The CFO’s Guide to Mastering AI in Finance

Finance in the Age of AI: How Automation, Data, and Curiosity Are Redefining the CFO Role

In the fifth and final episode of Finance Focus, First Focus CEO Ross Sardi and Jarrod Morris, CEO and founder of Bolden, explore how artificial intelligence, automation, and data analytics are transforming finance leadership. They discuss how AI is reshaping the finance function from manual processing to intelligent insight, why data quality and context matter more than ever, and what skills will define the next generation of finance leaders in Australia.

Key takeaways

• AI and RPA are removing manual finance work, freeing teams for strategic analysis and decision support.
• AI-native SaaS tools are outperforming legacy platforms that try to retrofit AI features.
• Data quality and context determine the reliability of AI-driven insights in finance.
• Finance leaders must shift from gatekeepers to enablers, empowering teams with data and tools.
• Curiosity, communication, and problem-solving now matter more than technical credentials.
• Fractional finance and shared service models are rising as businesses seek specialist expertise.
• APIs and integrations underpin finance innovation, while security and governance remain critical.
• Custom “vibe coding” and AI-driven app creation are now accessible to non-technical leaders.
• Future success metrics for CFOs will focus on plan execution, efficiency gains, and innovation impact.
• Finance is evolving into the central nervous system of business, driving both governance and growth.

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AI and RPA: Automating the manual finance workload

AI and robotic process automation (RPA) are streamlining finance operations across Australian businesses. From invoice processing to reconciliations, automation is taking over repetitive, rules-based work. Jarrod explains that this shift is not just about efficiency, but about freeing finance teams to become embedded business partners focused on decision support and analysis.

For example, AI now triages and processes tickets in IT support functions. The same principles apply in finance, where automation handles data entry, approvals, and reconciliations with speed and accuracy. As Ross notes, AI’s natural language capabilities allow RPA to evolve from strict “if-this-then-that” rules to flexible systems that can infer intent and context.

From automation to intelligence: Finance analytics powered by AI

The next frontier is analytics. AI enables teams to draw insights from vast amounts of financial and operational data. However, both Ross and Jarrod stress that success depends on data integrity. AI models can process data rapidly, but without accuracy and structure, businesses risk making poor decisions based on flawed assumptions.

Finance leaders must ensure data governance and validation before layering AI tools over their systems. A finance professional with domain expertise remains essential to interpret results, test outputs, and ensure insights make business sense. AI can answer questions, but human judgment gives those answers meaning.

Garbage in, garbage out: The importance of clean, contextual data

Finance teams have long known the dangers of poor data quality. As Jarrod highlights, the balance sheet itself captures data errors through reconciliations. With AI, the ability to detect, classify, and surface discrepancies has improved dramatically. AI-driven reconciliation tools can now review transactions against accounting standards, flag anomalies, and provide clear exception reports in seconds.

Still, as Ross cautions, data must be contextual. AI can total figures but cannot understand business nuance unless trained. Finance leaders need to ensure their systems distinguish between contract types, client segments, and revenue categories so that insights reflect operational reality. Without context, automation produces numbers without narrative.

Does the data make sense? Finance as the strategic filter

The question “does this make sense?” defines the modern finance function. AI can summarise performance, but finance leaders must test whether outputs align with strategy. For example, a dashboard may show revenue growth, but only by separating performance by sector or customer type can finance reveal where true profitability lies.

Modern tools like Power BI remain powerful for visualising structured data, yet their scope is often retrospective. Strategic finance platforms, by contrast, integrate directly with HR, CRM, and ERP systems to deliver live forecasting and scenario planning. These integrations allow finance teams to move from static reporting to real-time decision support.

The evolving finance leader: From controller to enabler

AI is shifting the finance leader’s focus from compliance to enablement. Routine accounting, payroll, and reporting are increasingly automated, while finance professionals move into oversight and strategic partnership. Ross explains that finance leadership should no longer sit with one person but be distributed across the executive team, empowering every leader to think financially and act on data.

Jarrod agrees, adding that the finance leader’s primary job is to resource the execution of the plan. This means connecting people, data, and technology to enable business units to deliver outcomes. Finance leadership has become less about owning numbers and more about equipping teams to act on them.

Hiring in the age of AI: Curiosity over credentials

The modern finance professional looks very different from a decade ago. In an AI-enabled environment, curiosity is as valuable as experience. Ross describes curiosity as the ability to ask smart questions, spot patterns, and challenge data that does not make sense. The best candidates combine analytical precision with an appetite for experimentation and learning.

