Imagine if you could pinpoint when you’ll have the cash flow to hire another employee, or how a supply chain disruption would affect your business.
As a small business owner, I’m not a financial expert and I can’t predict the future. What I can’t learn or do myself, I automate. That’s how I started using AI for financial forecasting.
While AI in finance is useful for entrepreneurs, it’s helping companies of all sizes make more accurate predictions and better, data-based decisions. Join me as I explore the basics of AI financial forecasting and how you can test and adopt it yourself.
Table of Contents
Why Use AI for Financial Forecasting?
According to Gartner, 58% of finance functions are using AI in 2024, up 21% since 2023. More than a quarter of companies (28%) use AI for finance analytics, including forecasting. That number is rising fast. They’re using AI for everything from sales and demand forecasting to risk assessment to budget forecasting.
Here’s why companies are clamoring to add AI-powered financial forecasting to their toolbox.
1. Better Efficiency
AI models process data faster than humans — far faster. This speed saves time and costs from manual forecasts. Companies have reported lower operational costs and greater operational efficiency after implementing AI for finance.
“Our finance team spends 40% less time with AI forecasting compared to manual work,” reports Chunyang Shen, co-founder of Jarsy, Inc. “This saves time and leaves us with more time and effort to make key business decisions instead of doing computations.”
2. Fewer Errors
You can use AI to find anomalies and human errors in large datasets like expense reporting and invoices. One study found that machine learning models reduce forecasting errors by approximately 30% over traditional statistical approaches.
3. More Accurate Forecasts
With better data analysis, AI can create more accurate forecasts. Infosys reports that 80% of financial planning and accounting teams are now projecting more often and more accurately with AI tools.
4. More Timely, Data-Backed Decisions
Better, faster forecasts mean companies can make smarter decisions in real-time. AI can alert companies when forecasts change or key performance benchmarks are breached. That means that instead of waiting for monthly or quarterly forecasts, you can take decisive action now to reach your benchmarks.
And how do AI tools impact financial performance? Nearly 60% of companies using AI for corporate finance reported growing revenue, with 10% reporting growth of over 10%. Additionally, 31% of the same companies found that AI implementation cut costs, with 7% cutting costs by over 10%.
Limitations of AI in Financial Forecasting
AI is good at speed, scalability, and pattern identification. But it’s not without limitations. Inaccurate data inputs or not enough baseline data can result in faulty results. Then, there can always be outlier events.
“The future patterns are very useful and the algorithms can work with real-time data, but AI does not exclude all unexpected factors,” warns Shen. “Human management is still required for monitoring these factors or market fluctuations.”
How to Use AI for Financial Forecasting
All of this is exciting, but before diving in, I want to take a minute to understand how AI in financial forecasting works and how it differs from traditional forecasting.
“Historically, financial forecasting and analysis were predominantly qualitative, relying on small sample data and human expertise,” writes researcher Olubusola Odeyemi. “The methods employed were largely based on fundamental and technical analyses which involved scrutinizing financial statements and market trends to make predictions about future market behaviors.
“The advent of AI and machine learning has ushered in a new era, characterized by the processing of vast amounts of data and the application of sophisticated algorithms to uncover deeper insights and patterns,” she explains.
Welcome to the new paradigm — out with manual processes, and in with predictive intelligence.
So does AI financial forecasting work? In a nutshell, AI models use machine learning to analyze inputs from internal and external data sources to create future predictions.
Financial forecasting depends on inputs from historic and external data to produce outputs. AI models process, prioritize, and analyze financial data to help companies predict revenue, cash flow, expenses, and more. Here are the steps.
1. Data Collection
An AI model collects input from large amounts of data. This starts with your own historical financial data from costs to transaction histories to financial performance. You can also use retrieval-augmented generation (RAG) to connect your current sales or accounting software to AI to pull new data in real-time. The models then clean and process the data for analysis.
Some models also consider external data like stock prices, economic indicators, and social media sentiment.
2. Identifying Key Patterns
Next, the model uses feature engineering to identify the most important data points, like price trends or seasonality, to make the best predictions.
3. Choosing a Model
Based on the goal, AI financial forecasts may use different models. A time series model predicts trends over time like season sales, while deep learning models like LSTM can predict stock prices from historical data.
4. Testing and Training
The model learns from historical data, tests, and fine-tunes its model.
5. Forecasting
Now, we get to the output — the forecast. Once the model is ready, humans can prompt it to make specific predictions, set it to run at regular intervals, or send alerts if a prediction changes.
I think it’s helpful to see this in action. Here’s an example of how a forecast could look:
8 AI Financial Forecasting Tools to Try
I’ve got good news for you: You don’t have to build your own AI model to leverage AI financial forecasting.
The tools I curated below can integrate with your existing systems and analyze your financial data.
AI Financial Forecasting Tools for Small Businesses and Startups
1. Quickbooks
If you already use Quickbooks for accounting, great! No need to add a separate tool. Quickbooks apps like Fathom, LivePlan, and Clockwork can take your financial data and generate powerful cash flow and revenue predictions without messing with spreadsheets.
