The role of the finance function is undergoing a fundamental shift from stewardship to foresight. As data volumes explode, the challenge for leaders isn't just processing information, but distilling it into actionable intelligence. In this deep dive, we look at why the AI revolution in finance starts with decisions, not software. Imane Haouassia breaks down the three pillars of modern FP&A; clean data, human-in-the-loop forecasting, and narrative storytelling, to show how teams can move beyond "pretty reports" to become true architects of company value.
AI won’t replace finance teams, but AI powered finance teams will replace the rest
How finance leaders can use AI to drive better decisions, not just prettier reports.
In the last few years, we’ve heard that AI and automation will “free finance to be more strategic.”
Yet in many companies, finance still looks the same; long hours before board meetings, last minute questions from leaders, manual checks in Excel, and many versions of “final” files. What has grown is the volume and speed of data, not always the quality of decisions.
The real shift now is that AI will not replace finance teams. But finance teams who learn how to work with AI will quietly replace those who don’t.
From looking back to seeing what’s happening now
Classic FP&A focuses on what happened last month, last quarter, or last year. It is useful, but slow. In a world of inflation, funding pressure, changing customer behaviour, and new competitors, that delay is expensive.
AI and automation allow finance to move closer to real time. Instead of static reports, teams can build driver based models that update as new data comes in. Tools can flag unusual trends in revenue, margins, or spending before they turn into big problems. Scenario analysis that used to take days can be done in minutes.
This does not replace traditional FP&A. It makes it faster and more forward looking.
Don’t start with tools. Start with decisions.
Many finance leaders start their AI journey with the question “Which tool should we buy?”. The better first question is “Which decisions do we need to make faster and better?
For example, where are we burning cash without a clear return? Which customers, products, or locations are truly profitable once we include discounts, refunds, and support costs? How quickly can we respond when investors ask about runway or downside scenarios?
Once these key decisions are clear, AI becomes easier to use. You can then ask; what data do we need? Where is that data today? How can automation help us answer these questions in hours instead of weeks?
Without this step, AI stays at the level of a nice demo and never reaches the real work of the company.
Three things modern FP&A needs with AI:
To turn AI from “cool to have” into value, FP&A teams need three core elements.
- Clean and shared data
AI does not fix bad data. It multiplies it.
Finance has to push for one source of truth for key numbers, clear definitions for metrics, and alignment across finance, product, sales, and operations. A simple test is; would you trust a new analyst to make decisions with this data today? If the answer is no, fix the basics before talking about AI. - Human + machine forecasting
AI models can spot patterns and build forecasts, but they do not understand context; a new competitor, a change in regulation, or a product launch that went better or worse than expected.
The best setup is “human in the loop”; let models create a baseline forecast, then have the team review it. Where do we agree? Where do we disagree and why? This keeps AI as a partner and a second opinion, not a black box. - Better stories, not just better spreadsheets
If AI can build reports and run simulations, the unique role of finance is to explain what the numbers mean and what to do next.That means turning data into a simple story; what changed, why it matters, and what we recommend. It means giving leaders a short list of key decisions, not a long list of charts.
Finance teams of the future may not be much bigger, but they will look different. More work will be automated (reconciliations, report refreshes, copy paste tasks…) People will need to be comfortable using AI tools, not as data scientists but as smart users.
Hybrid profiles (part analyst, part business partner) will become the norm. This is not about cutting people. It is about moving them away from low value tasks and into areas where human judgement matters.
The real risk is doing the bare minimum
Finance leaders do not need to be AI experts. But they do need to own one key question;
"How can we use AI and automation to support better, faster decisions in our company?"
Those who take this seriously now will shape what modern finance leadership looks like. Those who do not may discover that it is not AI that replaces them, it is the finance teams who learned how to use it.
