AI is now a capital allocation decision and CFOs are setting the terms

Across Australia and New Zealand, the AI conversation has shifted noticeably over the past 12 months. For many organisations, the question is no longer what AI can do, but whether current investments are translating into measurable business value.

Despite the productivity and revenue upside AI is expected to deliver, many executive teams are still grappling with how much to invest and how deeply AI should be embedded across the business. At the same time, new research from Workday and ADAPT highlights a growing execution gap. While ambition is high, 77% of CFOs say they have yet to see meaningful return, and 89% report their data environments are not ready to support more advanced use cases.

This isn’t resistance from finance. It reflects a more disciplined phase of adoption. As AI moves from experimentation to enterprise deployment, it is increasingly being treated as a capital allocation decision, one that demands clearer accountability for outcomes, risk and operational readiness.

From experimentation to accountability

Over the past two years, most organisations have run pilots and tested tools to understand what AI might deliver. But scaling it across core operations forces a far more practical conversation: where will this actually lift productivity, how will the value be measured, what new risks does it create for controls and compliance, and what changes to processes and operating models are needed to make the benefits real?

As a result, CFOs are pushing organisations to move beyond isolated experiments and focus on AI embedded within core processes, particularly those that influence cost structures, risk exposure and operational efficiency.

The challenge many businesses now face is not underinvestment. It’s investing ahead of their operational readiness. In many cases, the technology is moving faster than the operating model.

The expanding role of the CFO

AI initiatives now cut across technology, risk, workforce and financial performance. This is reshaping the role of the CFO. Finance leaders are increasingly determining which initiatives move from pilot to scale, how value is defined and measured, and whether investment aligns to productivity targets and operating performance.

Innovation is no longer judged by capability alone, but by its contribution to sustainable financial outcomes, and in many organisations, that shift is already visible.

AI initiatives without a clear path to efficiency, risk reduction or revenue impact are struggling to secure funding.

Finance has always been the function that turns ambition into accountability. AI is no exception.

Data readiness is the real constraint

One of the clearest findings from the research is that the biggest constraint on AI value is not the technology itself, it’s the data.

Fragmented finance, HR and operational systems limit automation, reduce confidence in outputs, and create governance risk. Without consistent, trusted data, organisations risk layering AI on top of inefficient processes rather than eliminating them.

For CFOs, the priority is less about advanced algorithms and more about visibility, control and reliability. That is why many finance leaders are focusing first on strengthening data foundations and modernising core systems before expanding AI investment.

Where finance is seeing value

The strongest early use cases share a common characteristic: they improve control while reducing manual effort.

Finance teams are seeing impact in areas such as continuous monitoring of controls, automation of reconciliation and close activities, real-time scenario modelling and forecasting, and improved visibility into workforce and operating costs.

These changes shorten cycle times, improve accuracy and allow finance teams to focus on analysis and decision support rather than transaction processing. This is where AI moves from experimentation to operational performance.

Closing the readiness gap

The next phase of enterprise AI won’t be defined by how many tools organisations deploy. It will be defined by how effectively they convert investment into measurable outcomes.

The organisations seeing the strongest results are taking a disciplined approach: prioritising high-volume, high-impact processes, embedding AI into existing workflows, establishing clear governance and accountability, and measuring success through productivity, accuracy, reduced cycle time and risk reduction.

Across ANZ, a gap is emerging between AI ambition and operational reality. Those investing now in data quality, integration and governance will be best positioned to turn AI into measurable performance over the next few years. Without those foundations, AI often adds cost and complexity before it improves performance. Most organisations no longer have an AI strategy problem. They have a value realisation problem. In this cycle, competitive advantage will come from organisations that modernise core systems, strengthen data foundations and embed governance early. AI will drive performance, but only in businesses that are structurally ready. *Matt Lovell is a B2B SaaS executive and technology advisor with more than 20 years’ experience in consulting, implementation and sales leadership in the software industry.

Top image credit: iStock.com/hirun