9 AI Adoption Best Practices for CFOs

In 2025, we asked finance leaders which innovations they believe will transform FP&A over the next five years. CFOs consistently pointed to three priorities:

• Modern, user-friendly financial tools with embedded AI.
• Native AI capabilities that save time, enhance control, and automate workflows.
• Reliable data and modeling that increase confidence in the numbers.

Yet despite this clear demand, AI adoption in finance remains uneven. Among those surveyed, only about half (52%) say they are already using AI-driven tools for FP&A.

So what’s holding teams back?

For many CFOs, the value of AI is not in question. The challenge lies in knowing where to start and how to apply AI without disrupting existing systems.

If AI is still a developing area for your finance organization, here are some other important terms to know:

• Artificial intelligence (AI): A broad category of technologies designed to perform tasks that typically require human judgment, such as recognizing patterns, learning from data, and supporting decisions. In finance, AI is used to improve forecasting, automate analysis, and surface insights across large and complex data sets.
• Machine learning (ML): A subset of AI that enables systems to learn from historical data and improve outcomes over time without being explicitly programmed. In finance, machine learning powers forecasting models, anomaly detection, and pattern recognition that help teams anticipate trends and identify risks earlier.
• Generative AI: AI capable of creating new content, such as text, explanations, or summaries, based on existing information. In FP&A, generative AI is used to translate financial results into clear narratives, explain variances, and produce executive-ready commentary.
• Agentic AI (or agentic assistants): AI designed to take action within defined workflows and guardrails rather than responding only to direct prompts. In finance, agentic AI supports teams by proactively identifying issues, guiding analysis, and suggesting next steps while keeping humans in control.
• Natural language processing (NLP): Technology that enables systems to understand and respond to human language in a natural way. In finance, NLP allows users to ask questions about performance, forecasts, or variances and receive contextual, easy-to-understand answers. Allows virtual assistants and chatbots — such as Alexa and Siri — to understand human speech, including contextualized sentences and requests.
• Robotic process automation (RPA): Technology that automates repetitive, rules-based tasks by following predefined instructions. In finance, RPA is commonly used for activities like data entry, reconciliations, and file handling, often serving as a foundation alongside more advanced AI capabilities.
• Explainable AI (XAI): AI designed to make its outputs transparent and understandable to users. In finance, explainability is essential for trust, auditability, and compliance, as it shows not only what outcome was produced, but why it was produced.

These concepts provide helpful context for how AI shows up in finance discussions.

Below are 9 practical AI adoption best practices focused on applying AI in ways that improve efficiency, accuracy, and decision-making.

AI is a powerful tool to reduce your team’s time spent on routine tasks, like reporting and data reconciliation. It’s a simple way companies can reduce overhead and gain back significant capacity across the organization.

By automating repetitive, lower-value tasks, you can reduce the risk of burnout and sustain performance during peak reporting periods.

This allows teams to focus on the higher-value work, including complex analysis, cross-functional collaboration, and supporting strategic decision-making.

When AI takes repetitive work off your team’s plate, it creates space for the work that actually moves the business forward.

Instead of managing spreadsheet versions and manual variance analysis, your team can focus on interpreting results, partnering with the business, and supporting stronger strategic decisions.

For construction company Orion Group Holdings, adopting Planful AI removed the need for endless spreadsheet versioning and empowered the team to execute complex, project-level variance analysis more efficiently than before.

“Planful AI analyzes the trends and identifies the areas where you need human brains to focus,” said Barrett Gilley, VP of Finance at Orion. “We can respond to what’s happening in the business very quickly.”

Fully manual processes introduce risk, especially when your finance team is working under tight deadlines and across large, complex data sets. That’s the exact moment small errors with big consequences slip through, creating downstream issues that are time-consuming to identify and resolve.

AI-powered tools like Planful AI Signals help finance teams detect anomalies, inconsistencies, and potential errors at a granular level. Instead of reviewing every data point, teams can focus their attention where it matters most, improving accuracy while maintaining control.

Finance departments of all sizes are managing ever-increasing volumes of data, and the deadlines to report are getting tighter as well. Business leaders also expect updated scenarios, budgets, and forecasts to be delivered on a far more frequent basis. Companies can either add headcount to meet these demands or use AI-enabled technology to scale planning and analysis more efficiently.

With AI applied to cloud-based financial data, Finance teams can use what-if scenario modeling to analyze scenarios based on different assumptions.

If data is the fuel that powers AI, it’s crucial to have a reliable and consistent supply of that fuel.

Cloud-based finance environments make it easier to centralize data, scale analysis, and apply AI across planning, forecasting, and reporting without increasing manual effort

Moving your data to the cloud involves three major action items:

• Hold discussions with your counterparts in IT to ensure your infrastructure is up-to-date and can handle secure, scalable data flows across finance systems.
• Evaluate which processes within the finance department are ready for automation. Common functions ready for this transition include elements of accounting, accounts payable and receivable, and payroll-related activities.
• Identify where AI is already in action across the business and apply those learnings to finance, rather than starting from scratch.

