Data and Tech
What Are AI Agents? Here’s How They’ll Transform FP&A
Discover what AI agents are, how they’re reshaping daily FP&A workflows and what that means for the future of finance.
Main Content
We recently surveyed and analyzed the responses of over 200 finance professionals, ranging from managers to C-suite executives, for our State of Strategic Finance 2025 report in collaboration with BPM Partners.
And the most striking thing we found was the growing optimism around AI in finance.
Just a few years ago, many finance teams were skeptical of AI, with concerns about security, accuracy, and the potential for job loss. But that sentiment is shifting. Now, 57% of respondents are actively using AI in some part of their financial operations.
These numbers are a wake-up call for finance teams still underestimating AI’s potential and waiting on the sidelines. The question is no longer whether finance teams should use AI in daily workflows, but how to adopt it in a way that drives impact while protecting sensitive data.
In the following sections, we’ll break down how finance teams are using AI today and what this means for teams that have yet to make a move.
Before the widespread availability of large language models (LLMs), finance teams were already finding ways to automate their work.
Tools like Excel macros, enterprise resource planning (ERP) systems (for tasks like automating journal entries or consolidating financial statements), and robotic process automation (RPA) helped them speed up tasks like pulling data, matching transactions and creating reports. But they still needed constant manual updates, didn’t handle exceptions well, and couldn’t offer much in the way of usable insights.
Now, AI is building on that foundation in a much smarter way. Instead of relying on fixed rules, these tools can spot patterns in data, adjust to new information, and surface insights that help teams move faster and make better decisions.
Many of the finance leaders we surveyed say they are integrating AI into their existing workflows to make processes more accurate, scalable and easier to manage.
The top four AI use cases that emerged from the survey responses are data analytics, predictive analytics, anomaly detection and generative AI.
And this isn’t just a one-off finding. It reflects a much broader trend across the industry, as Craig Schiff, Founder and CEO of BPM Partners, put it in this episode of The CFO Show Podcast:
What we saw in this survey is that AI adoption for finance has picked up dramatically. We do multiple surveys a year on our own and with other companies, and we’ve seen the adoption rate, acceptance, and plans for using AI in financial systems grow quickly. Even vendors are now embedding AI in their newer releases to focus on very specific things finance teams need help with.”
Craig Schiff, Founder and CEO, BPM Partners
Let’s take a closer look at the top ways finance teams are using AI at work today.
Data analytics is the most widely adopted AI use case in finance, and it builds on tools and systems many teams have used for years, like Excel and Power BI.
AI takes those tools further by automating both analysis and reporting.
Instead of waiting for someone to run a report, adjust filters, or build a custom query, modern AI tools can scan large volumes of financial data, identify trends, and deliver real-time insights. These tools still require human oversight, but with far less hands-on effort compared to traditional methods.
Many of the teams we surveyed said they use AI for generating insights and summarizing data, which are at the heart of data analytics. These capabilities help teams go beyond the numbers to understand what’s happening in their business and where to focus their attention.
Some of these teams use agentic AI tools like Vena Copilot’s Reporting Agent, which lets them generate reports through natural language prompts, explore interactive dashboards, and access plain-language summaries, even directly within Microsoft Excel and Teams.
For example, in Vena Copilot you can request a revenue breakdown by account and receive a pre-formatted report based on your actuals that opens in Excel. From there, you can use built-in Ad Hoc reporting tools to get a granular view of how each business area is performing.
If a number is unusually high, you can drill into the details to uncover trends that might not be obvious at first glance. This helps your team understand what’s happening, why it’s happening, and what to prioritize next, helping the team move from reactive reporting to more forward-looking, strategic analysis.
Forty percent of the finance teams in our survey said they’re automating their forecasting efforts to some degree. While not all of these efforts involve AI, some teams are starting to explore how it can make forecasting more dynamic by spotting trends faster, adapting to new variables, and helping finance leaders make more timely, data-driven decisions.
