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It was just over two years ago when OpenAI introduced ChatGPT and reached 100 million users in a few short months. Since then, Artificial Intelligence (AI) has evolved, transforming many industries and shaping the future of technology.
For finance and accounting, AI has the potential to dramatically transform processes, enhance decision-making, and reduce costs. It should come as no surprise then, that 58% of finance functions already use AI in some manner. Moreover, 84% of those investing in AI for finance consider it meeting or exceeding expectations.
In Financial Planning and Analysis (FP&A), AI's capabilities improve workflows by automating repetitive tasks, unlocking structured real-time data, and leveraging generative AI capabilities to deliver timely actionable insights. In this post, we uncover some of the most popular use cases and trends in AI in FP&A for 2025 and beyond.
Many associate AI with automation, which is applicable to a certain degree. If semantics mean anything, Robotic Process Automation (RPA) paired with AI is the driving technology to automate repetitive, time-consuming tasks that take capacity away from more impactful measures.
Consider a junior FP&A analyst who spends most of their time in Excel. They field requests from finance executives and other business unit leaders to support decision making. The process begins with the analyst scouring for data dumps from multiple systems, which they aggregate in Excel. In Excel, the analyst has to structure, cleanse, and format the data just to establish a consistent data set to work from.
At this point, they are already a full day or two of work hours synthesizing the data for use. By the time the analyst can generate a report or reveal an insight, they are already half a week into the deliverable as the patience of the executive team grows thin. The analyst rushes to analyze the results and deliver the insights.
In this all-too-common scenario, 80 percent of time is spent on the data preparation and processing portion, and only 20 percent is spent on meaningful analysis with context. Best-in-class finance functions flip this balance. Leveraging automation frees up time for FP&A to focus the 80 percent of time on the more impactful strategic planning and analysis rather than data mining.
An overlooked application of AI in FP&A is the unlocking of financial data in a manner conducive to planning and analysis. For some, that data lies in multiple general ledger instances, for others it resides locked in various systems.
With RPA and AI, you can access structured and processed underlying data and consume it in your preferred frontend tool like Excel or FP&A Software. This eliminates the manual data cleansing workflow we highlighted in the example above, streamlining FP&A work to focus on analysis.
The more you can automate the preparation of financial reports, the more accurate and reliable your metrics will be. Plus, the time saved preparing reports can now be allocated to more mission critical efforts to support the business.
80% of CFOs said they expect their companies to embed more automation and digital technologies into their operations. – Deloitte CFO Survey
Generative AI (GenAI) has many use cases from creating content drafts, to source developer code, though it also has unearthed potential for FP&A. AI-infused systems with Natural Language Processing (NLP) that are purpose built for FP&A can have a profound impact on day-to-day operations.
NLP capabilities can analyze and interpret complex financial data to provide immediate insights and financial performance results. For FP&A, purpose-built finance Gen AI tools equate to having another junior analyst on your team.
Want to know who are your top 10 customers?
Curious how much you spent on travel expenses last quarter?
Wondering how net income from 2024 compared to 2023?
FP&A AI tools can easily handle these types of inquiries and produce immediate answers. Imagine how much time and effort it takes for a traditional analyst to pull this data from a source system, import it into Excel, format the data for analysis, and send you the results.
Equipping a single analyst with this capability gives them the capacity of two or three of their peers, saving you on headcount and maximizing productivity.
It is no secret that time is money, and every day that goes by where your strategy remains idle, your competition is one step closer to taking market share. The speed at which a company makes strategic decisions is an area of improvement for many.
Too often the desire to be data-dependent leaves you and your team handcuffed, hoping to discover that single metric that validates a thesis to act on. In some instances, gathering the necessary metrics to inform decisions takes far too long, and the quality of data remains questionable.
AI tools help augment decision-making by providing actionable insights derived from large datasets. All you need is someone at the wheel to inquire and interact with the AI tool.
Although AI has the potential to completely revolutionize daily FP&A roles, there are reasons to be cautiously optimistic. For one, hallucinations are not the only issue when blindly relying on AI. The key to overcome these issues is to provide ample context and support the model with reliable, historical data and assumptions.
AI-driven predictive analytics tools analyze historical financial data, market trends, and external factors to improve forecasting accuracy. FP&A analysts and finance professionals can use purpose-built AI models to help predict revenue trends, and expenditures to support business decisions.
Machine learning within AI can help detect patterns in financial performance to project future outcomes, helping organizations prepare for market fluctuations. That said, these predictions and forecasts are based on assumptions and historic records. If your input data is low quality, the projections will be as well.
When you are responsible for the results but can only point to the AI model’s output without any context or rationale, you put yourself in an indefensible position. Yes, leverage AI to help automate certain tasks, though you want to be certain to control the variables and assumptions to generate your own forecast and models that you can speak to directly when probed by an executive.
Given the rapid advancements in this groundbreaking technology, we are still in the early stages of AI in FP&A. As Amara's Law indicates, "we overestimate the effect of technology in the short run and underestimate the effect in the long run." Although we've reached widespread usage, most of it is still centered around testing and experimentation.
Machine learning within AI is evolving rapidly, and what was once thought to be a distant possibility is nearly a reality for many forms of AI. One of the much anticipated AI applications in FP&A and accounting is AI agents.
For example, AI agents will closely resemble the same capabilities of analysts. An FP&A AI agent could quickly analyze a subset of data, produce a broad set of insights, and create visualized metrics. From there, a more senior analyst or say a director of FP&A, could take that information and apply additional context to craft rapid, informed strategic initiatives for the organization's decision makers.
Make no mistake, AI is revolutionizing finance and accounting, particularly within the FP&A function. AI enables organizations to improve efficiency, reduce costs, and make data-driven decisions at a far greater pace than ever before.
Embracing these AI-driven trends can give your organization a competitive edge, positioning your finance team as a strategic enabler of growth and innovation. Full adoption and integration of AI in every imaginable workflow will take time—you just do not want to be at the short end of that bell curve.
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