AI’s impact on finance is undeniable, particularly with the recent surge in generative AI (GenAI) capabilities. In fact, 70% of CFOs expect modest productivity gains of 1% to 10% through the adoption of AI, while 13% expect even larger strides of over 10% by 2024, according to Deloitte’s CFO Signals survey.
As the hype fades and the reality of this technology takes hold, AI is steadily proving its practical value. For example, areas like financial forecasting, cash flow optimisation, and regulatory compliance are already seeing huge advancements thanks to AI, particularly through predictive analytics and natural language processing (NLP). Even routine tasks like invoice processing, three-way matching in accounts payable, and intercompany eliminations are becoming more automated, unlocking efficiency and accuracy.
However, AI isn’t just about performing routine tasks faster. It’s fundamentally shifting the role of finance departments by supporting them to focus on more strategic, analysis-driven work. As more companies adopt AI, it becomes a way for finance teams to move from reactive reporting to proactive insights – aligning themselves more closely with organisational goals. But it’s not without its challenges. 65% of CFOs cite technical skills as a barrier, and 53% point to fluency as another roadblock.
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Now, let’s explore the fast changing world of AI in finance!
What is AI in finance?
AI in finance refers to the use of advanced algorithms and machine learning (ML) to automate processes, improve decision-making, and bolster customer engagement. This intelligence-led technology enables finance teams to do everything from analyse data at scale and predict market movements to personalise customer experiences – all while fuelling the transformation of the financial function.
AI use cases in finance
Applications of AI in finance span various critical areas. Here are some examples of AI in finance.
Financial forecasting and planning
Predictive analytics powered by AI is changing the way finance professionals forecast. Unlike traditional methods, AI recalibrates continuously, keeping forecasts relevant and accurate. Coupled with GenAI, which automatically generates contextual commentary to explain trends and forecasts, finance teams gain deeper insights into potential future scenarios, enabling more strategic decision-making with up-to-date, actionable information.
Regulatory compliance
AI, especially NLP, can significantly reduce the burden of navigating complex and often-changing global regulations. It automates the review of documents, highlighting key risks or changes in compliance status, allowing teams to focus on the bigger picture while AI handles routine regulatory checklists.
Cash flow optimisation
AI-driven predictive models help forecast cash flows more accurately by analysing historical and real-time data. This alerts finance teams to potential cash shortfalls or surpluses before they become a problem, enabling proactive decision-making.
Expense management
Tools like optical character recognition (OCR) scan receipts and invoices to populate fields automatically, reducing human error and the need for manual entry. AI also supports “manage-by-exception” workflows, allowing finance teams to focus only on anomalies rather than every transaction.
Task automation
AI automates time-consuming administrative duties, such as invoice capture, payment execution, and data entry, creating space for finance teams to focus on high-value projects.
Fraud detection
AI-driven fraud detection systems continuously monitor transaction data to identify suspicious activities. By recognising patterns that deviate from normal behaviour, AI flags anomalies in real time, allowing finance teams to intervene quickly. Over time, these systems improve their accuracy by adapting to new fraud tactics.
Read the infographic: What AI really means for the finance function in Australia >
Key benefits of AI in finance
Clearly, AI offers a range of capabilities that align perfectly with the needs of the finance function. But how does this translate into real-world benefits for the finance function and the broader business?
Efficiency and productivity
AI’s ability to automate repetitive tasks is driving massive efficiency gains. With AI handling the heavy lifting, finance teams can pivot from merely reporting historical data to analysing and forecasting future performance.
Cost reduction
AI-driven process improvements and predictive insights also lead to cost reductions. Automating administrative tasks reduces the need for human intervention, and AI-driven insights help identify cost-saving opportunities, such as optimising cash management or improving financial forecasting.
Improved decision making
AI is great at supporting decision-making by unlocking a deeper, more nuanced view of financial data. With AI algorithms working behind the scenes, finance teams are better positioned to assess risks with precision, anticipate emerging trends, and make quick, data-driven choices. This sharpens day-to-day operations and enhances both immediate and long-term strategic planning, turning complex data into clear, actionable insights.
Risk management
AI is the ideal for risk management, particularly when it comes to fraud detection. By analysing patterns in transaction data, machine learning models can spot potential fraud or money laundering activities, helping businesses act quickly to mitigate risks.
Scalability
Thanks to cloud-based AI solutions, companies now have scalable access to AI technologies without needing the computing infrastructure to run them in-house. This is particularly important for global organisations, where AI can easily integrate into existing financial systems, regardless of geographic location.
AI tools and platforms for finance
When it comes to AI implementation in finance you need to carefully weigh up your options. Selecting the right AI solution for your finance team should revolve around finding a platform that integrates seamlessly into your existing operations, amplifies your team’s strategic capabilities, and accelerates the move toward more intelligent, data-driven decision-making.