Expensive pilot projects often require multiple rounds of internal buy-in and can take months to demonstrate value. You’re better off running short, focused comparisons using historical forecasts and letting the data speak for itself.

For example, you can take a previous manual forecast and compare it with an AI-enhanced version. You’ll immediately see where the algorithm picked up on the same red flags as the original analysis and where it surfaces issues that may have been missed in the manual forecast.

With a solution like Planful AI Projections, CFOs can build intelligent, adaptive models that improve accuracy over time. Running side-by-side comparisons helps your team quantify gains and build internal trust in the process.

AI adoption often stalls after implementation, not because the technology falls short, but because it doesn’t align with how finance teams actually operate day to day. Generic AI tools may offer insights, but they often fall short when it comes to financial context, governance requirements, and explainability.

For CFOs, the priority should be selecting AI that fits naturally into existing workflows and gives teams time to build familiarity and confidence. Finance-specific AI understands financial structures, time periods, hierarchies, and controls, which reduces friction and helps teams build confidence in the outputs without having to change how they work overnight. Tools that require finance to change how they think or work overnight create friction and slow adoption.

This is where in-app guidance, like Planful’s Help Assistant, plays an important role.

Help Assistant is embedded natively into the Planful platform to provide AI-powered support for users. It’s able to give teams access to answers and step-by-step guidance directly within the tool as questions arise.

Instead of relying on formal training or external documentation, users can learn in context, at their own pace.

AI adoption introduces change to established finance workflows, which makes clarity essential. Without clear expectations, teams may hesitate to engage fully or default back to familiar processes.

CFOs play an important role in defining how AI fits into the finance operating model. This includes setting clear expectations on exactly where AI is intended to support analysis, where human judgment remains critical, and how success will be measured. When teams understand both the purpose and boundaries of AI use, adoption is more consistent and effective.

Creating space for feedback also matters. Teams closest to the work are often best equipped to identify where AI can meaningfully reduce effort or improve outcomes. Incorporating that input helps ensure adoption is practical, scalable, and not just theoretical.

As AI becomes embedded across finance processes, oversight becomes a requirement, not an afterthought. Clear governance ensures AI is applied consistently, responsibly, and in alignment with internal controls, external regulatory requirements, and enterprise risk frameworks.

For CFOs, this means clearly defining ownership, setting guardrails for AI use, and aligning AI initiatives with broader risk, compliance, and reporting frameworks. In larger organizations, this responsibility may sit with a cross-functional oversight group that shares learnings and maintains visibility at the executive or board level, and ensures consistent application across the business.

Strong governance reinforces trust, both within the finance team and across the business, while providing a stable foundation for responsible adoption as AI use expands over time.

AI, data science, and machine learning will continue to reshape how finance teams operate. According to a 2025 World Economic Forum study, financial services firms spent $35 billion on AI in 2023 and are expected to reach $97 billion by 2027.

As your organization adopts AI and automates more processes, the most meaningful impact will come from the capacity it unlocks. By reducing manual effort and accelerating insights, AI opens the door to higher-value work and a more strategic role for the CFO.

And the best part? You don’t need a technical background or a massive enterprise budget to take advantage of modern AI.

Planful AI is purpose-built for Finance to deliver answers you can trust, instantly.

• AI in finance transforms planning, forecasting, reporting, and close with faster insights and fewer manual steps.
• Planful AI empowers CFOs to lead with meaningful data, automate repetitive work, and focus on strategic outcomes.
• Finance teams using AI-driven insights can forecast confidently, detect anomalies instantly, and close the books with greater speed and trust.

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CFOs who adopt AI now will shape the new era of financial performance.

Get started with Planful AI today.

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How is AI changing the role of Finance?

AI is shifting Finance from manual data collection to higher-value decision support. By automating reconciliations, forecasts, and anomaly detection, AI lets CFOs finance teams spend less time gathering data and more time focusing on strategy, analysis, scenario modeling, and driving business performance.

What are the most common uses of AI in finance today?

The most common applications of AI in finance focus on reducing manual effort while improving confidence in results. These include forecasting accuracy, anomaly detection, variance analysis, financial close automation, and generating clear explanations and narratives from financial data. Finance-specific AI tools like Planful AI integrate these capabilities directly into financial workflows, improving speed and trust in every cycle

How can CFOs start implementing AI in their finance functions?

CFOs can begin by focusing on practical, low-risk use cases such as automating high-volume tasks and validating AI’s impact against prior results. From there, adopting explainable, finance-specific AI platforms such as Planful AI that embed intelligence into planning, forecasting, and close workflows without disrupting existing processes.