Artificial intelligence won’t do finance’s job, but it will redefine how the job gets done. Many CFOs anticipate that AI can be the lever that helps them meet, or even leapfrog, stakeholders’ demands to both control costs, yet be more agile. But as usual with any technological advance, the reality of AI’s impact on CFOs and the finance function is more nuanced than either the AI hype or skepticism suggests. AI promises greater speed, accuracy, and foresight, but it also introduces new responsibilities that the CFO must oversee.

Finance leaders recognize that they must act now, even though AI is still evolving, because waiting means falling farther behind as competitors (and their own AI projects) continue learning, compounding their advantages and making leadership gaps harder to close. This guide explores what AI means for CFOs today—how it works, where it’s being used, and what challenges lie ahead as AI becomes a trustworthy part of the CFO’s toolkit.

What Is AI for CFOs?

For CFOs, AI comes down to two main types of technologies that are capable of enhancing accounting and financial operations: analytics and generative AI. Analytical AI spans from automated reporting and predictive analytics to risk detection and scenario planning, while GenAI focuses on content creation and workflow automation. Both are capable of processing financial data at far higher volumes and greater depths than traditional financial systems can achieve. 

Analytical AI, built on machine learning (ML) and advanced statistical models, is embedded directly into financial systems to perform forecasting, classification, matching, and optimization tasks. GenAI and AI agents tackle narrative and interactive aspects of finance work, such as drafting variance explanations, summarizing reconciliation reports, orchestrating multistep workflows, and providing natural-language interfaces to query enterprise performance management (EPM) and ERP data without the need for complex formulas or coding.

Together, the two layers boost productivity through automation and elevate work quality by delivering faster, more accurate insights.

Key Takeaways

  • AI reaches every area of finance, automating routine tasks and workflows, and generating both historical and prospective analyses.
  • CFOs see tangible benefits from faster reporting cycles, richer information for sharper decision-making, cost and cash flow optimization, greater visibility, and stronger fraud detection.
  • AI benefits CFOs personally, unlocking bandwidth to meet the expanding demands of their role and equipping them with better tools for storytelling and leadership.
  • CFOs lead AI adoption through a combination of vision and tactical execution, balancing added responsibilities for governance, data quality, ROI evaluation, and compliance management.
  • Embedded AI delivers the greatest value by integrating automation and analytics directly within ERP and EPM systems for accuracy and control.

AI for the CFO Explained

In finance operations, analytical AI processes data streams from multiple sources to identify trends, classify transactions, calculate probabilities, and predict outcomes. It uses pattern recognition to spot anomalies, such as duplicate invoices, unusual payment patterns, or revenue recognition inconsistencies, at speeds and scale that human reviewers can’t match. Then, GenAI can turn analytical results into clear, actionable insights. For example, if a 15% variance in quarterly expenses appears, GenAI can draft an explanation for the finance team’s review, correlating the variance with operational data and describing what happened and why. It can also respond to queries in plain language; type “What’s driving our cash burn?” and AI will synthesize data from multiple systems to provide a comprehensive answer, often accompanied by supporting visualizations.

The technical architecture typically involves AI models operating either within existing ERP and EPM systems or alongside them, connected by secure APIs or a combination of the two. The ML models continually learn from new activity, improving their accuracy over time. Natural language processing features extend AI’s capabilities to unstructured data, like contracts and emails, adding context to numerical analyses. The larger context provided to AI systems by the continual expansion of all this data over time makes their results more accurate and helps them adapt to company-specific patterns.

Although AI platforms can be bolted onto financial systems, current trends suggest that embedding AI capabilities into the applications that finance teams already use improves adoption, simplifies administration, and speeds ROI. In an embedded approach, AI features appear as natural extensions of existing workflows—team members see AI suggestions in reconciliation screens, predictive insights within planning interfaces, and alerts in approval queues.

According to McKinsey & Company’s “State of AI in 2025” research, organizations that fully redesign workflows around embedded AI are seeing measurable impact on earnings before interest and taxes, though only a minority has achieved this level of transformation. AI now appears in core financial systems, such as EPM, ERP, and accounting, as well as in specialized workflows, including treasury, order-to-cash, accounts payable (AP), accounts receivable (AR), and procure-to-pay.

How Is AI Impacting the CFO Role?

