What Are AI Agents? Here’s How They’ll Transform FP&A

Everywhere you turn online, people are talking about AI agents, and it’s easy to see why.

AI is evolving from simply responding to questions to actually taking over entire tasks independently. Give an AI agent a goal, and it’ll get to work: triggering workflows, adapting to live updates, and making decisions along the way, all with minimal oversight.

We’re already seeing this in action. 

OpenAI’s new Operator, for instance, can take a high-level goal like “order dinner for four under $50” and carry it out on its own, navigating the web, filling forms, and executing multi-step tasks. Microsoft is also advancing in this space with Copilot Studio, which enables users to build custom AI agents that retrieve information and automate workflows based on your organization’s data.

For FP&A teams, this shift is a game changer.

Instead of spending hours pulling data from multiple systems, cleaning spreadsheets, and double-checking numbers, AI agents can take care of those repetitive steps. That frees your team to focus on what really matters: interpreting results, advising leadership and driving strategy.

In this article, we’ll explain what AI agents are, how they have the potential to reshape daily FP&A workflows and what this means for the future of finance.

What Are AI Agents?

AI agents are software programs powered by artificial intelligence that can autonomously perform tasks.

Unlike traditional AI tools that require detailed instructions for each step, AI agents are goal-oriented. They can carry out multi-step assignments across different tools with little to no human input.

At its core, agentic AI is built on top of large language models like GPT. 

The model understands the user’s request, while the agent acts as the executor. It collects the context, decides what needs to happen and coordinates actions across different tools. This pairing enables agents to reason through tasks, trigger workflows and adapt as new information becomes available.

To do this effectively, AI agents rely on three foundational components:

• Memory: The ability to retain relevant context so they don’t start from scratch with every prompt. This enables continuity across tasks and conversations.

• Entitlements: Permission to securely access the systems, data, and tools they need (like financial models, internal reports, or approval workflows).

• Tools: Integrations with platforms such as spreadsheets, ERP systems, or dashboards that allow agents to take meaningful action on your behalf.

Together, these components allow agents to do more than assist. They can autonomously complete real, business-critical work behind the scenes.

For FP&A teams, this means significantly less time spent on manual, repetitive tasks and more time focused on high-impact work like interpreting results, stress-testing assumptions, running scenario plans, and advising leadership on strategic decisions.

AI Agents vs. AI Assistants: What’s the Difference?

If your FP&A team already uses generative AI tools like ChatGPT or Microsoft Copilot, you might wonder: Is there really a difference between AI assistants and AI agents?

AI agents go beyond suggestion and text generation. They carry out entire workflows with minimal guidance. Once given a high-level goal, they can:

• Break down the task into smaller steps

• Decide what to do next based on data and context

• Interact with multiple systems (like ERPs, BI tools, HRIS systems, or spreadsheets)

• Deliver a completed output, like a forecast model, reconciled financials, or a dashboard

Simply put, AI assistants support your work, while AI agents help do the work for you.

Here’s a hypothetical view of how the two compare across common FP&A workflows:

Even better, AI agents can help put real-time financial data into the hands of business stakeholders without routing every question through FP&A.  

Instead of waiting for a monthly report or asking an FP&A analyst to pull numbers, a department head could simply ask the agent directly, “What’s my team’s spend versus forecast this quarter?" They would get an instant, contextual response with visualizations and commentary pulled from live data in your ERP or planning system.

How Can AI Agents Be Used for FP&A?

AI agents bring a new level of speed, automation and clarity to everyday FP&A tasks.

They’re especially useful in moments when you need insights on the go.

Say you’re meeting with executive leaders or the board and need to pull up current numbers, explain variances, or respond to follow-up questions in real time. Instead of scrambling to check multiple systems or provide answers after the fact, you can rely on an AI agent to retrieve the correct data, run the analysis and surface key insights right when you need them.

AI agents also shine when it comes to recurring, time-consuming workflows like monthly reporting.

Let’s say it’s the end of the month, and you need to prepare a revenue variance report.

Normally, you’d log into your ERP to grab actuals, then open your spreadsheet to compare against the budget. You’d do some manual calculations, highlight the biggest variances, and finally pull it all into a slide deck for your leadership update. That takes too much time.

