Organizations have rapidly adopted AI-enabled financial forecasting and planning. A recent study indicates that the use of machine learning by FP&A has increased among companies since 2020, which is a rise of 16 percentage points.
AI Financial Software Adoption Gaining Momentum
Just a few years ago, a tiny percentage of finance teams were using AI, but according to recent data, a large percentage of finance teams are using AI. In Gartner’s 2024 survey of finance leaders, 58% of respondents’ finance teams are using AI, a 21-point growth from 2023! Of those finance teams indicating that they are not using AI yet, about half are in implementation, which is likely a strong pipeline of potential future users.
Similarly, in a 2024 global FP&A survey, only 6% said they were currently using AI, but an additional 59% said they planned to use AI in the future (15% in the next 6 months and 44% within 1-2 years!)
This indicates how quickly the adoption of AI has gone from optional to a requirement of finance planning. Large U.S. companies are driving this trend. More than three-quarters of U.S. companies are piloting or using AI in core finance functions, including accounting and financial planning.
AI Use in Other Finance Areas
Above 60% of organizations are using or piloting AI for treasury and risk management purposes. In areas traditionally considered slow to move like tax compliance, nearly half — nearly 43% — are planning or piloting AI solutions. The global KPMG survey indicated that at the beginning of 2024, nearly 75% of companies had adopted AI in at least one aspect of financial reporting processes and essentially all expect this will be the case in three years. The areas of finance that trail behind other functions in the pace of AI adoption are catching up; previously in 2022, finance lagged behind HR or legal functions in AI use, however, by 2024 “that gap is all but gone,” as CFOs accelerate AI proof of concept initiatives.
Finance Industry Leaders in AI Adoption
Some industries are taking the lead. The sectors of financial services and information technology are showing rates of uptake well above the general adoption rates of technology in this utilization of AI for forecasting and planning. In 2023, the financial services sector alone spent approximately $35 billion on AI financial software development services — one of the highest if not the highest of any industry.
AI Usage Expands Across Industries
A recent study from the Boston Consulting Group revealed that fintech, software, and banking have the biggest concentration of ‘leaders’ (i.e., organizations that have developed and scaled their AI solutions). These sectors were some of the first to recognize AI’s potential value to manage complex forecasts that incorporate lots of data (e.g., banks using AI to model potential credit risk and FinTech analytics using AI to explore their predictive modeling needs).
We are now starting to see AI begin to pop up in essentially every industry and sector. Other industries like manufacturing, retail, healthcare are using or beginning to use AI-enabled planning models to improve demand, forecasting, and financial flexibility. For instance, industrial juggernaut, Siemens, is using AI in their FP&A processes and have been able to state that their use of AI has led to an estimated 10% improvement in forecast accuracy for their financial projections. In short, organizations that were ‘testing the waters’ using AI-enabled forecasting and planning tools in select sectors and industries have moved quickly into broadly developing AI-enabled forecasting and planning tools across the organization.
Benefits: Accuracy, Efficiency, Cost Savings, and Agility
Companies that are using AI-based forecasting software are already identifying real performance benefits and that is fairly consistent, if not exceeding original expectations.
Greater Forecast Accuracy
The primary advantage is better forecast accuracy. AI’s ability to sift through much more historical and real-time data will normally generate better forecasts than simpler manual approaches. In fact, AI/ML users have rated the level of quality of their forecasts much higher than non-users. For example, AI users have a much higher percentage of forecasts rated as “good or great” (65% vs 40%). Siemens for example verified that, with forecasting, the accuracy is better (approximately10%) with AI incorporated into different forecasting models.
However, more modest levels of forecast accuracy can still produce large financial benefit through lower levels of surprises and buffer costs. For example, a global entity discovered that machine learning, whilst certainly better than forecast accuracy, still only achieved about1.6% more accuracy than forecasting based on traditional approaches; not spectacular in itself, however it is a small but significant benefit that aggregates to billions of dollars in revenue annually. Improving forecast accuracy ultimately enables optimization of resource allocation and decision-making as the plans are based on fewer inaccuracies.
Time Savings
The other major benefit is efficiency and time savings. AI tools perform data sourcing, data gathering, reporting and even variance analysis. They do this automatically, and as a result, manual workloads for finance teams are drastically reduced. Forecasting took days or weeks to complete standard tasks, and now the same tasks can take hours or even minutes.
Philips NV stated that with AI, generating forecasts, their FP&A (finance and planning analysis) team could complete 80% of a complete P&L in 3 hours when they would have needed a few days with about 200 people. Their productivity had substantially improved. In a second example, a large US health insurance provider, issued earning generates in less than a few minutes with an AI-enabled business planning platform and discounted future value Baseline Market data, would take it weeks to source the data previously.