With machine learning models that learn from historical trends, seasonality, and external signals, finance teams can generate more accurate forecasts with less effort.
Plus, more advanced platforms, like Vena Copilot’s Insights Agent, can support conversational forecasting, where finance teams can ask questions or explore different scenarios in plain language. For example, a user might ask, “How would a 10 percent increase in revenue in New York contribute to the organization’s gross margin?”
The system responds instantly with a breakdown of how that change would impact margins across the business.
This kind of scenario modeling makes it easier to test assumptions, evaluate trade-offs, and plan for uncertainty, helping finance teams move faster and provide more proactive, strategic guidance to the business.
Anomaly detection is another growing area where AI is proving valuable in finance, particularly for catching unexpected changes or irregularities in data before they snowball into larger issues.
In the past, spotting anomalies like sudden cost spikes, revenue dips, or errors in transaction data relied heavily on manual checks, rule-based alerts, and dashboards built around static thresholds. While helpful, these methods can miss more subtle patterns or generate too many false positives.
AI models, on the other hand, learn from your historical data to understand what “normal” looks like, then alert you when something meaningfully deviates. That could be a spike in operating expenses from a single region, an unexpected drop in cash flow, or even potential fraud indicators hidden in transaction patterns.
These kinds of early signals help finance teams reduce risk, improve accuracy, and spend less time combing through spreadsheets to catch issues manually.
Generative AI is starting to play a growing role in finance, especially when it comes to making complex data more understandable and usable across teams.
Traditionally, interpreting financial reports required significant time and context, particularly for stakeholders who were not as close to the numbers. Generative AI now helps streamline this process by creating plain-language summaries, answering contextual questions, and showing where the numbers came from.
For example, in Vena Copilot, a finance team might ask, “What is my budgeted year-over-year growth in revenue between 2023 and 2024?” Instead of just returning a number, the AI responds with the full calculation breakdown and explains how the result was derived.
This kind of auto-generated insight saves time and makes reporting more accessible to business leaders, department heads, and other non-finance stakeholders. Some platforms also use generative AI to help teams create board reports, write planning narratives, or even draft follow-up emails based on forecast changes or budget shifts.
Adoption of generative AI is still behind other use cases like predictive analytics. This is partly because finance teams tend to be more cautious with new technology. But as these tools continue to improve, the potential to simplify financial reporting, improve communication, and make insights more accessible will only grow.
We are at a real inflection point when it comes to AI adoption in finance.
The technology has already proven its value in areas like reporting, forecasting, and scenario analysis. And 82% of our survey respondents feel optimistic about AI's capabilities and potential impact on their departments. But, some finance leaders are still hesitant or unsure whether to adopt AI now or wait for the technology to mature.
So what does this mean for the future of finance teams, their roles, and the value they bring to the business?
We’ve seen this pattern before. When cloud computing first emerged, many finance teams hesitated, waiting for the technology to prove itself or become more mature.
It took a 5-year span for cloud platforms to become the standard, and today, it’s difficult to imagine running a modern finance function without them.
AI is following a similar path, but at a much faster pace–less than eight months in this case. And while AI isn’t replacing FP&A jobs, it is changing how these teams work and where they add value. The technology is already being integrated into planning, reporting, and forecasting tools that finance teams use every day.
Based on his work with finance teams, Craig Schiff has observed that positive word-of-mouth and early success stories are accelerating AI adoption. He puts it this way:
“Companies have started using AI and are expressing how it’s helped them, so now positive word of mouth is spreading. This also taps into people’s fear of missing out: AI is proven to some extent, and they think ‘If I don't move forward, I'm going to be left behind’. Software vendors have also made it easier to use AI with less reliance on data scientists. They've made it more purposeful, focused on very specific things that you know you need help with.”
So, the question is no longer whether AI will become part of everyday FP&A workflows; it already is. The real risk now is being slow to adopt it while other teams are already improving accuracy, increasing speed, and delivering deeper insights.
