit is uniquely positioned to connect the dots and drive smarter decisions at a scale no manual process can match.
When compliance tasks are automated and trusted data is connected, GRC teams stop chasing risk and start shaping how it’s managed, elevating their advisory ability further.
The AI fluent advisor
A survey we recently undertook with global executives found 91 per cent of executives say AI is already accelerating and sharpening financial decision-making. For internal audit, this raises the bar considerably.
The “compliance cop” is being replaced by a strategic advisor who is value-oriented, forward-looking, holistically focussed, and adaptable. A modern auditor measured by their contribution to business goals, not the volume of tests performed or checklists completed.
This requires a fundamentally different toolkit combining critical thinking with technological proficiency and a unified governance strategy.
Auditors who can interpret AI-generated insights, contextualise risk across business functions, and communicate findings at board level are becoming invaluable. The technology amplifies their capability, but only when it has reliable, well-structured data to work with.
The hidden flaw
Too many organisations investing in AI are learning the hard way that the quality of their outputs are entirely dependent on the quality of their inputs.
For audit functions, data governance is the foundation on which everything else is built. Robust governance ensures that AI tools have reliable, consistent, and well-structured information to analyse. Without it, even the most sophisticated platform will surface incomplete conclusions and unreliable signals.
It is vital executives and boards must change how they support the audit function to ensure they thrive in a modern landscape.
Automation plays a complementary role freeing up auditors from time-consuming manual tasks so they can focus on higher-value, specialised work that requires human judgment.
Our recent survey shows clear priorities in Australia this year, with 78 per cent of organisations now allocating dedicated budgets for transformation projects, and 83 per cent of local executives prioritising stronger data governance and automated data collection.
Organisations investing in both purpose-built audit platforms and the talent to use them effectively will be best positioned to maintain control and competitive advantage as the landscape continues to shift.
Those who don’t risk falling further behind. Not because their AI isn’t capable or the team’s don’t know quite how to use the technology, but because they can’t trust the data foundations it is built upon.
Confidence worth having
AI systems won’t simply fail when given poor data. They generate outputs with misplaced certainty, producing risk assessments, anomaly flags, and strategic recommendations that appear authoritative but rest on shaky foundations.
In audit, where decisions carry regulatory, financial, and reputational weight, that kind of misplaced confidence is more dangerous than no answer at all.
Strong data governance addresses this at the source. It doesn’t replace human expertise or strategic judgment; rather, it creates the conditions for both to thrive.
Clean, connected, trustworthy data means AI systems operate within appropriate confidence intervals, surface genuinely meaningful signals, and support auditors in making decisions that hold up to scrutiny.
For organisations serious about turning AI’s potential into measurable business performance, the lesson is clear. Fix the foundations, the rest will follow.
