by Kaeli Lange, Zohar Liebermensch, and Camden Wiggins
In today’s fast-paced and rapidly evolving landscape, Artificial Intelligence (AI) is no longer a luxury, but a necessity for competitive parity. AI can significantly enhance the accuracy and efficiency of financial reporting, accounting, compliance, and other finance functions. As teams look to shift from a support function to a forward-looking strategic partner, leaders are under growing pressure to provide real-time insights, enhance agility, and support enterprise-wide transformation. AI is the critical lever in enabling this evolution.
Finance leaders are being asked to do more with less, whether that be to serve as strategic advisors, risk managers, capital stewards, or operational enablers. They are facing an overwhelming volume and velocity of data that complicates traditional tools. AI offers a transformative opportunity. It can augment human judgment, accelerate insight generation, automate transactions, and embed resilience into planning.
However, even the most forward-looking leaders are still struggling to gain momentum and select impactful, yet feasible use cases to propel the functions within their organizations to operational greatness. This article explores how AI technologies such as Generative AI (GenAI), large language models (LLMs), agentic workflows, and complex machine learning (ML) techniques can be applied to enhance organizational processes, with a focus on use cases that offer strategic value to CFOs.
Use Case Ideation Framework
When identifying AI opportunities across processes, we use a six-style framework to guide ideation and ensure relevance. This framework is based on primitives commonly recognized across the industry, including by OpenAI, to guide ideation and ensure relevance.
- Content Creation: Draft, edit, and localize text, visuals, and presentations.
- Research: Accelerate topic exploration and summarize complex materials.
- Coding: Generate and debug code, enabling broader use of tools like Python and SQL.
- Automation: Streamline routine tasks and optimize workflows.
- Ideation & Strategy: Support brainstorming, planning, and alignment with KPIs.
- Data Analysis: Detect trends, anomalies, and extract insights from diverse data formats.
These styles represent broad categories of AI use cases, each offering infinite solution possibilities. Because of this vast potential, institutions must carefully assess feasibility and impact, then prioritize based on their unique goals, constraints, and readiness. Use case assessment goes far beyond functional needs. We also consider organizational priorities, available resourcing, technology constraints, infrastructure and data readiness and controls, risk factors, and any IT-imposed limitations on tools or platforms. This holistic approach ensures that AI initiatives are not only innovative but also practical and sustainable.
While there is no singular solution that can apply to every organization, below are some common ways finance functions are leveraging AI after completing their ideation phases.
Financial Planning & Analysis (FP&A)
FP&A is a critical function that can benefit from the ability of AI to deliver faster and more dynamic and accurate results. Chief Financial Officers (CFOs) leveraging AI can gain more timely insights and reduced lag time, resulting in lower costs, increased efficiency, and greater agility to proactively adapt to planning changes, rather than reacting to them. Some AI considerations include:
- Forecasting and Budgeting: ML and Agentic AI can enhance forecasting and budgeting by analyzing historical data and market trends, producing more accurate, timely, and even autonomous forecasts for metrics such as revenue and expenses.
- Scenario Analysis and Planning: LLMs and automation can be utilized for scenario analysis and planning, allowing FP&A teams to perform scenario modeling in real-time, using natural language as input.
- Report and Narrative Generation: GenAI can be leveraged to draft narrative explanations of budget vs. actual variances, identifying performance drivers and saving analysts the time they would ordinarily be using to write reports.
- Analysis Interpretation: Not only can GenAI be used to perform analyses and explain what occurred, but it can also be used to explain why, ideate on potential solutions, and suggest next steps.
Month-End Close and Financial Reporting
Month-end close can be a time-consuming process, with manual reconciliations and financial reporting often delaying the close. AI can be used to expedite close timelines by automating these areas, resulting in faster and more accurate close cycles, and ultimately, more timely financial information for management and investors alike. The following are a few of the ways AI can enable an expedited close:
- Reconciliation Automation: AI, in combination with classical automation, can streamline large volumes of reconciliation work that often leads to delays in month-end close.
- Fraud and Anomaly Detection: As identifying errors and potential fraud are critical to month-end close, complex ML can be leveraged to detect anomalies and search for unusual patterns in the data, catching abnormalities in transactions that may not have been discovered otherwise.
- Financial Report Drafting: Once the numbers are finalized and accurate, GenAI can be used to produce financial reports and narratives to support management’s commentary and observations, saving analysts the time that it takes to write such reports.
