AI in FP&A: How companies are leveraging AI for finance teams

A rundown of what the best AI for finance teams can do, from automation to analytics

It's on the news. It's taking over your LinkedIn feed. You just read a clickbait article on the robo-pocalypse.

AI is here to stay, and it’s changing the way we approach business intelligence. From interpreting historical data to enabling forecasting and scenario modeling, financial planning and analysis (FP&A) professionals are using AI to become even more valuable and strategic collaborators.

According to a recent FP&A Trends Survey, only 35% of FP&A professionals' time is spent on high-value, business-level activities, with the rest drained into processes that could be fully automated – like manual data collection and validation. This is where AI finance tools are making the biggest impact: automating routine tasks so FP&A teams can focus on shaping strategic decision-making. 

The emergence of sophisticated AI technologies, from machine learning algorithms to specialized agents, is creating new possibilities for automation, analysis, and modeling that were unimaginable just a few years ago. For finance leaders navigating this shift, the question is no longer whether to adopt AI, but how to implement it effectively. 

This guide explores how modern finance teams are leveraging AI to transform their FP&A processes. We’ll explain the benefits of AI in finance, which tools and approaches are delivering the most value for FP&A professionals, and how you can integrate AI into your own financial planning workflows.

What is AI, and why is it important to the future of business planning?

AI, or artificial intelligence, is the simulation of human decision-making by computers. It encompasses everything from basic automation to sophisticated machine learning algorithms and large language models (LLMs).

Unlike traditional programs, AI adapts its behavior based on new information, finds patterns in data sets, and makes predictions using these patterns. Where traditional tools show you past performance, AI systems can spot trends, suggest likely outcomes, and adjust their analyses as circumstances change.

This shift from hindsight to foresight is transforming how businesses approach financial decision-making. Today's AI-enhanced systems can:

• Process millions of data points in seconds, surfacing opportunities that human analysts might miss
• Improve visibility into business performance by connecting disparate data sources
• Conversationally interpret historical data and offer forward-looking recommendations
• Learn and refine their analyses, becoming more accurate over time

For finance teams, this means less time manually analyzing past results or market trends, and more time planning ahead.

How do different teams in finance leverage AI?

Teams within finance departments are tapping into AI in distinct, high-impact ways. But whether you're in FP&A or investor relations, AI tools can help streamline planning, enhance forecasting, and drive more strategic decisions. 

Here’s how different finance teams are putting AI to work today:

Financial planning & analysis (FP&A)

FP&A teams use AI to generate more accurate forecasts by analyzing historical performance, pipeline data, and external market indicators. With AI, they can simulate different growth scenarios and financial outcomes in minutes rather than days.

Let’s consider a scenario where FP&A can leverage AI. Imagine an FP&A leader is preparing a board update. Instead of manually slicing data across three regions, they ask AI to generate best- and worst-case revenue projections, adjusted for shifting currency rates and seasonality. The AI flags an unexpected dip in France tied to a product delay – something they would have otherwise missed – resulting in more accurate information.

Ready to transform your FP&A process with AI? Download the full Office of the CFO 2025 report and discover how leading finance teams are driving smarter, faster decisions.

Supply chain 

Supply chain teams use AI to model the cost and risk of sourcing, logistics, and inventory decisions. They can simulate disruptions, assess cost-to-serve by region, and optimize working capital.

For example, during a supplier delay, a supply chain analyst might run a scenario comparing local alternatives. The AI highlights not just cost differences, but how lead times will impact Q2 revenue, enabling the team to choose the best option for both margin and delivery reliability.

Investor relations

Investor relations teams use AI to automate reporting, model earnings guidance, and benchmark performance against peers. With faster access to insights, they can communicate more strategically and respond to market developments in real time.

If an IR team has an earnings call coming up, they might use AI to test different revenue guidance scenarios. AI can help compare historical reactions from investor segments and flags which positioning is most likely to maintain confidence, giving leadership actionable insight on what to communicate.

Financial control & compliance

Controllers and compliance teams use AI for automated variance analysis, real-time close tracking, and audit preparation. AI surfaces anomalies faster and helps teams stay ahead of regulatory obligations.

For example, AI can help flag a spike in discretionary spending from one cost center. This anomaly detection prompts the controller to conduct a proactive, early investigation weeks before that line item would’ve triggered concern in the formal review. This also avoids a larger issue during the quarterly audit.

