How FP&A Teams Are Really Using AI in 2026

AI in Finance Has Gone From Buzzword to Business Tool

AI in Finance Has Gone From Buzzword to Business Tool

A few years ago, if someone brought up AI in a finance meeting, it usually felt like a conversation about the future. Something interesting, but not quite real. Now in 2026, that has changed. AI is showing up in everyday workflows inside FP&A teams. Not in flashy ways, but in the practical places where it saves time, reduces manual effort, and helps people focus on the work that matters.

Forecasting Starts With a Smarter Baseline Using Predictive Analytics

One of the most immediate shifts has been in how teams approach revenue forecasting. Instead of starting every cycle from scratch, AI models now generate a baseline forecast using historicals, seasonality, and external indicators. By leveraging predictive modeling, organizations can enhance forecasting accuracy and scenario analysis, using advanced analytics to identify risks and improve strategic decision-making. Finance still brings judgment and adjustments, but the heavy lifting on data prep and pattern recognition happens automatically. This means more time spent on insights, less time spent recreating the wheel.

Scenario planning has become more dynamic too. Rather than manually creating every what-if model, analysts can now adjust assumptions—like missed bookings or hiring delays—and instantly see how those changes flow through the forecast. It’s not about eliminating planning work. It’s about speeding up the time between question and answer.

Variance Analysis for Finance Teams Is No Longer a Blank Page

Every FP&A team spends hours each month writing variance commentary. What AI offers here is a first draft. The system compares actuals to plan and generates a starting point for the narrative. For example, it might note that revenue missed expectations due to lower-than-expected expansion in enterprise accounts.

This saves time, but more importantly, it helps teams shift from formatting to analysis. Finance still controls the message. AI just takes care of the grunt work.

Reporting Is Becoming More Interactive With Generative AI

Some of the biggest gains come not from full automation, but from how AI is changing access to information. Business leaders are starting to ask questions like, “Which departments are over budget this month?” and get answers directly, without relying on ad hoc reports from finance.

AI-enabled natural language queries are making financial data more self-serve, which gives FP&A more time to focus on guidance instead of just delivery.

Board and leadership reporting is also getting more efficient. AI can translate dashboards into slide outlines or summary text, giving finance teams a strong draft to refine for the next review or update. AI-enabled reporting also helps finance teams track key metrics such as time savings, forecasting accuracy, and decision-making speed, making it easier to monitor ROI and evaluate the impact of AI tools.

Behind the Scenes, Financial Data Quality Is Improving Too

AI isn’t just helping with outputs. It’s improving the inputs. Systems can now flag unmapped accounts, detect unusual values, or highlight gaps in submissions before close. This reduces late surprises and keeps processes cleaner.

Workflow intelligence is getting smarter as well. If an approval is stuck, or if a department is trending late on forecast submission, AI can flag it and suggest a better routing. These small improvements keep the entire planning cycle on track. Additionally, these AI-driven enhancements contribute to improved risk management by enabling earlier detection of anomalies and reducing the likelihood of errors.

AI Works Best With a Strong Foundation

None of these benefits happen in a vacuum. To make AI effective, teams need a solid FP&A platform that brings structure, governance, and integration. That means actuals, forecasts, and budgets all live in the same environment. Dimensions like entities, cost centers, and time periods are standardized. And data connects cleanly to systems like your ERP, CRM, or HR platform.

Platforms like Kepion make this possible. Because it’s built on Microsoft technologies—SQL Server, Excel, Power BI, and Fabric—it fits naturally into the tools most finance teams already use. There’s no need to rebuild your stack. You’re just getting more value from it.

How to Start Small With AI in FP&A

You don’t need an AI strategy. You need a first step.

Most teams start by identifying one repeatable pain point. That might be drafting revenue forecasts or writing variance commentary. From there, they clean up their data model, run the AI-enhanced process in parallel, and compare results. Time savings, forecast accuracy, and analyst feedback become the metrics that guide the next move.

