Viktoriia Bielkova

For decades, FP&A has been built around a familiar cycle: close the books, analyze variances, and write commentary explaining what happened.

But as AI capabilities continue to advance, it is worth asking a simple question:

Is this still the best use of finance’s time?

A large portion of FP&A effort today goes into preparing decks, writing narratives, and explaining movements in financials. These activities are important—but they are also increasingly automatable.

AI can already generate clear, structured commentary based on financial data. It can highlight drivers, detect anomalies, and produce summaries in seconds. Similarly, creating presentation decks is becoming faster and more standardized through automation.

This does not mean these outputs are no longer needed. It means they no longer need to be the primary focus of highly skilled finance professionals.

The real shift is not about eliminating commentary. It is about redefining where finance adds value.

Instead of spending hours explaining why revenue was up or down, FP&A should focus on building systems and processes that make those explanations immediate, scalable, and consistent.

More importantly, finance should focus on what comes next.

AI opens the door to a different model of FP&A—one that is less about retrospective explanation and more about forward-looking decision support.

This includes:

• implementing AI-driven forecasting models that continuously update based on new data
• enhancing scenario planning capabilities to evaluate multiple strategic options in real time
• identifying patterns and risks earlier, before they fully materialize in reported results

These are not incremental improvements. They represent a shift in how finance operates.

However, adopting AI in finance is not just a technical challenge—it is an organizational one.

It requires finance teams to rethink priorities. Time spent on manual analysis, commentary, and formatting needs to be reallocated toward building capabilities, improving data quality, and integrating systems.

It also requires a different skill set.

Finance professionals need to be comfortable working with data beyond traditional financial statements, understanding how models are built, and critically evaluating outputs generated by AI.

Judgment becomes more important, not less.

AI can generate insights, but it cannot fully understand context, business nuance, or strategic trade-offs. That remains the responsibility of finance.

Another important consideration is consistency.

When commentary and reporting are automated, alignment across internal reporting, leadership materials, and external communication becomes easier—but only if the underlying data and definitions are consistent.

Without that foundation, automation can amplify inconsistencies rather than resolve them.

Ultimately, the role of FP&A is not to produce reports. It is to support better decisions.

If AI can handle a growing share of reporting, commentary, and presentation work, then finance has an opportunity to refocus on higher-impact activities—understanding the business, challenging assumptions, and shaping strategy.

This shift is already starting to happen, but unevenly.

Some teams are still investing heavily in refining decks and narratives, while others are investing in automation, data infrastructure, and AI capabilities.

Over time, that difference will matter.

Teams that continue to prioritize manual reporting may move faster—but not necessarily become more effective.

Teams that invest in AI and transformation will spend less time explaining the past and more time influencing the future.

For FP&A professionals, the implication is clear.

The value is no longer in how well you can explain the numbers.

It is in how effectively you can build, use, and challenge the systems that generate them.