AI is impacting the fast-evolving CFO role in two major ways and several secondary ones, all of which add up to a fundamental rethinking of how CFOs go about their work. First, AI frees finance teams’ time by automating more of their core processes, such as report generation for compliance and account reconciliations during the close, allowing CFOs to dedicate more attention to forward-looking, strategic endeavors. For example, though older systems can automate AR and AP tasks, CFOs and their teams are often frustrated by the number of “edge” cases that fall outside such systems’ commonly rules-based decision-making. AI systems bring more intelligent decision-making that addresses more scenarios without asking for human assistance. This increased automation and the reduction in exceptions that require finance team intervention means better financial information is available any time a CFO wants to see it, rather than after the clean-up of an accounting close—bringing CFOs closer to the goal of continuous closing.

Second, AI can then act as a strategic partner in pursuing those endeavors, such as by identifying and supporting mergers and acquisitions (M&A) efforts or new business opportunities. According to research from the World Economic Forum and Accenture, 70% of financial services executives believe AI will directly tie to revenue growth in the coming years, signaling a shift from viewing AI primarily as a cost-reduction tool to seeing it as a strategic enabler of growth.

As a practical matter, external auditors, including the Big Four accounting firms, are already using AI in their audits. CFOs who aren’t versed in AI may find themselves at a disadvantage when discussing findings or defending their own AI-generated analyses.

AI also imparts new responsibilities to CFOs, who are increasingly involved in conversations about AI governance to see that the tools meet accuracy standards for financial and compliance reporting. CFOs are also being called upon to evaluate AI investments and oversee data quality standards, especially around data privacy policies and for new types of data the finance team may not have used before, such as external social media and weather data intended to refine financial forecasts.

At the professional development level, AI changes the nature of what constitutes financial leadership for CFOs. They need to understand the pros and cons of how AI models generate predictions, when to trust automated insights, and how to blend all that with human judgment and oversight. This changes the core skillset of finance teams.

Benefits of AI Adoption for CFOs

AI is addressing many long-standing finance pain points—from slow monthly closes and manual reporting to inconsistent fraud detection. Although automation is the first objective, the real benefits for CFOs include how AI helps strengthen their financial control, accelerates decision cycles, and levels up their teams’ understanding of business performance.

When embedded directly into core systems, AI turns finance into an ongoing learning engine that can keep pace with business change. It helps enforce compliance, flags anomalies before they become audit issues, and enhances transparency by documenting every step. This alleviates traditional finance department headaches and delivers the foresight CFOs need to guide strategy and protect enterprise value. This all adds up to a more agile finance function that operates faster, smarter, more proactively, and with greater confidence.

CFOs see benefits in five key areas:

  1. Faster reporting: AI automates data aggregation and consolidation, transforming periodic financial reporting cycles that take several days into a continuous, real-time process. In a recent survey, 70% of CFOs said AI helps their teams move faster or deliver more. Automated reconciliations, providing explanations for variances, and exception routing are some of the ways AI shortens close-to-report and forecast cycles.
  2. Enhanced decision-making: Faster reporting gives business leaders access to the most current information for day-to-day decision-making. AI-powered predictive analytics and scenario modeling make testing multiple potential outcomes easier and faster, so CFOs can obtain data-supported evidence before recommending committing resources. Additionally, because AI can analyze multiple large data sets simultaneously, it can reveal hidden correlations between operational metrics and financial performance that manual analysis might miss.
  3. Cost optimization: AI can monitor expense spending and flag outliers and inefficiencies as they occur, allowing for faster corrective action. It can also benchmark costs against historical trends and industry standards to identify cost-saving opportunities, such as vendor consolidation or contract renegotiation.
  4. Increased visibility: ERP systems already track key performance indicators via helpful dashboards. AI tools can further increase financial visibility by multiplying the number of trackable metrics and improving the ability to drill down from consolidated results to transaction-level details. GenAI adds another dimension, drafting explanatory analyses for finance team members’ review and insertion into management reports. This transparency helps CFOs understand not just what happened, but why.
  5. Uncovering more fraud: AI detects fraud by analyzing behavioral patterns across vast numbers of transactions. It can identify duplicate invoices, ghost vendors, and expense anomalies—and can learn from each confirmed case. Furthermore, AI can screen 100% of transactions, rather than random samples. As a result, it can find fraud cases that previously might have slipped through internal control gaps and catch evolving fraud faster—before it becomes material.

Key Uses of AI for CFOs

AI’s potential spans the entire finance function, but CFOs are discovering that certain applications deliver more immediate, measurable impact. In many instances, AI builds upon existing automation foundations: for example, by taking rule-based processes and making them adaptive, thus turning previously rigid workflows into intelligent ones where AI can learn and improve. A common thread in successful implementations is that AI amplifies human judgment, rather than replacing it—automating the routine and spotlighting insights that improve decision-making.