Jarrod adds that future finance hires must be enthusiastic about technology, collaborative across departments, and comfortable solving problems in creative ways. They do not need all the answers; they need the curiosity to find them. For many Australian organisations, this shift may also accelerate adoption of fractional finance models, where businesses access a blend of expertise through shared service providers rather than hiring full-time CFOs.

The rise of the fractional model

Finding a “unicorn” finance leader who is analytical, strategic, creative, and tech-savvy is challenging. Smaller businesses may not have the budget to employ such talent full time. The solution is fractional finance – partnering with providers like Bolden to access CFO-level skills as a service. This model brings advanced financial, technological, and strategic expertise under a single invoice, making it viable for growing companies.

Jarrod notes that as businesses scale past roughly $50–100 million in revenue, it becomes viable to bring this expertise in-house. Until then, fractional models provide flexible access to senior finance talent and allow mid-sized firms to compete with enterprise-level capability.

Redefining success: New metrics for modern CFOs

In the age of AI, CFO performance can no longer be measured purely by compliance and cost control. Success now hinges on how effectively finance enables execution of the broader business plan. Metrics should reflect outcomes such as:

• Uptake and utilisation of new technologies across business units.
• Reduction in manual processes and cycle times.
• Contribution to revenue growth through strategic insights.
• Improvements in forecast accuracy and planning agility.
• Adoption of performance-linked incentive programs such as ESOPs or profit share models.

Finance leaders who align their success metrics with organisational performance will naturally drive innovation, accountability, and growth.

Building bespoke finance tools: The power of “vibe coding”

AI is now enabling non-technical finance leaders to build their own tools. Ross shares his experience of developing an application over five days using natural language coding prompts. Known as “vibe coding,” this approach allows users to describe what they want and let AI generate most of the underlying code. The result: custom finance tools tailored to unique business needs.

While this capability is exciting, both Ross and Jarrod emphasise governance. Any solution that handles sensitive financial data must meet enterprise standards for security, sovereignty, and compliance. Businesses must also manage “shadow IT” risks by ensuring these tools are built and integrated under proper oversight.

At its best, bespoke AI-driven development can bridge systems, integrate APIs, and visualise data across departments. At its worst, it can create compliance gaps. The key is to combine innovation with structure, encouraging experimentation while enforcing responsible use.

APIs, integration, and data governance

Every successful finance innovation relies on connectivity. APIs allow finance systems to share data between accounting, CRM, HR, and operational platforms. Finance leaders should ensure every system in their stack provides robust, well-documented APIs with appropriate access controls. Jarrod warns that while AI and integration open new efficiencies, they also introduce governance challenges. Sensitive data must remain protected under Australian privacy laws and industry standards.

By centralising data, applying security best practices, and using AI to interpret unstructured inputs, finance teams can unlock a single, reliable source of truth. This forms the foundation for automation, forecasting, and analytics that genuinely support decision-making.

Finance as the central nervous system of business

Jarrod describes the general ledger as the “central nervous system” of an organisation. Almost every business process touches the chart of accounts in some way, from payroll to procurement to project management. Finance therefore sits at the intersection of data and action. By modernising this core, businesses improve not only reporting accuracy but also operational efficiency across the board.

Ross expands on this idea, noting that finance and technology now share responsibility for driving transformation. Finance must ask, “How can we automate this process?” and “Is technology delivering the ROI we forecast?” Meanwhile, technology teams must understand how financial outcomes measure success. Together, these disciplines create a closed loop between planning, execution, and performance.

Continuous learning: Staying one step ahead of AI

Both leaders stress that finance professionals need to invest in their own AI literacy. Just as people once pursued MBAs after hours, today’s finance teams should dedicate time to exploring AI tools, prompt engineering, and data analytics. The learning curve is shorter than many expect, and staying even one step ahead of industry peers can create a lasting competitive edge.

Ross compares it to learning an instrument: if you stop practising, AI will quickly outpace you. Immersion and curiosity are now essential professional skills. Finance leaders who experiment with new platforms, test automations, and understand emerging standards such as the Model Context Protocol (MCP) will be best positioned to guide their organisations through the next wave of disruption.

Final reflections

AI and automation are not replacing finance; they are redefining it. The finance function is becoming both more technical and more human, grounded in governance yet driven by curiosity. By combining accurate data, smart tools, and continuous learning, finance leaders can move from reactive number crunching to proactive business partnership.

The Finance Focus series closes with a simple truth: technology’s role in finance is to make better decisions possible, and finance’s role in technology is to ensure those decisions make sense. For Australian businesses, that partnership is the key to sustainable growth in the AI era.

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