79% of FP&A Teams are Using AI to Enhance Operations

Seventy- nine percent of Financial Planning and Analysis (FP&A) teams are using Artificial Intelligence (AI), but most are applying it to enhance operations rather than drive strategic decision-making. Today, FP&A teams are using AI primarily to automate reporting, improve data quality, streamline Excel workflows, and generate financial insights faster. While these use cases deliver clear efficiency gains, they remain focused on operational improvements rather than higher-value activities.

This highlights a growing gap that AI is helping finance teams move faster, but not yet fundamentally transforming how decisions are made

FP&A Teams Are Using AI to Enhance Operations

A recent industry report shows that 79% of FP&A teams are using AI, yet most use cases focus on efficiency rather than strategy.

In practice, this means:

• Automating Excel processes

• Cleaning and formatting financial data

• Generating basic reports

• Assisting with commentary writing

These are valuable improvements. They reduce manual work and help teams close faster. But they don’t fundamentally change how finance operates. As highlighted in the report, AI is helping teams move faster, but not necessarily smarter.

Where FP&A Teams’ AI Usage Delivers Immediate Value

Most FP&A teams’ AI usage today is focused on practical, operational wins, the kinds of tasks that are easy to implement and deliver immediate time savings. In fact, current adoption is still largely centered on efficiency rather than strategy, with common use cases like reporting, spreadsheet automation, and analysis leading the way.

Here’s where AI is already making a measurable impact:

1. Automating Reporting and Commentary

One of the most widely adopted use cases is generating financial narratives. Instead of starting from a blank page, teams can instantly produce:

• Draft variance explanations.

• Executive summaries.

• Monthly close commentary.

This aligns with real-world data showing that 70% of report writing and commentary generation are among the top AI use cases in FP&A, resulting in less time writing and more time analyzing.

2. Enhancing Excel and Spreadsheet Workflows

Spreadsheets are still at the center of finance, and AI is making them faster and easier to manage.

Teams are using AI to:

• Build and troubleshoot formulas.

• Clean and structure datasets.

• Automate reconciliations.

These are some of the fastest ways to start using AI to enhance business operations, especially for teams heavily reliant on Excel.

3. Conversational Data Access

Instead of digging through reports, teams can now query their data directly, fundamentally changing how finance interacts with information. With tools like ChatGPT and Copilot, finance professionals can simply ask questions like “What drove the variance this month?” or “How did revenue compare to forecast?” and receive immediate, contextual answers, no manual filtering required. This shift toward conversational data access reflects how self-service financial insights are becoming a core AI use case in finance.

4. Data Cleanup and Structuring

Before finance teams can generate insights, they need reliable data, and that’s where AI is already delivering clear value.

Today, AI is widely used to clean, organize, and standardize financial data at scale. Instead of manually fixing inconsistencies, teams can rely on AI to:

• Tag and classify transactions.

• Categorize expenses consistently.

• Detect anomalies and outliers.

This matters because most AI use cases in FP&A start with data analysis. If the underlying data is inconsistent or incomplete, the output will be too. By improving data quality upfront, teams create a stronger foundation for more advanced use cases like forecasting, scenario modeling, and decision support.

Why AI-Enhanced Business Operations Aren’t Yet Strategic

Despite strong adoption, most AI use in finance remains concentrated on operational tasks rather than on high-value, strategic applications. Current usage tends to prioritize quick wins like reporting, spreadsheet automation, and basic analysis. These are areas that are easier to implement and deliver immediate efficiency gains. Most teams are using AI to move faster, not necessarily to make better decisions, with only a small portion applying it to more advanced use cases.

That gap becomes clear when you look at where AI is not being widely used yet:

• Scenario modeling

• Forecast optimization

• Strategic planning

• Cross-functional decision support

These are the areas where AI can have the greatest impact, but they require stronger data foundations, better integration, and greater trust in AI outputs. Until those pieces are in place, most organizations remain stuck in the early stage of AI maturity, focused on efficiency rather than intelligence. The result is a clear disconnect. AI is improving workflows, but it’s not yet driving the decisions that define modern FP&A.