With an AI agent, you could simply say, “Create a revenue variance report for March. Highlight any accounts with over 10% variance from budget and draft some commentary.”

And the agent would:

• Pull actuals from your ERP

• Compare them to budget data and flag key variances

• Suggest likely drivers for significant differences

• Draft bullet-point commentary

• Drop everything into a ready-to-share slide or dashboard

Instead of doing the heavy lifting yourself, you’re reviewing and refining.

Here’s a quick overview of the different ways AI agents can support FP&A workflows:

Can Enterprise Finance Teams Trust AI Agents?

As with any technology, AI agents are only as reliable as the integrity of the data, logic and governance that support them. In Finance, where accuracy, compliance and transparency are non-negotiable, AI agents must meet these criteria to be considered trustworthy.

They must be:

• Built on secure systems with enterprise-grade data protection, role-based access controls, and detailed audit trails

• Tested against internal standards to ensure accuracy, consistency, and alignment with financial reporting expectations

• Used with clearly defined approval processes so that your team is reviewing outputs for refinement and governance before sharing them

Let’s go over each of these criteria briefly.

1. Security

Security is one of the most important considerations for any finance team evaluating AI tools because these agents don’t just analyze data. They access, retrieve, and act on it.

Financial workflows include highly sensitive information like revenue figures, headcount assumptions, forecasts and pre-close reports. If mishandled or exposed, it can result in compliance violations, reputational damage, or financial loss.

That’s why any AI agent used in an enterprise context needs to have a secure foundation. 

Leading platforms typically incorporate:

• Data encryption, both in transit and at rest

• Audit logs, so every action is tracked and traceable

• Role-based access control to limit data access based on permissions

• Compliance certifications like SOC 2, GDPR, and others, depending on your industry

Before adopting any AI tool, confirm these safeguards align with your organization’s IT governance and data privacy policies. Only trust tools that offer transparency, robust safeguards, and clear accountability for how your data is protected. As Rishi Grover, Co-Founder and Chief Solutions Architect at Vena Solutions, put it in a recent interview on The CFO Show:

“If you’re a public company, you already trust cloud-based tools to handle sensitive pre-release data like 10-Ks and 10-Qs. AI is no different. Just make sure the tool is secure and compliant with your internal and external protocols.”

2. Establish Oversight

Even the most advanced AI agents require human oversight, especially in finance, where decisions must be grounded in sound judgment, context, and accountability.

That means you need to know how your AI agents work, where they’re pulling information from, and how they’re arriving at conclusions. In short, you need both visibility and control. Rishi, in the same episode, puts it this way:

“AI is your co-pilot. It should not be flying the plane. You are flying the plane. There has to always be that human oversight to what an AI application is producing.”

That oversight starts with transparency. If you’re going to trust an agent’s recommendations, you need to see exactly which data points it used, what logic it applied and how it got from input to output.

Tools like Vena Copilot make this possible by showing users the full path, from raw data to commentary, so you can validate every step. This level of visibility builds confidence, especially when the outputs feed into executive reports, forecasts or strategic planning.

Confidence in the output of AI agents also depends on data readiness. 

AI is only as reliable as the data it learns from. Large language models perform best when trained on clean, consistent, and centralized historical data. As Rishi explains:

“Most companies are already moving toward a centralized data strategy, pulling data from across systems, normalizing it, and storing it in a shared environment. The Office of Finance needs to be the steward of that data, ensuring it’s fresh, reliable and monitored consistently.”

Once the data foundation is in place, oversight becomes an ongoing process of monitoring, refining, and improving agent performance.

Best practices when it comes to overseeing AI agents include:

• Requiring human review and approval of the agent’s outputs before they’re shared or acted on

• Keeping outputs editable, so teams can refine commentary, correct assumptions, or tailor insights to stakeholders

• Creating a feedback loop so the system learns from outcomes, user input and changing business needs

• Maintaining traceability so every recommendation or calculation has a clear audit trail

3. Testing for Accuracy

Before fully relying on AI agents, you need proof that their outputs are accurate, consistent, and aligned with your standards.

That means validating not just the final result produced by the agent, but also how the agent arrived at it, especially in use cases like forecasting and modeling and variance analysis, where minor mistakes can lead to significant consequences. Accuracy isn’t something you check after the fact; it’s something you design for upfront.

