AI in financial planning 

Before we dive deeper into how AI can actually be applied in financial planning and analysis, it’s useful to first clarify what AI really is. 

AI is a broad term used in many different contexts, which can make it difficult to pin down exactly what it means — especially when it comes to financial planning. To make it more concrete, AI is often grouped into three main categories: language models, predictive models, and AI agents. All of these can be relevant in FP&A work. 

 

Which AI models can create value in FP&A? 

 

Language models 

Language models (LLMs) — such as ChatGPT — are generalists that excel at working with text. They can quickly understand, summarise, and draft content, or help ensure a consistent tone of voice. This makes them useful when you’re dealing with unstructured input from different parts of the organisation, such as budget comments, business plans, email threads, or notes in Excel. At the same time, they often require human judgement, as they can “hallucinate” (generate answers that sound confident but are incorrect). Their output is also heavily influenced by the quality of the input and the context they are given. 

 

Predictive models 

Predictive models are more like specialists. They are trained for a specific purpose — for example forecasting sales, identifying trends, detecting anomalies, or categorising data. Their main strength is their ability to uncover patterns and relationships in large datasets. 
Common challenges include transparency (often referred to as the “black box”problem, meaning the model can struggle to explain why it produces a certain result) and the risk of overfitting. Overfitting happens when the model learns random patterns from historical data and loses accuracy when real-world conditions change. 

 

AI agents 

I agents often build on language models, but take things one step further. They can break down a task into multiple steps, retrieve additional information and compare it with historical data, identify relationships between external data sources, and use tools and systems to go all the way from raw information to a finished analysis. 
The potential for more advanced automation in FP&A is significant — but for it to work in practice, you need strong data availability, clear instructions and goals, and well-defined boundaries (data, tools, and controls). 
 

Below is an overview of the three AI model types, along with their strengths and challenges. 

How AI can be used in FP&A today — opportunities and challenges 

For AI to create real value in financial planning and analysis, it needs to be connected to actual processes and day-to-day tasks within the organisation. These rarely revolve solely around budgeting and forecasting — they often span both strategic and operational needs across the business, such as: 

 

• Strategic decision support and business cases 
• Process development and continuous improvement 
• Data, systems, and model ownership 
• Operational financial analysis, reporting, and variance management
• Communication and support for the wider business 
• Budgeting, forecasting, and planning processes 

 

Below, we focus on the last three areas and how AI can simplify and improve the work involved. 

Operational and financial analysis — from numbers to insights 

Operational and financial analysis is at the core of FP&A. It’s also where AI already has the greatest potential to support the work today — mainly by reducing manual tasks and improving the quality of analyses. 

AI agents, for example, can be used to automate more advanced FP&A workflows. This goes beyond simply “answering questions” — it’s about acting on data and moving the work forward. 

An AI agent can flag variances using predictive models and proactively investigate deviations by pulling additional information and comparing it with historical data. It can also generate reports automatically by retrieving the underlying data, creating visualisations, and sharing them with the right stakeholders — while identifying relationships across financial, operational, and external data sources.

Imagine an agent detecting a margin deviation, pulling sales data, cross-checking delivery status, and summarising the root cause in a short message to the relevant controller — all without any manual effort. 

However, for this to work in practice, strong data access, clear instructions, and highly relevant context are essential. That’s also why relatively few companies have managed to make it work really well so far. 

From reporting to understanding — AI as support in FP&A communication 

A large part of FP&A work isn’t about producing numbers — it’s about explaining what they mean. This is where AI can be especially valuable.

Language models, for example, can help translate financial results into a more operational context by linking financial KPIs to the underlying business drivers. They can also summarise long reports, meeting discussions, or decision materials into shorter, more accessible formats. 

This makes it easier to tailor communication to the audience — whether it’s the leadership team, operational managers, or other parts of the organisation. Over time, AI may also help create more structure around definitions, KPIs, and business rules, reducing the risk of misunderstandings and inconsistent interpretations of the same figures or concepts across the business. 

AI can also play a more active role in dialogue with the organisation — for example by following up after reports are shared, collecting feedback, or capturing recurring questions. This gives FP&A better insights to continuously improve both reporting and ways of working over time. 

AI in budgeting and forecasting — faster analysis and better decision support 

Even in budgeting and forecasting, there are several areas where AI can add value — especially when it comes to analysis and scenario planning. 

Predictive models can be used to identify trends and generate forecast suggestions. By applying predictive models in budgeting and forecasting, organisations can also detect early signals of change, build like-for-like models, or categorise data. However, it’s important to remember that explainability is critical. One of the most common challenges with predictive models is that they can feel like a “black box” — you get an output, but don’t understand how the model arrived at the result. This reduces trust in the numbers and can make it difficult to justify decisions based on them. 

By using AI agents in budgeting and forecasting, you can take automation a step further than traditional models allow. This isn’t just about suggesting numbers — it’s also about breaking the process into steps, acting proactively, and providing explanations throughout. For example, you could receive automated forecast proposals, ongoing forecast updates based on new data sources, and explanatory comments for each calculation or assumption. 

It’s also important to remember that a budget isn’t primarily about predicting the future as accurately as possible. It’s a management tool built around goals, priorities, and strategic choices. AI can support this by simulating different scenarios and highlighting consequences — but the direction still needs to be set by people. It’s also worth keeping in mind that an AI model is only as good as the context it has access to. That’s why a clear data model and well-defined business logic make a real difference. 

In more complex planning processes, AI supported workflows can also improve transparency by adding explanatory comments to each assumption and calculation, rather than presenting figures without context. 

Does everything need to be AI? 

AI offers almost endless possibilities — but that’s exactly why it’s important to ask the question: does everything need to be AI? Some tasks can benefit greatly from AI, but there are also situations where traditional methods are simpler and more effective — for example sending deadline reminders. A scheduled calendar function can often do the job more reliably and at a lower cost. Used in the right way, AI agents can save time and improve quality — but used incorrectly, they can become expensive, unreliable, and more complicated than necessary. 

Get started with AI in FP&A

There’s no one-size-fits-all approach to how AI should be used in financial planning. But there are clear patterns in where the technology can already create value today — and which common pitfalls to avoid. In our guide AI in Financial Planning — Hype or Reality? we take a deeper look at: 

• how AI is used — and can be used — in financial planning today 
• where AI can create the most value in FP&A 
• practical guidance for getting started in a structured way

 

You’ll also find an example AI prompt for financial planning and analysis that you can try yourself. Download the guide and explore how AI can become a practical support in your financial planning. 

 

Download the guide