How FP&A Teams Can Position Themselves to Better Use AI

Every FP&A team is hearing the same message right now: AI is coming, and you need to be ready. But "be ready" is frustratingly vague advice when you're a two-to-three-person finance team already stretched thin by budget cycles, reforecasts, and board reporting.

The good news? Positioning your team for AI doesn't require a massive transformation project or a data science hire. It requires practical, sequential steps that deliver value on their own while building the foundation AI needs to be effective. Here's what that looks like in practice.

Step 1: Fix Your Data Foundation (This Is Non-Negotiable)

Every AI application in finance—forecasting, variance analysis, anomaly detection, scenario modeling—depends on clean, consistent, well-structured data. If your data isn't trustworthy, AI will just give you faster wrong answers.

A 2024 study in Frontiers of Computer Science found that 94% of business spreadsheets contain errors. Meanwhile, the FP&A Trends Survey revealed that 30% of organizations haven't upgraded their planning systems in over five years. If your team falls into either category, data quality is your first priority—before any AI investment.

Sources: Spreadsheet error data from Poon et al., Frontiers of Computer Science (2024). System upgrade data from FP&A Trends Survey (2025). Personnel cost benchmarks from U.S. Bureau of Labor Statistics, ECEC (2023).

If you're still running on spreadsheets, the most impactful AI preparation you can do is move to a purpose-built planning platform with native GL connectivity. That single step transforms your data quality, eliminates manual errors, and creates the structured foundation every AI application requires. Our article on designing an AI-driven integration architecture goes deeper on how to think about this infrastructure layer.

Step 2: Automate the Manual Work That's Consuming Your Team

Here's a practical framing: every hour your team spends on mechanical data work is an hour they can't spend on analysis, business partnership, or learning new AI-enhanced capabilities. The 2026 AFP Benchmarking Survey found that the average budgeting cycle still takes nearly nine weeks. Much of that time goes to data collection, reconciliation, and consolidation—tasks that modern planning platforms automate entirely.

When you automate these fundamentals, three things happen simultaneously. First, your team gets immediate time savings that improve quality of life and output quality. Second, you create the clean, structured data flows that AI needs. Third, your team starts developing the analytical skills and strategic mindset that AI-enhanced work requires—because they finally have the bandwidth.

This is the part that often gets missed in the AI conversation: the biggest barrier to AI adoption for most FP&A teams isn't technical capability or budget. It's time. Your team is so consumed by manual processes that they can't step back to evaluate, learn, and adopt new tools. Automating the fundamentals breaks that cycle. For guidance on making this shift, see our article on using flexible forecasting to future-proof your budget.

Step 3: Build the Skills That AI Makes More Valuable

As AI handles more of the mechanical work, the skills that differentiate FP&A professionals shift. IBM's research on AI in FP&A describes this as a move from reactive reporting to proactive strategic partnership. Here's what that means for your team.

Data storytelling. When AI generates the analysis, the human skill becomes communicating what it means. Can your team translate a variance analysis into a narrative that helps a department head make a better decision? Can they present scenario outcomes to the board in a way that drives action?

Business partnership. AI can identify patterns and outliers, but it can't build the relationships that turn financial insights into operational changes. FP&A professionals who understand the business—who can sit with a VP of Operations and jointly work through what the numbers mean for their hiring plan—become more valuable, not less, in an AI-enhanced environment.

Critical evaluation of AI outputs. This is increasingly essential. When AI generates a forecast or flags an anomaly, someone needs to evaluate whether the output makes business sense. Does the AI's revenue projection account for the pricing change we just announced? Is the anomaly it flagged a real problem or an artifact of how we restructured cost centers last quarter? That judgment layer is pure human value.

Process design. As AI capabilities expand, FP&A teams that can redesign their planning processes to take advantage—rethinking what gets automated, what requires human judgment, and how the two integrate—will capture disproportionate value. This is an emerging skill set, and the teams that develop it early will have a significant edge.

Step 4: Adopt AI Incrementally, Starting with Embedded Capabilities

You don't need a separate AI strategy. You need a planning platform that embeds AI where it matters most. The Citizens Bank survey found that 82% of mid-size companies plan to increase AI investment, but the successful ones are doing it incrementally—not through big-bang transformation projects.

Start with AI capabilities already built into your planning platform: automated variance explanations, anomaly detection, smart baseline forecasting. These deliver value within existing workflows without requiring new tools, technical expertise, or change management programs.

As your team builds confidence and sees results, expand to more advanced applications—predictive scenario modeling, natural language queries against financial data, and proactive reforecast triggers based on variance thresholds. Each step builds on the previous one, creating a virtuous cycle of capability and confidence.

Step 5: Make the Business Case for Modern Infrastructure

If your team is still running on spreadsheets, every step above starts with the same prerequisite: a modern planning platform. The good news is that the business case for this investment stands on its own, even before AI enters the picture. Budget cycles compressed from nine weeks to two or three. Manual consolidation eliminated. Workforce planning at the position level instead of aggregate assumptions. Collaborative workflows that actually work.

AI is the acceleration layer—but the foundation delivers immediate, measurable value on day one. Our guide to building a business case for FP&A software walks through exactly how to frame this for leadership.

The Bottom Line

Positioning your FP&A team for AI isn't about hiring data scientists or buying the flashiest new tool. It's about building the data foundation, automating the manual work, developing the skills that AI makes more valuable, and adopting capabilities incrementally. The teams that take these steps now won't just be ready for AI—they'll already be experiencing the benefits, one planning cycle at a time.