The most valuable AI uses for CFOs combine operational efficiency gains with enhanced visibility and control, creating a multiplier effect that strengthens both tactical execution and strategic planning.

FP&A

AI-powered financial planning and analysis (FP&A) helps diminish the pressure on CFOs to deliver more accurate forecasts to boards and investors, especially if they are maneuvering through uncertain business conditions. AI-enabled forecasts can automatically refresh as new data flows in, revising revenue projections in response to updated sales patterns and recalibrating expense forecasts based on procurement activity—with both adjustments potentially also reflecting any changes in external market indicators. Rapid scenario modeling, a strength of FP&A systems with AI capabilities, changes how CFOs handle strategic planning requests. Because finance can test nearly endless numbers of what-if situations, they can serve as more responsive business partners when other managers ask about potential impacts of business decisions. AI models instantly show how changes in key variables, such as interest rates, currency fluctuations, or demand, cascade through each of the projected financial statements.

AI also augments the way CFOs communicate these insights, often generating interactive visualizations and narrative explanations that make complex financial analyses more accessible. AI-powered FP&A software can also act as an early warning system by identifying risks before they materialize, so that leaders can adjust course to capture opportunities or avoid pitfalls. For example, it can identify potential issues, such as when customer payment patterns signal a future cash crunch.

Workflow Automation

AI-powered workflow automation often presents immediate and measurable benefits for CFOs by automating a greater number of manual processes—especially valuable for CFOs pressured to curb head count. Tasks that once swallowed days of the accounting staff’s time during each close, such as journal entry creation, three-way invoice matching, bank reconciliations, and intercompany eliminations, can be handled automatically by AI-enabled systems. Among leading AI adopters, companies report automating up to 80% of AP workflows and nearly all recurring journal entries.

Besides efficiency gains, automated workflows give CFOs stronger assurance of financial integrity and regulatory compliance as error rates decline and process consistency rises. They can create a complete, time-stamped audit trail that documents every action and approval, critical, for example, for Sarbanes-Oxley Act (SOX) compliance and audit readiness.

Risk and Compliance

Managing regulatory compliance and financial and operational risks justifiably consumes significant CFO mindshare. Traditional approaches tend to be reactive or detective, leaving CFOs vulnerable to discovering problems only after they occur—or, worse, having auditors or regulators find them first. AI tools can shift this dynamic to proactive prevention.

For example, when new regulations are published, GenAI can read and summarize the key requirements, highlighting what has changed; it can then suggest which existing internal controls might be affected. Armed with this time-saving information, CFOs can decide how to adjust policies and processes before compliance gaps emerge. This same capability can also assist CFOs in optimizing tax strategies when tax codes change by analyzing transactions across jurisdictions and identifying new tax-saving opportunities.

What’s more, AI systems can monitor 100% of structured data transactions, such as AP and AR, and many unstructured data, like emails. This means they can close the blind spots inherent in periodic sampling through automated enforcement of tax codes, accounting standards, company policies, industry regulations, or environmental, social, and governance (ESG) requirements.

AI also introduces a new level of risk surveillance, giving CFOs the ability to spot early-warning patterns before they escalate, such as recognizing when rapid expansion strains segregation of duties, when new business models create fresh risks, or when organizational restructuring opens compliance gaps. For instance, when a business launches a new subscription model, an AI risk monitoring system can highlight recurring revenue accounting requirements that need to be addressed.

With these capabilities, CFOs can get ahead of risk and compliance challenges and improve their prowess as facilitators of business innovation and growth. 

Anomaly Detection

AI excels at spotting irregularities that can signal anything from fraud to operating issues to finance staffing concerns. Unlike rule-based systems that only catch known issues, AI’s machine learning algorithms can learn what “normal” looks like for each organization and flags deviations that warrant investigation. This level of anomaly detection gives CFOs operational intelligence across their domains of responsibility. It also increases finance team efficiency, making it possible for teams to focus on meaningful anomalies, as opposed to investigating every budget or forecast deviation.

Some areas where CFOs use AI anomaly detection include:

  • Fraud prevention: AI can flag duplicate invoices, ghost vendors, unusual payment patterns, expenses charged during staff vacations, and correlate vendor and employee records.
  • Revenue protection: AI can pinpoint leakage from incorrect discount applications or unenforced contract terms.
  • Cost control: It can spot departmental spending creep before overruns become material.
  • Customer intelligence: Early warnings are issued regarding key customers that begin changing their orders, returns, or payment patterns.
  • Internal controls: AI can flag unusual journal entries, approvals just below authorized limits, transactions without proper documentation, and purchase orders created after invoices arrive.
  • Staff performance: It can be used to identify processing inefficiencies or error patterns, potentially indicating either additional training needs or potential manipulation.
  • Security monitoring: When event logs are monitored, AI can flag unusual access, such as attempts occurring outside normal hours, large data downloads, or out-of-scope record viewing.

