AI is paying off for Australian finance teams
New research shows that 97% of Australian finance teams using AI and automation are seeing measurable returns. This is no longer a theory-it's showing up in productivity, revenue, and the day-to-day experience of finance staff.
Source: Independent survey for Robert Half Australia.
Where teams are seeing ROI
- Improved employee experience: 44%
- Productivity gains: 43%
- Revenue growth: 43%
- Cost reductions: 39% (lower labour costs, fewer manual errors)
"AI in finance has moved from cutting costs to creating value by empowering people. It's building a more strategic, engaged work environment that pays dividends in performance, retention, and revenue," says Lauren Haxby, Practice Director at Robert Half.
The takeaway: AI is freeing teams from routine work and shifting effort to higher-value analysis and decision support.
Skills shift: hiring is lagging demand
- 98% of finance leaders now expect new hires to be proficient in AI and automation.
- 92% are struggling to find candidates with the right skills.
"Most teams want AI-proficient talent, but unclear requirements are blocking progress. Companies need to define the exact skills and outcomes they expect to engage the right candidates," Haxby adds.
Finance roles are blending traditional accounting and FP&A expertise with data analytics, tooling proficiency, and problem-solving.
Finance remit is expanding
With time freed up by automation, 98% of leaders say finance has taken on new responsibilities.
- Producing financial reports and decision dashboards (40%)
- Deeper financial analysis and strategic insights (34%)
- Optimising cash flow and working capital (34%)
- Business partnering and advising stakeholders (32%)
- Driving digital transformation within finance (30%)
- Fraud detection reviews (28%)
The role is moving away from data entry and toward insight, forecasting, and guidance.
What this means for CFOs, Controllers, and FP&A leaders
AI adoption is no longer just a cost play. It's a capability play-how fast your team can standardise processes, improve accuracy, and produce actionable insight on demand.
Practical next steps for the next 90 days
- Define outcomes: Pick 3-5 metrics (e.g., monthly close time, forecast accuracy, DSO, error rate, analyst hours saved) to track ROI.
- Start with repeatable use cases: AP/AR matching, expense classification, variance analysis, cash forecasting, management reporting.
- Map a skills matrix: Core accounting + SQL/Excel automation + BI dashboarding + prompt-writing for AI tools.
- Clarify hiring specs: List the exact systems and workflows a candidate must improve, and the KPIs they will own.
- Upskill your team: Build short sprints around dashboards, data quality, and model validation; pair analysts with data-savvy peers.
- Governance: Document controls for data access, audit trails, and error handling-especially for fraud detection use cases.
Tools and training
Explore vetted AI tools for finance workflows: AI tools for finance. For role-based upskilling, see courses by job.
Methodology
An online survey conducted in July 2025 included 500 hiring managers across finance and accounting, IT, and HR in small, medium, and large Australian organisations.
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