Revenue operations (RevOps)

RevOps teams use AI to unify data across finance, marketing, and sales. With intelligent agents, they can run capacity planning, optimize GTM alignment, and continuously update forecasts.

For example, a RevOps leader looking to assess whether to accelerate hiring in EMEA might use AI to analyze headcount, marketing pipeline velocity, rep ramp times, and current revenue goals. They could also use AI to put together a hiring plan that’s aligned to actual deal momentum, not guesswork.

Revenue growth management (RGM)

RGM teams turn to AI to optimize pricing, promotions, and product mix decisions. With elasticity modeling and channel profitability analysis, they can identify revenue levers without compromising margin.

Ahead of a new product launch, an RGM team might ask AI to simulate how different price points will perform across channels. The AI could highlight an ideal price for DTC that’s higher than expected but backed by strong historical conversion data.

Treasury and cash management

Treasury teams use AI to forecast liquidity, monitor FX exposure, and model short-term investment strategies. Agents can help teams optimize capital allocation while managing risk.

When a major client misses a payment, AI can help revise cash flow projections. By simulating different working capital levers – like delaying a supplier invoice or tapping a short-term credit facility – AI works in tandem with treasury teams to cover the gap.

Corporate development

Corporate development teams use AI to run deal modeling, track integration progress, and monitor investment theses. AI-powered finance tools can assess synergies, test what-if scenarios, and analyze post-deal performance.

During early diligence, a corp dev team might use an AI agent to model multiple acquisition scenarios. The agent could identify which structure yields the fastest ROI and flags integration risks based on past benchmarks – all before external consultants weigh in.

What are the top applications of AI in FP&A?

Whether it’s modeling business scenarios or translating insights for leadership, FP&A teams are well-equipped to turn AI outputs into action. Because FP&A sits at the intersection of financial data, operational planning, and executive decision-making, these teams are primed to benefit from AI in finance.

Here are some of the top applications of AI in FP&A:

Financial forecasting

Traditional forecasting processes often leave FP&A teams stuck in cycles of manual updates. With AI, teams can shift from periodic revisions to continuous planning that adapts to business changes. Machine learning models analyze historical trends, market signals, and operational data to automatically refresh forecasts. This means FP&A professionals can focus on evaluating scenarios and providing strategic guidance rather than maintaining Excel spreadsheets.

Financial modeling and analysis

Building and maintaining complex financial models traditionally requires significant manual effort. AI can validate assumptions, identify potential errors, and suggest model improvements based on historical accuracy. When FP&A teams can trust their models' integrity, they can focus on interpreting results and providing strategic guidance to leadership.

Real-time performance monitoring

Month-end variance analysis typically involves hours of investigating discrepancies across departments and accounts. AI can identify significant variances as they occur, analyze contributing factors, and suggest probable causes. When FP&A teams can spot and address issues in real time, they can help business partners course-correct before variances impact quarterly results.

Strategic scenario planning

Building multiple scenarios manually can consume weeks of FP&A time. AI accelerates this process by automatically generating and testing various scenarios based on different assumptions and market conditions. Teams can quickly model the impact of changing variables – from hiring plans to pricing strategies – and provide leadership with data-backed recommendations for different business conditions.

Want to dive deeper into scenario planning? Explore our step-by-step Scenario Planning Guide and learn how to build resilient strategies that prepare your business for any outcome.

Board and executive reporting

AI transforms financial reporting by automatically generating clear performance narratives and powering real-time dashboards that surface key trends. Instead of spending days gathering data and writing summaries, FP&A teams can use AI to analyze performance, identify storylines, and create initial report drafts, freeing them to refine insights and recommendations. AI can also help FP&A teams prepare for board meetings by fielding questions with precision and depth.

Expense management

AI transforms expense management for FP&A teams by automatically categorizing transactions. This technology can streamline accounts payable (AP) processes and automate spend analysis to uncover new cost-saving opportunities. For example, it can accelerate month-end close times by automatically validating data, and improve search by making documents more accessible and available.

Budget automation and control

Budget processes often trap FP&A teams in administrative tasks. AI streamlines these workflows by automating approvals based on predefined rules and flagging exceptions that need human review. This allows FP&A teams to focus on strategic budget planning while maintaining appropriate controls.