Cash Flow Analysis

AI-enabled cash flow analysis and forecasting gives CFOs an updated view of liquidity that adapts to changing business conditions. These fluid models help answer questions that once required manually handled scenario work: Will we have sufficient liquidity for next quarter’s expansion? When will seasonal patterns create cash constraints? How might customer concentration affect collection timing?

AI synthesizes all kinds of internal and external data into its models, including sales pipelines, payment histories, market indicators, and operational metrics. It can also layer in other outside influences, such as weather or political disruptions, social media sentiment, and currency fluctuations.

Combining these expanded inputs with advanced analytics can reveal patterns and correlations that give CFOs new perspectives on cash planning. For example, AI-driven cash flow forecasts might recognize that certain customer segments pay more slowly during earnings season, or detect that changes in vendor payment behavior signal supply chain stress. When integrated with ERP and treasury systems, these forecasts provide CFOs and treasurers with up-to-date, data-supported information so that liquidity and investment decisions rest on evidence rather than assumptions.

AP/AR Processing

CFOs’ use of AI in AP/AR automation focuses on three primary goals: optimizing working capital, improving processing efficiency, and controlling fraud. These efforts are finding success. In a study of 500 companies using AI in AR processing, 82% report productivity gains. At the same time, almost all have reduced their days sales outstanding. On the AP side, AI adoption has increased fourfold to 29% of those surveyed in 2025 versus only 7% in 2024, according to global research from the Institute of Financial and Operations Leadership. Respondents’ top two objectives for AI adoption were to speed up AP processes and strengthen controls.

AI is most effective at improving cash conversion cycles when working both ends—AR and AP—simultaneously. On the AR side, it can determine which customers to chase for collections and which to leave alone, actions that benefit cash flow and staff efficiency. For payables, AI helps the finance team make equally smart trade-offs. When cash is flush, it might suggest capturing a supplier discount by paying early. But when liquidity tightens, it’ll recommend skipping a discount to preserve cash, weighing such factors as upcoming commitments, credit line interest rates, and desired liquidity buffers. Together, these capabilities help CFOs manage working capital with greater precision, and without any added effort.

Unlike basic AP/AR automation that requires perfect matches, AI can make sense of the messy reality of business, learning from every transaction. It can pull information from emails and PDFs and route approvals to the right person, recognizing which managers handle certain vendor types or transaction categories. On the receivables side, AI can help apply cash more precisely, even interpreting vague remittance details like “January invoices” and determining which specific invoices that includes.

AI strengthens AP/AR controls by screening every transaction for violations and anomalies. On the payables side, it catches unusual payments and flags invoices that bypass purchase orders or exceed authorization limits. For receivables, AI accurately matches incoming payments to open invoices, reducing unapplied cash that can obscure true cash positions and create opportunities for fraud. This level of monitoring cuts down on blind spots in approvals and payment patterns, resulting in an audit-ready system that simplifies SOX compliance.

What Role Do CFOs Play in AI Adoption?

CFOs’ unique position, balancing innovation with financial discipline, makes them natural leaders in AI strategy and adoption. Within finance, they establish governance frameworks for AI processes, maintain compliance with reporting standards, and validate that AI-generated outputs meet audit requirements. They’re also reshaping their teams by hiring data scientists alongside accountants, and training existing staff to work with AI technologies.

Across the organization, CFOs serve as AI investment gatekeepers and ROI champions. They evaluate initiatives from every department, applying financial rigor to separate value from hype. Partnering with other C-suite leaders, they figure out how to fund AI projects and are major players on the investment committees that decide which initiatives receive funding. CFOs also champion data-quality standards, recognizing that reliable data underpins successful AI adoption.

This expanded role turns CFOs into AI educators and advocates, as well as stewards of capital. They explain AI capabilities in terms of business outcomes for boards and investors, able to discuss ROI, risk mitigations, and competitive advantage. They also navigate the ethical considerations surrounding automated decisions and make sure to maintain appropriate transparency. CFOs run pilot programs to prove AI’s value before scaling up. They define what success looks like and foster interdepartmental collaboration.