Custom AI vs off-the-shelf business solutions
Choosing between custom-built AI solutions and off-the-shelf cloud ERP systems is a critical decision that can influence your finance team’s success with AI. While both options come with distinct advantages, the rise of cloud ERP systems integrated with AI is proving to be a strong contender, offering a solid balance between flexibility, cost, and rapid deployment.
- Custom AI solutions | Building a custom AI system allows finance teams to tailor the technology to their specific needs. This might be the best choice for larger organisations with highly complex or unique requirements. However, custom solutions require significant time, resources, and expertise to develop, deploy, maintain, and continuously refine to keep pace with new advancements.
- Off-the-shelf or custom cloud ERP systems | For most finance teams, cloud ERP systems with integrated AI offer the best of both worlds. These platforms come pre-built with powerful AI capabilities, designed to be easily adopted and scaled. Platforms like NetSuite, Microsoft Dynamics 365, and SAP S/4HANA already feature sophisticated AI-driven tools for forecasting, compliance, fraud detection, and cash flow management. These solutions are cost-effective, highly scalable, and backed by ongoing updates that ensure teams have access to the latest AI advancements without the burden of developing bespoke solutions.
Naturally, choosing the right option comes down to a few different factors:
- Size of the organisation | Larger companies with more complex needs may require a custom-built solution, while smaller to mid-sized organisations may benefit more from ready built solutions that can be customised around existing processes.
- Budget | Custom solutions can be pricey, both in terms of development and ongoing maintenance. Off-the-shelf tools, on the other hand, come with a predictable cost structure and can be a more economical choice.
- AI maturity | If your team is just beginning to explore AI, an off-the-shelf solution is an excellent way to dip your toes in. It’s a lower-risk way to start automating processes and gaining insights. As your AI maturity grows, you may find that a custom solution, or even a hybrid approach, could better meet your evolving needs.
Ultimately, the best choice depends on where your finance team is on its AI journey, and what your long-term goals are.
The role of AI stakeholders in finance
AI in finance should bring together key players form across the business, to ensure that the adopted technology delivers value and runs smoothly. From tech experts building the systems to those ensuring fairness, compliance, and risk management, everyone has a part to play in making AI work for the business.
- CIOs and CTOs | Oversee AI implementation, ensuring alignment with business goals.
- Ethics and diversity officers | Ensure AI solutions are fair, transparent, and unbiased.
- Developers | Design and maintain AI algorithms to ensure they are effective and accurate.
- Legal and risk teams | Ensure AI applications comply with laws, regulations, and best practices.
- Auditors and internal controls | Monitor AI systems for effectiveness and potential risks.
Governance of AI in finance to ensure responsible use
Clear oversight helps mitigate risks, such as bias in AI models, and promotes transparency. This is why effective governance is so critical for AI technologies – it ensures that AI is used ethically and complies with regulations.
Here are some key considerations for governance of AI in finance to ensure responsible use:
- Transparency | Ensure AI models and their decision-making processes are understandable and accessible to all stakeholders.
- Bias mitigation | Regularly audit AI systems to identify and eliminate potential biases, ensuring fairness and equal treatment across all users.
- Accountability | Define clear roles and responsibilities for AI oversight, ensuring that there is accountability for AI-driven decisions.
- Regulatory compliance | Stay up to date with local and global regulations to ensure AI applications adhere to legal standards and industry best practices.
- Ethical standards | Establish ethical guidelines to steer AI development, focusing on privacy, security, and human-centric decision-making.
- Data privacy and security | Implement robust measures to safeguard sensitive financial data, ensuring AI systems comply with privacy laws such as GDPR.
- Continuous monitoring | Set up ongoing performance monitoring to assess the effectiveness and potential risks of AI models, allowing for timely adjustments.
- Stakeholder involvement | Engage diverse stakeholders, including ethicists and legal experts, in the governance process to ensure balanced perspectives on AI use.
The five stages of AI maturity in finance
Some finance teams may still view AI as a futuristic concept, but the reality is, early adopters are already reaping the rewards with simple, cost-effective AI solutions that pose little risk. The key to success? A clear framework that helps businesses move from cautious experimentation to full-scale transformation. By understanding and following these stages, finance functions can not only catch up but leap ahead of competitors who are still playing catch-up. Here’s a breakdown of how finance teams can navigate their AI journey:
- Awareness and exploration | At this stage, finance teams are just beginning to dip their toes into the AI pool. There’s recognition of AI’s potential, but the focus is primarily on understanding how it can fit into existing workflows. It’s about small wins – early pilots that can demonstrate AI’s value without diving too deep.
- Experimentation and proof of concept | Here, finance teams start to experiment with AI in specific, lower-risk areas. These are often simple tasks, such as automating data entry or testing predictive analytics in forecasting. The goal is to prove that AI can deliver measurable value before scaling it up.
