How AI is re-shaping financial reporting

CFOs aren't adopting AI because it's fashionable. They're adopting it because modern finance teams are expected to deliver insight, assurance, and direction at a speed traditional reporting models were never built to handle.

These tools connect data, risk, and strategy in a way we've never seen before. 

As a result, AI is fundamentally shifting the CFO's role from an interpreter of past performance to an orchestrator of real-time and informed decision-making.

Instead of spending cycles producing reports, teams can now interrogate live data and support executives with forward-looking insights. 

As CFOs increasingly direct their companies into the future, AI is going to be an invaluable –and essential – force multiplier. 

Getting the ball rolling

This shift from reporting to real-time steering is profound and there is still much to be done, but that hasn't stopped many finance teams from taking their first steps.

They are using AI and consolidated data to generate more structured financial narratives, frequently review budgets, improve document preparation, or assemble regulatory disclosures.

These changes stem from the need to be faster and more consistent. Yet adoption remains uneven. 

A recent survey we undertook with global executives revealed 94 per cent of Australian executives agree that their current approach to the adoption of generative AI tools could introduce risk. 

A separate survey we completed with reporting practitioners found 52 per cent of Australian respondents believed reliability of outputs prevented them from using AI in more of their daily work, while 48 per cent said security or legal concerns and 47 per cent said readiness of their organisation and top leaders were factors for them.

The barriers are familiar but persistent. As AI use accelerates and market conditions shift at a moment's notice, the need for data integrity will only increase in the New Year. But because of team silos, manual and disconnected data systems, or competing priorities, many companies lack the right infrastructure to get there.

Without a strong data foundation, even the smartest algorithms can only go so far. What's important heading into 2026 is implementing emerging technology and tools with rigor and real-world practicality, strengthening operations and confidence.

Making the arduous simple

Meanwhile, internal audit is undergoing its own transformation, with a growing emphasis on real-time alerting. 

As AI is enabling the continuous monitoring of large datasets rather than periodic sampling, auditors can redirect focus onto areas where human analysis truly adds value.

Automation is allowing for better detection of signals in unstructured data, adapting framework controls – such as those relating to Scope 1, 2, and 3 emissions, and updating risk maps with new information.

This approach doesn't just improve reliability, it builds resilience – a much-needed function going forward.

The same transformation is unfolding within risk management. AI gives organisations a far more structured and transparent view of how risks are mitigated, how individual controls connect, and where gaps or overlaps may exist.

That level of clarity delivered by GenAI turns risk management from a fragmented exercise into a coordinated, enterprise-wide discipline, alongside finance, audit, and compliance teams.

And that shared understanding strengthens alignment for CFOs, enabling faster, more confident decision-making.

A necessary convergence

The rise of sustainability reporting further reinforces the need for AI adoption by finance leaders. 

With sustainability and financial reporting rapidly converging, generative AI is already smoothing the seams between them, enabling these teams to work from a shared, AI-enhanced single source of truth.

For financial reporting, tasks like variance analysis, data comparison, peer benchmarking, and narrative construction – once manual and contentious – can now be performed with greater speed and consistency. 

The impact is even more pronounced in sustainability functions. Frameworks like the International Sustainability Standards Board (ISSB), and Australian Sustainability Reporting Standards (ASRS), continue to shape how non-financial performance is understood and presented.

Our survey revealed that in the coming two years, the AI-driven innovations that practitioners believed would improve their organisation's ability to integrate sustainability metrics into financial reporting and risk management are: AI-powered sustainability data aggregation – automating collection and analysis of sustainability data (57 per cent); automated sustainability reporting – drafting disclosures and monitoring regulatory changes (49 per cent); and sustainable supply chain optimisation – identifying risks and improving sourcing decisions (42 per cent).

As adoption grows across both domains, AI is becoming central to how organisations monitor sustainability commitments and risks. The aim is to create a single, integrated narrative grounded in rigor, shared data, and operational reality.

Still a long way to go 

AI will not reinvent the mission of the CFO. The mandate remains the same, yet reporting is no longer solely historical, forecasting is no longer a chore, and risk management moves from reactive to predictive.  

But none of this happens without deliberate action. Governance, traceability, and human oversight remain non-negotiable. 

Without guardrails, automation becomes just another source of risk. AI can elevate the office of the CFO, but only if its outputs are trusted and its application is transparent.