For all the buzz and headlines, many of us have fallen into a love-hate relationship with artificial intelligence. We recognize the immense potential AI can have on nearly aspect of our lives – the time it can save, the answers it can provide – yet, we intuitively know it’s like any other tool: It’s only helpful when you truly know how to use it properly (and dangerous when you don’t).
It’s no different in the world of financial planning and analysis (FP&A). In fact, in a recent FP&A Trends Survey, 59% of respondents said they were curious about using AI in the future to improve forecast speed and accuracy, yet only 6% are actually using it now for that purpose. The rest are really only using it for straightforward tasks, like Excel automation or other basic functions, instead of strategic transformation where it could potentially have the largest impact.
So, what is the role for AI and automation in FP&A, and how can teams use it properly so it adds efficiency and productivity, not just novelty?
Understanding what AI can (and can’t) do for FP&A
AI can do more than just polish your reports or retrieve simple answers. Its real power comes in the ability to automate processes like data analysis, driver prediction and forecast creation — which, when done correctly, can eliminate a substantial amount of manual effort.
In fact, machine learning (ML) – a subset of AI – can play a big role in FP&A through its ability to use algorithms to learn from data patterns and improve over time. This allows it to not only perform routine tasks (such as data retrieval) but also make decisions about that data, risk management, and even real-time scenario planning. This not only reduces the chance of human error but also makes every workflow more efficient.
For example, it’s not uncommon for companies with fully implemented AI and automation systems to trim their annual forecast process by up to thousands of FTE hours, or even run a revised forecast from new sales figures in just a few hours (versus several days and using potentially a dozen people).
Of course, for everything it can do by itself, there are still plenty of tasks that require human collaboration:
- Financial forecasting – While AI can tackle data processing and even make some initial projections, it still needs the human lens of strategic context and someone to validate its assumptions.
- Scenario planning – While you can leave the data analytics to AI, your finance team will need to interpret the various scenarios and their implications on company goals.
- Profitability modeling – AI can make complex calculations and even recognize ways to save money, but humans must make the tough decisions as to which opportunities make the most sense to the overall strategy.
- Performance analytics – Humans will need to interpret the trends and anomalies identified by AI across all data for their relevance to the business, especially related to the market and company goals.
The role of AI in FP&A: Why expertise still matters
No doubt about it: AI and automation are reshaping FP&A, but not in the fully autonomous way many suggest. Instead, I like to think of them more as powerful accelerators: streamlining data preparation, automating recurring reports, and running complex scenarios in seconds. When used well, these tools allow FP&A teams to focus on strategic analysis instead of mechanical tasks. But none of that eliminates the need for human judgment. In fact, without the right expertise guiding the process, AI often produces forecasts that look sophisticated but are fundamentally off‑base.
A major culprit is data quality. Many organizations still struggle to maintain a single, reliable source of truth, and fragmented data environments make it difficult for AI models to learn the right patterns. That survey from FP&A Trends found that only 22% of companies have unified, trustworthy data feeding their planning processes. That means that if the underlying information is inconsistent or incomplete, even the most advanced AI model will generate flawed outputs. Before AI can improve forecasting, the organization must address data governance, integration, and consistency before it can go any further.
Even with clean data, however, AI will never inherently understand your business. It can’t tell the difference between a true trend and a one‑time anomaly, or grasp operational constraints, pricing strategy, channel dynamics, or the political realities of budgeting. That’s why forecasting expertise remains essential. Skilled FP&A professionals know how to structure the data, choose the right models, validate the results, and build guardrails that prevent automation from drifting into inaccurate predictions. They know when to override the model (or when to retrain it), as well as when the assumptions need to change.
The organizations that get the most value from AI aren’t replacing FP&A teams but elevating them. Automation handles the repetitive work, while experts focus on scenario planning, strategic insights, and decision support.
How finance teams can adopt AI without the risk
All this, however, shouldn’t be seen as a recommendation to turn away from this innovative technology. Instead, it’s more about “proceed with caution” — but definitely still proceed, as Michael Coveney writes:
“AI/ML algorithms are a game-changer in that they allow FP&A to be more proactive within the business. … AI/ML is here to stay and will only become more critical as data and uncertainty grows. The question for FP&A is do they want to be part of the revolution or will they fade into irrelevance?”
Now’s the time to think through your plan for the year ahead to take one giant step ahead of your competitors. Our FP&A experts can help you set up and leverage AI and machine learning to complement your team’s expertise and give you that advantage you’ve been waiting for.