Resource planning and allocation

Traditional resource planning relies heavily on historical patterns and manual adjustments. AI can analyze multiple data points – from utilization rates to market conditions – to suggest ways to optimize resource allocation. FP&A teams can model different scenarios instantly to better understand the impact of shifting resources between departments or projects. This helps organizations maximize ROI while maintaining operational efficiency.

Cross-functional communication

Financial planning and analysis requires the collaboration of multiple stakeholders, including operation teams. But FP&A teams can struggle to translate financial insights for non-finance stakeholders. AI helps bridge this gap by generating department-specific reports and answering common questions about financial implications. This enables FP&A to spend more time on strategic partnership and less time explaining basic concepts.

What finance functions does AI not replace (yet)?

AI can help finance teams achieve everything they were already achieving as finance professionals, but faster and more effectively. In reality, it doesn’t matter whether you’re an analyst, a finance manager, or a CFO – AI still needs a human to determine its course. 

Here are some finance functions that AI cannot perform effectively (as of this moment):

• Relationship management: AI can assist with data analysis and customer interactions, but it can't replace the personal touch of a human being who understands a client or organization’s unique financial needs, historical context, and goals.
• Strategic decision-making: While you can use AI to eliminate low-impact data processing tasks and uncover important insights, the true value of a human finance professional lies in intuition, prioritization, and strategic thinking.
• Creativity and innovation: While generative AI (GenAI) is known for being a creative tool, its outputs and ideas are still just a statistical average. AI can support creative thinking, but its outputs and ideas are nowhere as original and out-of-the-box as those belonging to human beings.
• Regulatory compliance: AI certainly helps in areas like fraud detection, but it can’t currently enforce actual compliance with regulations and laws.
• Ethical considerations: Human care and moral judgment are still needed when sensitive financial decisions are being made.

Which challenges slow adoption of AI in FP&A?

AI has seen fast adoption by finance teams in the past few years, especially with generative AI’s rise in popularity. But it hasn’t been without its hurdles. Here are some of the main detractors preventing finance teams from fully adopting AI in FP&A:

• Data privacy and security: AI technology providers have different approaches to data privacy and security. These differences can feel overwhelming for decision-makers to understand and navigate, resulting in slower adoption and roll-out. 
• Ethical considerations: As AI becomes more advanced, ethical concerns over AI will continue to hinder adoption. Questions about fairness and bias in automated decision-making make many finance leaders hesitant to fully commit to AI solutions.
• Loss of creativity and critical thinking: As AI automates routine tasks and decision-making, there's a risk of losing the ability to apply critical thinking and creative problem-solving to complex challenges.
• Fear of technology or market failure: Those who remember the dot-com bubble burst in 2000 may be hesitant to fully embrace AI technology. They worry that current AI valuations and enthusiasm could lead to another tech market crash, making them cautious about significant investments in AI solutions.
• Over- or under-commitment to AI: FP&A teams may try to implement too many AI tech tools at once or let fear of failure prevent them from moving on AI at all.
• Integration and training requirements: Finance teams may struggle to integrate AI into their existing systems and workflows, creating friction in adoption. New AI tools also require heavy training, which can be time-consuming and expensive to carry out.

While these challenges may seem daunting, there are practical steps finance teams can take to protect themselves and their organizations when implementing AI solutions.

How to safeguard your finance team against the drawbacks of AI

1. Assess your AI readiness

Before jumping into AI for finance teams, consider whether your organization is ready for AI. Consider factors like resource availability and process maturity. You’ll also want to note where inefficiencies are causing bottlenecks, and how much you stand to gain from implementing AI

2. Know the limitations of AI

Make sure your team knows what AI can and can't do, so they don't rely on it blindly. Think of it as knowing the strengths and weaknesses of a teammate. 

3. Use human oversight

Trust your team's expertise and let them exercise their judgment to prevent any potential negative impacts of AI. Look for FP&A AI solutions that facilitate human review and approval processes, allowing your team to validate AI-generated insights before they're implemented.

4. Establish clear data protocols

To address privacy and security concerns, establish clear protocols for data handling, and ensure your team understands compliance requirements. Regular security audits and parent-child account structures can help maintain data integrity while using AI tools. It’s worth noting that the best FP&A AI solutions will come with built-in security features, compliance certifications, and customizable permission settings to support your governance framework.