How to Use AI for FP&A Beyond Operations

To move from operational gains to real strategic impact, FP&A teams need to rethink how they apply AI across their workflows. Today, most use cases focus on speed and efficiency, but the next phase is about using AI to drive better decisions, not just faster processes. As highlighted in the Drivetrain report, the real opportunity lies in shifting from tactical automation to insight generation and decision support, where AI can influence planning, forecasting, and business strategy.

Here’s how to use AI for FP&A to enhance operations and go beyond them:

1. Apply AI to Scenario Planning

Instead of relying on static forecasts, AI enables FP&A teams to simulate multiple scenarios in real time. This allows finance to move from reactive adjustments to proactive planning, especially in volatile environments. With AI, teams can:

• Run multiple scenarios instantly.

• Adjust assumptions dynamically.

• Highlight risks and opportunities.

This may result in faster, more flexible planning that adapts as conditions change.

2. Automate Variance Analysis

Variance analysis is no longer just about identifying what changed. It now includes understanding why it changed. AI can connect financial results with historical trends and underlying business drivers, which helps teams uncover root causes more efficiently. Instead of manual investigation, AI can:

• Link financial results with historical trends.

• Identify key business drivers behind changes.

• Surface explanations automatically.

This shifts FP&A from reporting numbers to explaining performance.

3. Integrate External Data Signals

One of AI’s biggest advantages is its ability to incorporate external data into financial analysis. Traditional FP&A relies heavily on internal data, but AI expands that view by analyzing market conditions, news, and industry benchmarks. With this broader context, decisions become more informed and forward-looking.

This gives finance teams a more complete picture by enabling them to:

• Analyze market trends.

• Monitor news sentiment.

• Compare against industry benchmarks.

4. Enable Cross-Functional Decision Support

AI also allows FP&A to move beyond siloed reporting and become a true business partner. By connecting financial data with operational metrics, teams can provide insights that influence company-wide decisions. AI makes it easier to:

• Link finance data with sales performance.

• Analyze marketing spend effectiveness.

• Connect operational metrics to financial outcomes.

AI-Enhanced Business Operations Require New Skills

As AI adoption grows, the role of finance professionals is evolving beyond traditional modeling and reporting. Teams are no longer expected to produce numbers but to also explain them, contextualize them, and influence decisions. This shift is reflected in the data, where 87% of finance professionals identify data storytelling and communication as critical skills for the future of FP&A. In practice, this means finance is becoming more cross-functional, requiring professionals to translate insights into clear, actionable narratives.

At the same time, the structure of finance teams is beginning to change as new roles and expectations emerge. AI is not replacing jobs, but it is reshaping them—pushing teams toward more hybrid, tech-enabled capabilities. As a result, finance professionals are now expected to combine financial expertise with a working understanding of AI tools and systems. This shift is driving demand for:

• AI analysts and technical finance roles.

• Greater familiarity with AI tools and workflows.

• Hybrid skill sets that blend finance, data, and technology.

However, there’s a growing gap between what’s required and what teams are actually doing to prepare. While interest in AI is high, consistent investment in upskilling remains limited. The report shows that most professionals are still only lightly engaging with AI learning, which slows down adoption at a deeper, strategic level. Specifically:

• 68% spent five hours or less on AI upskilling

• 15% spent no time at all

This creates a clear capability gap: finance teams recognize the importance of AI, but many are not yet building the skills needed to fully leverage it.

Governance Matters in AI Adoption

Another critical issue in AI adoption is governance, and it’s one that many finance teams are still working through. As AI becomes more embedded in financial workflows, the risks around data, compliance, and decision-making increase significantly. According to the IBM study, executives consistently cite concerns around data lineage, security, privacy, and regulatory constraints when implementing AI in finance.

These concerns highlight a key reality: adopting AI without proper governance can introduce as much risk as it does opportunity. Without clear controls and structured frameworks, organizations face several critical risks:

• Data security and privacy vulnerabilities.

• Lack of transparency in AI-generated outputs.

• Regulatory and compliance challenges.

• Misuse or overreliance on AI-driven insights.

More importantly, governance should enable AI to scale safely across the organization. Reports emphasize that successful AI adoption depends on strong data governance, clear accountability, and ethical frameworks that guide the development and use of AI systems.

To fully realize the value of AI-enhanced business operations, governance must evolve alongside adoption to ensure that speed and innovation don’t come at the cost of trust and control.