Matt Wood
By Matt Wood, Global Head, Finance & Accounting Outsourcing, Personiv
Accounting and finance are on the brink of a technology shift that will accelerate processes, make forecasts more accurate, and create more value for stakeholders. This is good news for CFOs struggling to attract talent in the midst of a years-long shortage. It’s also a major change for a cautious field that often lags in technology adoption.
For example, many organizations are planning to adopt AI-driven automation to improve efficiency across the finance and accounting industry. With teams often stretched thin, AI can help alleviate workloads on employees by handling repetitive, high-volume tasks and boosting operational productivity. Because AI is so accessible now, it’s also leveling the efficiency and automation playing field among SMBs and larger companies.
Predictive modeling for financial planning and analysis
Financial planning and analysis (FP&A) has always relied on data to understand historical performance and predict future trends. AI can supercharge the planning function by more quickly analyzing larger amounts of company data and detecting patterns that can be difficult even for experts to see. A 2024 report on finance and accounting trends found that using AI can accelerate budget cycle time by 33% and reduce sales forecast errors by 57%.
Because AI excels at pattern recognition, it can surface emerging trends that might have otherwise gone unnoticed during the planning process, such as an ongoing rise in materials costs that’s small but persistent and widening in scope. Combining internal financial data with customer data can also allow for planning based on evolving customer behavior and needs. That can help identify the product or service lines that are likely to perform best in the future, for example, so planners can allocate more resources to them.
Scenario modeling for FP&A
Adding relevant third-party data to predictive models can allow organizations to tailor analysis and insights for a range of scenarios that are affected by factors inside and outside the organization. For example, combining weather trend data with supply chain cost data might help show how a bad hurricane season would affect a company’s transportation costs.
Even without the inclusion of external data sets, planners can use properly constructed and trained AI models to quickly analyze a range of scenarios. For instance, what’s the impact on revenue and cash flow of having the company meet its sales goals throughout the coming year versus missing half of them? What’s the potential financial impact of a proposed new tax rule? Having this scenario information allows for planning to cover a range of possible outcomes.
Because scenario and predictive modeling save time and money, they can also give finance and accounting leaders more resources to allocate to strategy development and implementation. One survey of UK finance professionals found that using AI in FP&A saves their organizations up to 200 hours and the equivalent of US$ 130,000 per year.
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Real-time reporting for visibility and risk management
AI-powered automation enables organizations to collect and analyze data in real time, providing instant visibility into insights as they emerge. This gives CFOs and other leaders the resources they need to make decisions quickly, based on the most up-to-date information available, rather than having to wait for analysis of historical data, which creates a time lag between data collection and decision making.
Data visibility for real-time decisions is always valuable. In a dynamic or turbulent industry, market, or global economy, it’s a survival necessity and a competitive advantage. When leaders need to pivot or adapt their current strategy to account for shocks like supply chain disruptions or major regulatory changes, real-time data can serve as a guidepost to the next best steps. It’s not surprising, then, that nearly 70% of CFOs now say AI is “essential for financial reporting.”
Digital twins for accounting and finance functions
AI-powered digital twin technology used to be the purview of leading-edge technology companies who wanted virtual models of their real-world assembly lines and products to enable training, virtual experimentation, and remote troubleshooting without disrupting manufacturing processes or risking damage to real products.
Digital twins now enable CFOs to create and leverage virtual models of relationships, structures, and processes throughout the entire product lifecycle and value chain. This allows them to test variables, compare outcomes, identify issues, and develop effective solutions. Digital twins can also reflect specific systems within an organization, for deep dives into process efficiency, cost, and results. Among enterprise leaders, 70% have already started looking into using digital twins in their organizations.
Setting up for success with new technologies
These technologies can help organizations make their accounting and finance functions more efficient and accurate, provide greater insight into real-time operations, and enable more accurate forecasting and planning. Together, they have the potential to remake the way accounting and finance teams work, and the business world is still at the beginning of understanding what we can accomplish with AI-driven tools as they advance in power and sophistication.
That said, every new tool brings a set of concerns that need to be addressed for successful implementation. For AI-backed tools, organizations need to fully understand the potential impact on data privacy, cybersecurity, and compliance with data governance regulations.
Training and skill requirements are another consideration. Does the organization have the required expertise in-house to get the most value from these tools? If not, is it more efficient and cost-effective to train current employees, hire new talent, or work with an implementation partner or managed services provider? The organization may also need strategies specific to change management that will shift the culture away from legacy practices while supporting technology adoption and realizing ROI.
Addressing these concerns is easier when your organization starts with a small use case. After fine-tuning the implementation and generating positive results, it becomes easier to scale up that use case and develop others across the finance and accounting function. Each successful rollout also reinforces the company’s cultural shift to embracing technology and puts your organization in a better position to capitalize on new AI-drive technologies as they emerge.
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