5. Take a balanced approach 

Instead of rushing to implement every new AI solution, create a measured rollout plan that allows your team to learn and adapt. Start with one or two high-impact areas and gradually expand based on your success and any lessons learned. Quality FP&A platforms will offer modular implementation options, letting you start with core features and scale up as your team's comfort level and skill sets grow.

6. Invest in continuous learning 

Regular training sessions can help your team stay current with AI capabilities while sharpening their critical thinking and creative problem-solving skills. This dual focus ensures AI enhances rather than replaces human expertise. Leading FP&A AI providers support this through comprehensive training resources, regular feature updates, and dedicated customer success teams that help your team maximize the tool's potential.

Identifying the best AI for finance teams

One sure-fire way to avoid AI setbacks is to determine the best AI solution for your finance team. This means working with an external AI-driven provider, not just using free-form AI tools. 

But how do you know when you’ve found the right provider?

Start with the basics

What does the tool actually do? The best AI solutions for finance teams should handle your essential processes while offering meaningful automation rather than just flashy features. When assessing AI capabilities, look for customization options that let you tailor the AI to your specific workflows, rather than forcing you to adapt to rigid systems.

Look for key integrations

Your new AI solution needs to play nice with your existing tech stack, especially your ERP and accounting systems. 

Check security standards

Your chosen solution should meet the most rigid industry standards like SOC 2 and GDPR, with robust data encryption and careful access controls. After all, you're trusting this tool with sensitive financial data.

Assess support options

Even the most powerful AI tool is only as good as your team's ability to use it. Look for providers offering comprehensive training resources and responsive support teams. The best vendors will assign you a dedicated implementation specialist who understands both the technology and your industry.

Calculate ROI

Making AI work for your FP&A team starts and ends with the numbers. Consider the total cost of ownership and the timeline to positive ROI when vetting AI solutions. 

Evaluate the vendor

Take a hard look at the vendor itself before investing in AI for FP&A. Remember that you want a partner with staying power, not just a clever algorithm.

Ask a potential partner:

• What analyst recognition or industry validation have they received?
• What do their customers say? Can they provide reviews or references?
• Do they understand the unique challenges of finance teams like yours? 

Organize your research

Don’t let these factors overwhelm you. Organize your research by creating a simple scorecard. Evaluate each potential solution, and assign them a rating for each of the areas discussed above. Decide which solution aligns best with your needs, then start with a small pilot program.

Like learning to fly a plane, you'll learn more from hands-on experience than you will from any sales pitch.

Tricks for keeping up with AI in FP&A

FP&A is quickly moving beyond periodic reporting to continuous planning and agile decision-making support. The modern FP&A team is the entire organization’s business partner in making better decisions rooted in strategic finance best practices. AI is adding fuel to that fire.

So, how do you keep up with its potential and cut through all the noise?

Here are some simple strategies to help your FP&A team stay up to speed with changes to FP&A AI:

Attend events and conferences

One way to stay in the loop is by attending industry conferences or workshops that focus on AI in finance. These events can teach your finance team about new opportunities for applying AI in FP&A, and they provide a place to meet other finance leaders who share the same needs and interests as you do.

Watch webinars and listen to podcasts

If you can’t attend an industry event in person, that’s okay. There are plenty of webinars and podcasts available on AI in FP&A for those who’d rather skip the in-person crowds.

Subscribe to newsletters

Another way to learn is by subscribing to newsletters and publications like Lead with AI and FP&A Trends Digest. These can provide valuable information on how AI is informing business and financial planning.

Follow AI experts on social media

Sometimes the best place to find resources on AI for FP&A is on social channels. If you’re an active social media user, then you can also follow experts like OpenAI’s Laura Modiano and Meta’s Yann LeCun on social media. Online groups and community forums can also offer a way to easily engage with like-minded finance professionals who share an interest in AI trends and learnings.

A realistic look at the future of AI for finance teams

We once imagined a world of self-driving cars, fully virtual environments, and intelligent systems that could take over repetitive work – and, today, much of that fantasy is becoming reality, albeit still evolving.

For finance teams, these technological leaps signal a critical turning point. Embracing next-generation AI solutions isn’t just a matter of innovation, it’s a necessity for speed, agility, and smarter decision-making.

At Pigment, we’ve gone beyond just conversational interfaces. Our suite of AI agents will enable finance teams to automate financial analysis, streamline modeling, and forecast with unprecedented speed and accuracy.

Get started with AI for finance teams

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