- Adoption and integration | By now, AI solutions are beginning to integrate into daily operations. Finance teams move beyond pilot projects and start embedding AI into core functions like risk assessment, fraud detection, and budgeting. This stage is all about streamlining processes and enhancing efficiency, all while managing change carefully across the organisation.
- Optimisation and scale | At this stage, AI has started to become a strategic asset. Finance teams are using AI to refine decision-making, uncover deep insights from data, and automate more complex processes. The focus now is on scaling AI applications across departments, fine-tuning algorithms, and ensuring the tech is continually delivering at peak performance.
- Innovation and transformation | The final stage is where AI becomes a catalyst for business innovation. Finance teams have fully embraced AI, with systems autonomously making high-level decisions, offering predictive insights, and shaping strategic directions. This is where AI is driving new business models, unlocking entirely new opportunities, and pushing the envelope on what’s possible in finance.
While many finance teams are still catching up to AI’s potential, the leaders in the field are already capturing the benefits through low-risk, affordable applications. By adopting a clear, structured framework, finance teams can integrate AI smoothly, allowing them to move up the maturity ladder with confidence.
Learn more: The practical guide to AI-driven finance strategies >
Overcoming AI implementation challenges
The importance of quality data
For AI to be effective, it requires high-quality, accurate, and proprietary data. AI solutions are only as good as the data they are trained on. AI-driven platforms that use secure and correct data, especially large-scale, proprietary datasets, are more likely to offer accurate and actionable insights.
Internal resistance
Internal resistance is one of the sneakiest roadblocks finance teams face when trying to embrace AI. If this is case, it’s more often less about the technology and more about mindset. To break through, finance teams need to flip the script and cultivate a culture where AI isn’t feared or seen as a job thief, but rather as a co-pilot that works hand in hand with human expertise. By encouraging collaboration and curiosity, teams can harness AI’s power to amplify decision-making, without losing the unique value that human insight brings.
Collaboration and human oversight
Similarly, it’s worth repeating that the human touch remains indispensable. CFOs need to craft strategies where AI doesn’t just operate in isolation but works in harmony with human expertise. It’s about striking the right balance. This means letting AI power through routine tasks, while leaving the complex, high-stakes decisions to the experts who can guide it.
Talent shortages
With AI set to become the heartbeat of finance, CFOs are about to enter a high-stakes talent war. Skilled data scientists are already in short supply, and as AI’s role expands, the competition for these experts will only intensify. Without the right talent to build and maintain these systems, even the most cutting-edge AI initiatives risk stalling.
Explore further: Why AI skills will be essential for your finance team in 2025 >
Here’s a few strategies for acquiringand nurturing the talent that can turn AI visions into reality.
- Build | Rather than looking outward from the start, organisations should first focus on building talent from within. Developing data science skills in existing finance employees not only kickstarts the AI journey but also deepens business knowledge within the models themselves. This creates a stronger, more seamless blend of technical capabilities and real-world understanding, increasing productivity while boosting AI adoption. Plus, fostering internal talent helps the business scale without relying too heavily on external hires.
- Buy | As AI initiatives expand, buying the expertise you need becomes essential. External data scientists can inject advanced technical knowledge into the organisation, taking AI solutions to the next level. The challenge here is the high price tag—hiring skilled professionals doesn’t come cheap. Finance leaders should make sure they’re investing in talent for complex, high-impact tasks that the current team can’t tackle. Otherwise, they risk paying a premium for capabilities they already have in-house.
- Borrow | The borrow approach fills gaps when short-term needs arise. Contractors or business service providers (BSPs) can help with routine tasks, build proof-of-concept projects, or temporarily ramp up AI capabilities. But it’s a balancing act—over-reliance on external resources can lead to escalating costs and disrupt long-term strategy. Borrow when you need to, but make sure it doesn’t become a crutch.
The future of AI in finance
Looking ahead, AI’s role in finance will only grow more central. What was once about AI ticking boxes and handling basic transactions will soon feel like ancient history. As AI matures, it will shift from a tool of automation to a partner that steadily powers the strategic heart of finance. Think risk mitigation, scenario modelling, and long-range forecasting – all powered by AI insights that let teams think bigger and act bolder.
AI won’t replace finance professionals, but it will elevate them. Armed with smarter tools and sharper predictions, finance teams will move faster, think clearer, and make decisions with a newfound precision that accelerates business growth.
In this data-driven future, the finance teams that get AI will be the ones setting, not keeping, the pace. For CFOs, the time to act is now: prioritise automation, build AI-ready teams, and unlock the full potential of AI to fuel smarter, more efficient business strategies.
Learn more:
- Why AI skills will be essential for your finance team in 2025 >
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- The AI capabilities hidden in cloud ERP >
- The newest and most powerful NetSuite AI capabilities >
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- The practical guide to AI-driven finance strategies >
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- Infographic: What AI really means for the finance function in Australia >