Cost Savings and ROI
It also was a reduction in planning cycles; rolling forecasts can be updated frequently enough to keep plans flexible and aligned with what is really happening in the organization. Organizations are now seeing cost savings and ROI benefits from their AI-enabled forecasts. Most importantly, when a forecasting process is automated, it eliminates some labor costs, and reduces the costly mistakes that can be difficult to correct.
More accurate forecasts allow organizations to avoid excess inventory levels and working capital restrictions associated with forecast mistakes. A more recent Salesforce SMB survey found that companies reporting AI usage in the forecasting process had an error rate about 40% lower than those relying solely on traditional forecasting processes.
Better cash forecasting means less cash collecting dust, as well as better managed debt – one consumer goods company (King’s Hawaiian) implemented AI for cash forecasting and reported interest expense savings of 20%, from better cash management and cash visibility.
Many early adopters are seeing a fast payback on their investment. In a 2024 study of U.S companies, 61% of “AI leader” companies said their AI ROI in finance exceeded expectations (none reported they were underwhelmed). Even those companies that were in pilot stages reported good results — 1 in 3 cautious adopters said they were already above forecast.
This is reflective of AI being able to generate savings directly (from process automation and headcount efficiencies) and unearth revenue opportunities (from improved insights and predictions). Given this, it is no shocker that 78% of business leaders are optimistic that their generative AI investments will yield either cost reductions, or increased revenue, in the next 1–3 years.
Agility and Rapid Planning
Perhaps the most widely promoted strategic advantage is informed decision-making at a faster rate. An AI-based planning system can continuously ingest new data (sales performance, market signals, etc.) and adjust predictions as necessary. The ability to respond quickly will enhance agility, giving organizations a better opportunity to realize and respond to emerging risks and opportunities. Finance leaders indicate that there is greater runway for the business to respond effectively and get back on track when deviations or “surprises” are identified enduringly (e.g., when demand goes soft, or when it is spending to the plan).
Deeper Insights and Risk Detection
AI tools allow for rapid simulations of several what-if scenarios. They can model the financial impacts of many assumptions (e.g., a change in interest rates or shock in supply chains) and assist in developing real-time contingency plans, as opposed to a quarterly or annual forecasting cycle. As one CFO mentioned, “with the ability for real-time forecasting and scenario modeling, you change the cadence of planning from a scramble every quarter, to a proactive iterative process”.
Agility was certainly evident in 2023’s uncertain environment – companies that invested in AI-enabled forecasting could act with confidence – to adjust for interest rate swings, labor cost anomalies, and supply chain disruptions – using AI to quickly reforecast and adjust expectations. In short, AI is driving continuous, data-informed, and resilient finance planning – a critical source of competitive advantage in today’s constantly shifting environment.
Widening the Types and Depth of Insights
AI can facilitate advanced analytics and machine learning models can surface relationship, or drivers of performance, that humans may not identify. For example, an AI-based forecast system may meaningfully allow for external variables (such as commodity prices, weather, and consumer sentiments), and help identify potentially hidden relationships to financial outcomes. Models can offer more insights and manage risk more effectively. Finance teams are also saying AI is helping them to identify anomalies (potential fraud or error identifications), and identify key business drivers that they may not have been able to ascertain in the past.
In practical terms, this translates into better planning as you are getting better attention to the things that matter the most in terms of revenue or costs. It can focus the discussion on those factors we care about most and minimize potentially unproductive distractions. CFOs are increasingly seeing AI as a “key growth enabler” for the business, rather than simply a efficiency play in the back office. In a recent KPMG study (2022) 46% of companies indicated they viewed AI as one of their biggest growth drivers, while more than 70% of finance leaders said they saw “more opportunity than challenge” to undertaking generative AI. Therefore, AI-enhanced forecasting, by bringing increased accuracy, speed, cost, and strategic insight, is increasingly finding the finance function coming in from the cold of the back office as rigid number crunchers and into a more agile and value-generating part of the enterprise.
Enterprise AI Forecasting Use Cases
Manufacturing
By examining actual use cases, it is easy to see how a number of companies have been able to successfully leverage AI forecasting for advantages in the marketplace. At the start, big manufacturers worldwide started applying AI to enhance their complex operational forecasts.
For example, Siemens AG developed AI-based dashboards for enhanced financial planning and analysis that used real-time data integration to allow their finance team to improve decision-making capabilities and speed. These initiatives delivered about a 10% improvement in forecast accuracy, allowing Siemens to have a more reliable estimate on orders, revenues, and costs.