Quickbooks has also announced it’s adding AI forecasting features to the Quickbooks Online Advanced plan, so an upgrade could eliminate the need for a third-party app.
2. Upmetrics
If you’re a founder creating a business plan and projecting financial scenarios, Upmetrics will give you financial forecasts and a whole lot more. Built for collaboration, the tool leverages predictive analytics from historical data to anticipate future trends.
I love that the software walks you through financial planning, like identifying when you will have the cash flow to make a hire or purchase equipment.
3. Cube
Cube integrates with Google Sheets, Excel, and other systems so you can aggregate your financial data into a single source. Its AI tool can flag data anomalies, highlight variances, and even create multiple forecasts based on different scenarios.
AI Financial Forecasting Tools for Mid-Sized and Large Businesses
4. Datarails
Live in Excel? Datarails may be your sweet spot. Rather than replacing existing tools or documents, Datarails turns your Excel files into beautiful, intelligent forecasts.
One feature I like is its customizable dashboards and visualization options for reporting. Its “what-if” scenario modeling helps teams anticipate potential outcomes based on variables.
5. Vena Solutions
Vena Solutions uses AI pattern analysis and pattern recognition to analyze large financial data sets and model scenarios. Its automated forecasting frees financial teams up to focus on other activities and react quickly to real-time insights.
One feature I like is the integration of Microsoft Copilot so users can request forecasts and information with unstructured language (i.e., chat).
6. Planful Predict
Planful Predict uses machine learning to generate accurate forecasts based on historical data to identify trends and make predictions. I think scenario planning and a user-friendly database are the top benefits of the platform, along with its wide range of integrations.
Planful Predict has 1,400 pre-built connectors with software including Salesforce, Workiva, Workday, ADP, and NetSuite.
AI Financial Forecasting Tools for Enterprises
7. Anaplan
If you need the enterprise package of financial planning solutions, this is it. Anaplan integrates scenario planning, revenue planning, and headcount planning together in a complete connected planning software. Model “what if” scenarios to your heart’s content with Anaplan’s AI-powered tools.
8. IBM Planning Analytics
This software harnesses the power of IBM Watson to help finance professionals model multiple scenarios to ensure their business stays on track. One distinctive feature: IBM Planning Analytics can run several algorithms side-by-side to find the best fit.
How to Get Started With AI Financial Forecasting
If you’re bullish about the power of AI financial forecasting, slow down. I believe it’s best to take a measured approach to AI testing and implementation. So let’s see how you can take it one step at a time.
1. Set goals and priorities.
First, be sure to set clear goals for the implementation. Do you want it to save time and operational costs, or grow revenue?
Next, assess your current tech stack to determine which financial tools you already have at your disposal and which new forecasting tools could enhance those. Identify any must-haves for your product search.
2. Allocate resources.
Consider whether you have the expertise and resources in-house to start a program or whether you need to hire a consultant or data scientist.
In a Nvidia study, companies reported that their top challenges in implementing AI financial forecasting are data issues (privacy and disparate locations), AI talent shortages, and budget shortages.
“Using AI comes with its own set of hurdles, as with any tool,” says Marin Cristian-Ovidiu, CEO of Online Games, who recently adopted AI forecasting. “The initial integration into existing financial systems can be quite daunting at best and often demands a good amount of resources. There’s also a continuous requirement to update and train the AI models to keep pace with evolving market conditions.”
3. Train your team.
Shen underscores the importance of training your team for success. “Teams have to know how to properly analyze AI outputs together with qualitative data,” he shares. “I suggest introducing comptrollership skills through training sessions for corporate teams, using group education and training that merge the financial and technological teams.”
For instance, HubSpot Academy offers training for sales forecasting and analytics. I definitely recommend checking out these resources!
4. Run an AI financial forecasting pilot.
To test the waters before implementing at scale, start with a pilot. I find this true for any new technology you’re adding to your stack.
“I highly recommend starting with a more targeted approach,” says Cristian-Ovidiu. “Try to pick a specific financial aspect where AI can immediately demonstrate its value, and expand from there.”
Identify your top-priority area to test, like sales forecasting, and pilot the platform, then compare results with your traditional forecasts and actual financials. Once you’ve assessed the results and made any necessary adjustments, you can roll it out to other financial areas.
5. Learn and adjust.
Of course, just like traditional forecasting, AI forecasting isn’t perfect and can make mistakes. It’s important to set up your model and tools correctly, monitor trends, and double-check numbers when they don’t seem right or when using them to inform major decisions.
“The most effective feature was the feedback loop, where other members of the team contributed ideas that allowed for the improvement of the AI model’s outputs,” shares Shen, who led a pilot with a dozen members of Jarsy, Inc.’s team for AI financial forecasting.
“After a lot of brainstorming by our team and continuous fine-tuning, it became natural to include AI in our finance work.”
Automate Forecasting for Faster, Smarter Decision-Making
While researching for this article, I learned that AI financial forecasting has the potential to reduce human error, make predictions faster, and marry your financial data with market intelligence. Huge benefits, in my opinion.
While no tool can eliminate uncertainty, AI can reduce it significantly, helping you make data-backed decisions faster and with more confidence.