To build confidence in their AI agents and catch issues early, finance teams should:

• Run parallel tests using known datasets and compare agent outputs against manually prepared results

• Validate the logic and calculations embedded in workflows, ensuring assumptions are sound and context-specific

• Use sandbox environments to simulate workflows and identify issues before going live

• Start with low-risk use cases, like commentary generation or basic data pulls, before scaling to more complex or business-critical tasks

With the right testing process in place, your AI agent can evolve from a promising tool into a reliable, audit-ready extension of your finance team.

What Do AI Agents Mean for the Future of FP&A?

The prospect of AI agents may cause some finance professionals to sweat, thinking that they will replace their jobs. But as Rishi puts it, AI isn’t here to displace finance professionals but to free them up.

“I have so much respect for the Office of Finance. These are incredibly intelligent individuals who joined their organizations to contribute to strategy, not to spend their days buried in manual tasks,” Rishi says. “AI is the co-pilot, not the pilot. With that support, finance can focus more on high-impact, strategic work.”

AI have the potential to redefine what it means to work in FP&A, allowing the function to move from reactive reporting to proactive, insight-driven business partnering.

This means:

More Time for Strategy, Less Time in Spreadsheets

Many FP&A teams still spend more time collecting and cleaning data than analyzing it. In fact, the 2022 FP&A Trends Survey found that only 33% of FP&A teams’ time is spent generating insights, while 45% goes to low-value tasks like manual data prep. AI agents flip that script, helping to automate some of that report building so teams can focus on data interpretation, storytelling and advising the business.

Forecasting Gets Faster and More Flexible

As markets shift more rapidly, static monthly forecasts are becoming less effective. AI agents help FP&A teams update forecasts more frequently and efficiently by automating data pulls, applying predefined logic, and generating insights in less time. According to Deloitte’s 2023 CFO Signals Survey, 42% of CFOs are already investing in AI to improve forecasting accuracy and agility. And half of companies using AI for forecasting have seen a 20% decrease in their overall forecast error.

Business Teams Won’t Always Have To Wait on Finance for Routine Answers

AI agents can help finance professionals instantly retrieve responses to questions like “What’s our Q1 spend vs. budget?” without digging through multiple reports. At Kuali, for instance, the finance team uses Vena Copilot to quickly answer stakeholder questions by inputting them directly into the tool, saving time and eliminating the need to open multiple files. This speeds up their workflow and allows Finance to focus on more strategic priorities.

Finance Becomes a True Strategic Partner

As AI agents take over the manual, time-consuming work, finance teams can shift their focus to analyzing results, advising stakeholders and shaping decisions that drive business impact. Rishi explains the shift this way on The CFO Show:

“I see three core areas where finance teams are getting the biggest benefits from AI: productivity, accuracy, and insights for innovation. In some cases, the very first forecast generated by an AI model has been up to 94% accurate, allowing teams to shift from building models from scratch to reviewing and analyzing outputs instead. That’s a huge gain in efficiency and frees them up for more strategic work.”

For future-ready FP&A teams, AI agents will be less about whether to use them and more about how strategically they’re deployed.

For a comprehensive overview of AI's role in financial planning and analysis, consider reading our article, The Definitive Guide to AI in FP&A, which delves into the benefits, use cases, and potential risks associated with AI adoption in finance.

Invest in AI Agents to Future-Proof Your FP&A Function

AI agents represent a fundamental shift in how finance teams operate. The opportunity now is to move beyond manual, reactive work and toward a more strategic, high-impact role in the business.

To get started, identify one high-effort, low-risk workflow, like forecast updates or variance commentary, and pilot an AI agent in that area. This doesn’t mean starting from scratch. Tools like Vena Copilot bring AI agent capabilities directly into the platforms you already use, including Excel, so your team can automate data pulling, model updates and reporting without leaving familiar environments.

Involve IT and stakeholders early, test for accuracy, and measure time saved. Then, use those insights to expand adoption and build confidence across the organization gradually.

The FP&A teams that make moves towards implementing agentic AI into their operations now won’t just improve efficiency—they’ll deliver faster insights, become more agile, and elevate their role as strategic partners.