At the same time, Philips was implementing machine learning and data analytics into their operating forecasts. With this work, their finance team has generated on-demand forecasts of their full profit and loss outlook in a matter of hours (most of it is done in hours instead of days), and with high accuracy. The speed to deliver a forecast has been transformative for Philips as a quickly-rising input cost or a sudden demand spike challenge their operations. Both Siemens and Philips demonstrate how large organizations are now embedding AI forecasting into their planning processes, facilitated less out of necessity and more as an evolution of operating and strategic intention, but these were studying as standard operations with their existing systems (like ERP or EPM solutions) to improve operational efficiencies and accuracy.
Technology & Software
Microsoft and other tech companies are placing an emphasis role on innovation. They’re already using internal AI models to predict sales accurately – sometimes to the point of 99 percent accuracy. These powerful models consider a large number of signals (e.g., customer relationship data, high level / real-time macroeconomic trends, etc.) and can process much more than manual analyses.
Smaller, progressive, high growth tech companies are using AI too. Particularly in fintech and SAAS, AI is being used to predict churn, forecast revenue, and dynamically optimize budgets in real time, which is critical for organizations trying to get their product(s) to market.
Financial Services
Banks and other financial institutions, such as insurance companies, are also one of the major groups looking to utilize this technology. They’re looking at using AI to help them forecast loan defaults, plan for liquidity requirements, and improve their approach to financial planning.
For example, a large U.S. health insurance company recently transformed their financial planning process with AI-driven software. The software can automatically identify claims data and broader economic and regulatory trends, and then provide continuously updated forecasts. Something that used to take weeks now takes minutes, by so increasing the finance teams speed and accuracy of finance projections across multiple scenarios. This makes that insurer more agile when planning premiums and reserves in a volatile healthcare market.
Consumer Products & Retail
AI forecasting isn’t exclusive to finance. The consumer products makers and retailers are using AI for other purposes too — particularly predicting demand and managing cash flow. For example, King’s Hawaiian is a mid-sized snack maker that employed a tool (AI-based) to predict cash flow based on up-to-date data regarding sales, shipping, and customers paying. That tool helped them hold the right amount of cash (i.e., they avoided unnecessary borrowing) and was able to lower their interest costs by over 20%, because they had better information about their cash needs.
In fact, in retail it seems that AI is being used to figure stuff out like how much inventory needs to be stocked and how many staff will be needed. In a nutshell, with better sales, you have a better chance that you’re stocking just the right amount of product (i.e., not too little and not too much). Some reports state that this could mean your inventory costs could be reduced by as much as 20%. These types of savings give you money to spend on other stuff.
Let’s consider a startup e-commerce site. They utilized AI to better predict demand, and they see a 20% increase in sales because they could adjust their marketing and supply chain strategies early based on what they learned from the AI. These were exemplary examples of how AI goes beyond cost reduction — it can actually enable companies to scale by improving operational alignment and agility.
Human feat. AI in Finance
Most notably, these companies are coming to understand that AI does not replace people; instead, it is a complement to humans that expands the capabilities of the finance teams. Some companies start small like testing AI in (or on) one product line or automating even one report, and then growing from there when they realize it actually works.
Culture and Change Management
Having consensus on this change means finance’s job is to train people and work through the process deliberately. Finance teams are now using AI to find unusual patterns — whether that be possible fraud, possible mistakes, or understanding the primary business drivers of their company that are hard to see before.
Once everything falls into place, AI is sort of like a co-pilot in finance — working on data processing, finding patterns, while humans make all the assumptions, validate outputs, and make strategic decisions. A finance director mentioned how AI had enabled his team to be “way more predictive and data-driven” so that people could be free to focus on big-picture thinking rather than just number crunching spreadsheets.
AI as a Mainstream Finance Tool
To sum up, organizations in industries everywhere are applying AI for forecasting and planning, and users are reporting great benefits. In manufacturing, finance, retail, and public service, users are reporting better accuracy, faster decision-making, and greater agility in planning for the future.
Since 2020, use of AI has spread throughout growing numbers of people and organizations and is likely to continue. Survey information published in recent articles show that nearly all major corporations in the U.S. plan to employ AI in their finance functions in the next few years.
There is of course this undercurrent of data quality and challenges with change-management that we will always need to deal with. But with each additional win from early adopters, the more we can see new decision-makers sampling AI in their own functions. AI-forecasting tools are now firmly established as a common class of enterprise software, and will allow finance teams to better apply their professional judgement and exercise their bat temperature when making decisions. As we look ahead through the 2020s, we expect to see AI become de-facto for financial planning and analysis, unblocking forecasting and planning for organizations of all types and sizes, and providing better insight than ever before.
