How CFOs Can Govern the 'Black Box' of AI in Finance

In this article:

• Balancing Governance and Innovation
• Keeping a Human-in-the-Loop
• Moving to Audit-First Design
• Laying the Groundwork for Unified Data
• Measuring the ROI of AI in Finance
• Empowering Your Finance Team
• Passing the Australian Boardroom Test

In 2026, the benchmark for successful Australian CFOs has shifted. It is no longer about the volume of AI deployed, but how clearly they can explain (and defend) the autonomous decisions resulting from that deployment to their Board.

Our new research, Realising ROI from AI Agents in Finance, highlights this tension: 70% of Australian CEOs rank AI as a top investment priority, yet 79% of organisations are still in the early stages of exploring agentic AI.

This caution is driven by governance risk, with 87% of CFOs insisting on a human-in-the-loop to ensure every autonomous action is traceable, defensible, and compliant with evolving Australian regulatory standards. And rightly so!

As one CFO interviewed for the report says, “If we let it loose now, we’d spend more time fixing problems than getting benefits.”

So, how can CFOs maintain financial governance and control without sacrificing ROI? The research is clear: success with AI in finance requires an enterprise platform built on clean data, transparent governance, and a commitment to keeping a human-in-the-loop.

Balancing Governance and Innovation

The big shift with AI in finance is autonomy. Unlike generative AI systems that make steps in response to human prompts, AI agents can determine their own steps to achieve a defined outcome.

So, when an AI agent adjusts a credit limit, reallocates working capital, or clears reconciliations within pre-set thresholds, the decision no longer passes directly through a human approver.

A finance agent might monitor accounts payable in real time and not just flag anomalies, but autonomously initiate corrective workflows without a human ever being involved.

Finance leaders in ANZ are used to being direct decision-makers – signing off on transactions, scrutinising reconciliations, approving capital allocations.

In an agentic environment, the CFO becomes something else: the architect of the framework that governs those decisions.

In effect, the CFO's role needs to transition from approving decisions, to designing the system that controls and explains them.

A system that ensures every agent can produce an auditable record of what it decided and why.

Defining the Boundaries of Human Oversight

While agentic AI will increasingly augment decision-making, our report found CFOs in ANZ are committed to financial system oversight from human checks and balances.

The question is where, and how deeply, should those human interventions touch the system?

In some workflows, AI agents will truly operate autonomously within predefined boundaries set by humans. In others, compliance requirements, risk appetite and sheer common sense make hands-on human judgement essential.

CFOs must set boundaries and articulate which approach is appropriate where. For example, automated anomaly detection in accounts payable could be safely autonomous.

But for material or complex issues like credit risk adjustments, revenue recognition interpretations, liquidity risk responses or material financial disclosures, human-in-the-loop validation will remain non-negotiable for the foreseeable future.

The CFO’s task now is to define those boundaries. Carefully calibrated oversight will include decision thresholds, exception triggers and confidence boundaries where agents can act independently within guardrails.

At first, nearly all financial workflows are likely to include some touchpoints where humans use their judgement, market understanding and professional insights to make business-critical and compliance decisions.

As a CFO, you and your teams are the pioneers developing your organisation’s first AI audit strategies. In the future, this will be a core finance capability.

Now is the time to explore thinking and agree on ground rules that your entire finance organisation understands.

Implementing 'Audit-First' Design for AI in Finance

Critical to this conversation is the belief that governance cannot be bolted on after autonomy. It must be embedded before it.

CFOs in Australia need to ensure auditability and explainability are embedded into every workflow. This starts with choosing technology partners who are transparent about their AI principles and committed to keeping a human-in-the-loop.

It also means designing processes with inbuilt traceability. Every autonomous action should generate a clear, reviewable record of what data was used, which rules were applied, what confidence thresholds were met and where human oversight intervened.

The agent must be able to reconstruct its logic in plain language for an auditor, regulator or Board director.

Audit-first design requires CFOs to work closely with CIOs, risk leaders and internal audit teams to define decision boundaries upfront.

Where can agents act independently? At what materiality thresholds must they escalate? How will overrides be handled and documented?

Laying the Groundwork for Unified, Governed Data

An audit-first AI strategy begins with unified, governed data. This is critical to autonomous agents operating safely at scale. Agents only work if they have access to clean data.

Yet, according to the Realising ROI from AI Agents in Finance report, 89% of CFOs say their data foundation is not well prepared.

Concerns around compliance, risk and control remain among the most significant barriers to scaling AI in finance. Data fragmentation and integration challenges compound the issue.

The data problem is usually caused by legacy ERPs that were not designed for an AI-first world. Their fragmented systems create data silos, allowing context to be lost between transactions and reporting layers.

CFOs whose organisations still rely on legacy systems need to act now to modernise foundational systems and prepare data architectures.

Prioritise robust data governance and security from the outset. Then start by automating workflows that are highly rules-based, produce auditable outputs and operate within well-understood boundaries.

Reconciliations as well as accounts payable and receivable are obvious low-hanging fruit.

Measuring the ROI of AI in Finance

More than 80% of CFOs in Australia and New Zealand report difficulty tracking ROI from technology investments. That challenge intensifies with AI.

Traditional metrics such as time saved or headcount reduction do not capture the full value – or risk – of agentic systems.

With an audit-first mindset, CFOs can reframe ROI metrics around defensibility and control.  That might mean tracking AI’s impact on forecast accuracy, cycle-time reductions or working capital improvement.

These insights will help to benchmark progress and provide ROI outcomes that can be defended in the boardroom.

Empowering Your Team with AI Skills

The transition to agentic AI requires a major cultural shift. For decades, finance has operated within well-understood processes.

Introducing autonomous AI agents requires reskilling, role clarity and a new type of confidence.

Our report notes that, while CFOs want humans to retain control, only 6% of organisations mandate AI awareness training. This is a governance risk in itself.

An audit-first strategy includes ensuring that finance teams understand not only how agents work, but how decisions are validated, where oversight sits and what accountability looks like.

Passing the Boardroom Test: Proving the ROI of Agentic AI

As agentic AI is embedded in finance systems, Australian Boards will want to know what data agents are using, which rules were applied and where human oversight sits.

• Can the CFO show that an exception was escalated according to predefined controls?
• Can they prove that compliance boundaries were enforced automatically, not manually patched after the fact?
• Are they confident the organisation could reconstruct the chain of logic if a regulator requested it two years from now?

Gartner predicts AI agents will become embedded in 40% of enterprise applications this year – up from 5% at the end of 2025. The pace will not slow.

So it's time to consider, as you move fast to deploy AI, whether you are also building frameworks to make that autonomy safe, traceable and defensible.

Discover how Australian CFOs are overcoming data and risk barriers to harness agentic AI. Read our practical guide to driving ROI through an Enterprise AI platform backed by robust governance.