This kind of focused pilot lets you test the value of AI without disrupting the rest of your process. And it gives you the internal momentum to expand with confidence.

Overcoming Challenges and Barriers to AI Adoption

While the benefits of artificial intelligence in finance are clear, the path to successful AI adoption is not always straightforward. Many finance professionals face a steep learning curve when it comes to understanding AI technologies like machine learning, generative AI, and predictive analytics. This knowledge gap can make it difficult for finance teams to select the right AI tools and integrate them effectively into their existing financial workflows.

Another significant challenge is managing the vast amounts of financial data required to power AI systems. Financial institutions must ensure that both structured and unstructured data are accurate, consistent, and accessible. High-quality data is essential for training AI models that deliver actionable insights, whether for financial analysis, fraud detection, or automating repetitive tasks. The complexity increases when integrating AI-powered tools with legacy systems, often requiring careful planning and resource allocation.

Data privacy and security are also top concerns in the financial sector. With sensitive customer information at stake, financial institutions must implement robust data privacy measures and maintain clear audit trails to ensure compliance and protect against breaches.

To overcome these barriers, finance teams can start by investing in user-friendly AI powered tools and providing targeted training to build confidence and expertise. Establishing clear protocols for the use of AI in finance tasks—such as financial analysis, fraud detection, and risk modeling—helps ensure consistency and governance. Leveraging AI agents to automate routine and repetitive tasks can free up finance professionals to focus on more complex tasks that require human intelligence and judgment. By taking a step-by-step approach, financial institutions can gradually build the foundation needed to fully leverage AI technologies and drive meaningful change in the financial services industry.

The Future of AI in Finance

Looking ahead, the future of AI in finance is set to transform the financial services industry in profound ways. Advanced AI tools are rapidly evolving, enabling finance teams to analyze vast amounts of historical data and unstructured data to identify patterns, predict future trends, and generate insights that drive better financial decision making. These capabilities will empower financial institutions to make more data driven decisions, improve strategic planning, and stay ahead of market trends.

AI powered automation is expected to streamline financial workflows, from data collection and data consolidation to financial modeling and scenario modeling. By automating repetitive tasks, finance professionals can shift their focus to higher value activities such as investment research, strategic planning, and resource allocation. This not only boosts productivity but also enhances forecasting accuracy and risk management, helping organizations identify opportunities and mitigate compliance risk more effectively.

Personalization will also become a hallmark of AI adoption in financial services. With advanced algorithms and machine learning, financial institutions will be able to offer tailored investment strategies, personalized financial planning, and more responsive customer interactions. AI powered systems will help asset managers and investment banking teams deliver improved service delivery and identify anomalies or fraud faster than ever before.

Cost savings and operational efficiency are additional benefits on the horizon. As AI solutions become more embedded in finance functions, organizations will be able to optimize resource allocation, reduce manual processes, and align AI initiatives with broader business objectives. The adoption of emerging technologies like generative AI and advanced analytics will further enhance the ability to generate actionable insights and support more complex scenario modeling.

Ultimately, the future of AI in finance is about empowering finance teams to leverage AI technologies for smarter, faster, and more strategic decisions. By embracing AI powered tools and developing a clear strategy for AI adoption, financial institutions can unlock new opportunities, improve risk management, and remain competitive in an ever-evolving financial landscape.

Where Kepion Fits in Your AI Journey

Kepion gives FP&A teams the structure they need to layer AI into their planning environment. From centralized models to robust workflow tools, it creates a controlled foundation for intelligent forecasting, scenario planning, and reporting.

Because it integrates with Microsoft tools like Excel and Power BI, finance teams don’t need to change how they work. They just gain more flexibility, control, and insight—plus the ability to take advantage of new AI capabilities as they become available.

If you’re curious about how AI could support your specific process, a Kepion demo can show you exactly how it fits your model, your team, and your timeline.