Potential Challenges of AI Adoption for CFOs

In practice, AI implementation brings challenges that can derail even well-funded initiatives. Smart CFOs recognize these obstacles early and plan accordingly, building realistic timelines and allocating the appropriate resources. Here are four challenges organizations are most likely to face:

  • Insufficient data quality: AI needs clean, standardized data to generate reliable analyses. Many organizations will need to fix inconsistent information drawn from disparate systems, including duplicate customer records, variable vendor naming, and erratic general ledger coding. Data cleanup can delay implementation and become a significant hidden cost. AI also needs months to years of historical data. Anomaly detection and AI-assisted planning won’t work if it doesn’t have a view of how the business has operated in the past. Systems like ERPs typically need to be in place before bringing in AI, as they serve as systems of record for more than finance.
  • Change management and user adoption: Resistance to AI commonly stems from concerns about job security or skepticism about automation replacing professionals’ judgment. Even willing adopters can experience steep learning curves when changing from familiar processes to AI-enhanced workflows. CFOs must invest in training programs and demonstrate early wins to build confidence—but must still expect temporary productivity declines during the transition. Those early wins can happen by using AI features within tools already in place, since many vendors are adding AI support within existing workflows. Change management starts well before AI is put into production as bad outcomes often come when AI is brought in but isn’t provided with enough good data to be effective.
  • Choosing between custom builds vs. out-of-the-box: Starting from scratch with AI is very rarely a good idea unless it’s needed as part of a business’s core offering. Analytics systems will often include AI tools for analyzing large datasets in various ways. Business management software vendors increasingly offer AI capabilities within their products and may provide integrated systems for creating unique AI agents by combining the tools, large language models, and other AI technology they provide. These environments typically require little or no coding. Using these systems to customize AI to unique business needs is a semicustom approach that’s far less demanding than attempting to build AI models from the ground up.
  • Compliance limitations: AI in finance must satisfy different rules across jurisdictions. What meets the European Union’s AI Act transparency requirements might fall short of US Securities and Exchange Commission documentation standards. Traditional audit rules weren’t built for AI outputs, leaving CFOs to develop their own internal controls to validate AI-generated journal entries or forecasts for SOX compliance. Data privacy laws further restrict how financial information can feed AI models. Together, these realities suggest CFOs should work closely with IT and legal partners to tackle new compliance challenges, where mistakes carry real penalties.

Choosing the Right Software Foundation for AI

NetSuite ERP gives CFOs the AI capabilities they need to increase productivity and gain deeper business insights. Because NetSuite consolidates business information into a unified database, it provides the clean data foundation AI requires to generate actionable insights. Its embedded AI approach means CFOs avoid bolt-on integration challenges while maintaining complete audit trails that satisfy compliance requirements. The software automates repetitive processes like data entry, transaction matching, and reconciliations, and its planning and budgeting feature enhances forecasting with predictive analytics that monitors variances and generates narratives explaining why numbers change.

This combination of automation, predictive analytics, and unified data in a single solution equips CFOs to improve operational performance through enhanced visibility, deliver perspectives that drive profits and growth, and maintain compliance with financial regulations. NetSuite accelerates financial processes to allow accounting and finance teams to focus on the activities and decisions that require the expertise of a human business partner.

AI is reshaping how finance operates—from forecasting and reporting to compliance and cash flow management. The next phase for CFOs is disciplined execution—that is, using AI where it drives measurable value, yet preserves accuracy, control, and transparency. Success requires clean data foundations, careful software selection, and strong change management to help teams adapt to new workflows. The future of businesses’ finance functions is likely to include a partnership between AI-powered systems and CFOs, each doing what they do best to create a finance department that is both more efficient and more strategic.

AI for CFOs FAQs

How do CFOs use AI?

CFOs use AI to reduce manual work and drive productivity in areas like reconciliations and collections. They also use it to improve the quality of financial planning activities, such as forecasting and scenario modeling, and to strengthen internal financial controls by catching fraud patterns and compliance issues that might otherwise go undetected. As leaders, CFOs guide companywide AI strategy by setting ROI expectations and confirming that implementations meet financial reporting requirements.

How is AI used in financial management?

AI automates routine finance tasks, ranging from invoice processing to bank reconciliations, to reduce errors and increase scalability. AI analytics powers strategic planning capabilities, enabling more accurate forecasting and flexible scenario modeling, and helping CFOs to optimize working capital, predict cash positions, and manage risk with greater precision. It continuously monitors transactions to detect fraud, maintain compliance, and flag operating anomalies before they become problems. Generative AI creates reports, drafts variance explanations, and provides natural language interfaces for querying financial data. In accounts payable and receivable, AI routes approvals, matches payments, and prioritizes collections according to customer behavior patterns.