And chances are, your employees are already using AI tools like ChatGPT, whether officially approved or not. This kind of shadow AI usage introduces real data security risks. Without a clear AI policy and secure, sanctioned tools in place, companies may be unintentionally exposing sensitive information while falling behind more proactive teams.
As Sherief Ibrahim, National Business Applications Lead at Microsoft, put it, “Embrace the fact that your people are already using AI, whether you want them to or not, and celebrate that. Have them share best practices with the rest of the team. Create that adoption.”
Delaying adoption also means missing out on the compounding value of AI.
Teams that start using AI today begin building internal knowledge, adjusting workflows, and creating space for strategic work. Over time, that adds up, and they’ll be in a much stronger position to scale their efforts in the future and overcome the risks of AI in finance. Those who wait, however, may find themselves falling behind under tighter deadlines and greater pressure to deliver results.
Business leaders now expect real-time insights, more frequent forecasts, and strategic recommendations from finance teams, often with very little lead time. But traditional tools and processes were not built for that kind of speed.
AI is helping to close this gap. It enables faster forecasting, shorter reporting cycles, and instant access to critical insights. This becomes especially important during periods of market volatility, when teams need to respond quickly. For example, managing cash flow in the face of a potential recession or adjusting plans as costs shift unexpectedly.
Some teams are already seeing measurable results. Association for Institutional Research (AIR), for example, used Vena Copilot to speed up their financial reporting by 25% as a lean team. They also completed their audit season and responded to queries from multiple teams faster than ever.
As external pressure continues to grow, these kinds of time savings are becoming both helpful and essential. Teams that adopt AI now gain the flexibility and responsiveness they need to keep pace with changing demands.
AI isn’t replacing finance teams, but it is reshaping the job market. It’s changing what finance professionals spend their time on and what skills are becoming most valuable to employers.
Historically, a large part of finance’s day-to-day work has involved gathering data, formatting reports, and preparing decks for leadership. As AI takes on more of these repetitive and time-consuming tasks, it creates room for finance professionals to focus on advising strategy, guiding business decisions and modeling different paths forward.
And we can see clearly from the survey results that the shift is already underway.
In many teams, new workflows are forming around tools that use natural language prompts, auto-generated summaries, and built-in analysis.
FP&A professionals who learn to work with these tools will be better positioned to lead in this new environment. Building AI fluency while also focusing on higher-value skills that AI cannot replace—such as strategic thinking, cross-functional collaboration, and data storytelling—will help future-proof finance professionals’ careers as their roles continue to evolve.
Prompting an AI tool effectively, reviewing and interpreting its outputs, and communicating insights clearly across the business are quickly becoming essential parts of their role. These are not technical skills in the traditional sense. They are extensions of what you already do: ask questions, spot patterns, and tell stories with numbers.
So, as AI becomes a core part of the finance tech stack, learning to work with it is no longer a nice-to-have. It’s a necessary step toward staying relevant and driving impact.
Finance leaders can no longer afford to delay defining their AI strategy.
With teams under pressure to move faster and do more with less, the question is no longer whether to use AI; it’s how to start.
Agentic AI tools like Vena Copilot provide a low-friction entry point. Rather than requiring teams to learn a new system, Copilot integrates directly into your existing workflows. You can ask questions in plain language, generate reports, and run ad hoc analysis, all within the familiar Microsoft Teams and Excel interface.
Start small by automating a recurring report, testing a what-if scenario, or generating a plain-language summary for leadership. These quick wins can free up time for more strategic work and help your team build confidence in using AI day to day.
Want to dive deeper into how finance teams are embracing AI? Read the full State of Strategic Finance 2025 Report.
Uncover what’s driving finance leaders’ strategic planning in 2025.
Download NowJessica Tee Orika-Owunna is a content strategist and writer with over seven years of experience creating and repurposing relatable, helpful content for global brands including Contentsquare, Softr, Hotjar and Vena. She specializes in turning everyday product, user, and subject matter expert insights into product-led content that answers real buyer questions and supports better business outcomes.