SEC and Regulatory Reporting
Reporting is another function that is time-consuming and demands precision and consistency. Using AI in SEC and regulatory reporting can lead to increased efficiency, improved insights, and compliance risk reduction. Below are some of the ways regulatory reporting can be transformed by AI:
- Automated Report Drafting: Using structured data, unstructured data, analysis, and a report template or outline, GenAI can be leveraged to produce first drafts of SEC filings and regulatory reports in minutes to hours instead of spending weeks or months crafting them.
- Automated Regulatory Monitoring: Given that regulatory reporting needs to be accurate and compliant, GenAI and Retrieval-Augmented Generation (RAG) agents can be used to help maintain precision and compliance through regulatory monitoring, cross-checking, or flagging mismatches.
- Report Generation and Tone Consistency: Reporting also requires consistency of narrative and tone across an organization, regardless of who is writing the report. Using examples of internal reports, GenAI can be used to generate reports using the agreed-upon voice of an institution.
Treasury Management
When used in treasury management, AI offers CFOs improved yield/cost, better risk control, and the ability to more effectively plan ahead for various scenarios. The following are some ways treasury functions may consider adopting AI:
- Cash Flow Forecasting: Machine learning algorithms can be utilized to create accurate cash flow forecasts, giving treasury a clearer picture of cash needs and enabling them to make more proactive moves and decisions.
- Automated Liquidity Management: Agentic AI specifically can execute routine liquidity management tasks autonomously, such as monitoring forecasts and global cash balances, moving excess funds, and providing insights and recommending actions for timely management of cash in different scenarios.
- Risk Modeling and Hedge Optimization: Treasury often manages risks (such as FX, interest rate, or commodity risk) via hedging. GenAI can be used in combination with automation and classical machine learning techniques for improving risk modeling and hedge optimization, by signaling when to execute trades or helping treasurers plan buffers by stress testing the balance sheet.
- Consumer Behavior Forecasting: Treasury often works with accounts receivable and accounts payable to optimize working capital. In accounts receivable, predictive ML algorithms can be used in combination with GenAI to predict customer behavior and recommend actions based on payment history and forecasted payments.
Audit and Internal Audit
AI can analyze documents, monitor controls, and identify underlying patterns and anomalies in large datasets, helping to ensure that audits are more efficient and accurate. A few ways audit can benefit greatly from AI include:
- Intelligent Sampling: As common ML methods and various Neural Network techniques can identify patterns in large volumes of data, a powerful use case is leveraging AI to select samples based on risk indicators rather than random sampling. Using historical data like amount, preparation history, timing, account type, etc. to suggest sample size rather than using a set number can strengthen audit coverage and allocate additional review for riskier items.
- Writing Audit Controls: GenAI can be leveraged to assist in writing audit controls using internal templates, control requirements, and policies to help ensure that all necessary details and steps are included.
- Automated Document Summarization: A common use case for GenAI and LLMs is ingesting, reviewing, and summarizing large volumes of text data, which presents an opportunity for AI to be leveraged for document and contract review.
- Process Interviews and Walkthroughs: AI chatbots and agents can be used to conduct interviews with process owners, collect necessary information, and then leverage GenAI to generate standardized walkthrough documentation.
Investor Relations and External Communications
When it comes to external communications, AI can enhance the process by tailoring the message and tone, saving time, and creating narratives that are both informative and easy to understand. Some ways that CFOs can leverage AI for important communications include:
- Automated Script and Narrative Generation: GenAI can assist with earnings release and script generation, saving the time that it takes to prep quarterly earnings materials, scripts, and slide decks.
- Investor Q&A Preparedness: Using role-based prompting techniques, GenAI can also help stakeholders prep for difficult Q&As and investor inquiries by generating thoughtful questions and answers from the perspective of an investor, stakeholder, or analyst.
- Sentiment Analysis: As it is important for CFOs to understand the public perception of their company, RAG agents can be combined with sentiment analysis techniques to autonomously monitor sentiment from the news, social media, and analyst reports.
- Drafting Large-Scale Communications: CFOs frequently handle large-scale communications, such as SEC filings and financial statements, and GenAI can be used to help craft these disclosures and parse complex information into summaries that are easy to understand.
Tax
AI can help the tax function cut costs, reduce risks, and plan strategically for complicated or nuanced tax situations. Some considerations of AI use cases in the tax function include:
- Tax Law Research and Compliance Monitoring: Tax laws and regulations are constantly changing, and can often be difficult to interpret, but RAG agents and LLMs can help institutions streamline their research and better understand the rules.
- Data Classification: Machine Learning techniques can be combined with agentic AI to assist with automated data classification, as preparing tax returns involves gathering and categorizing large volumes of financial data.
- Tax Process and Savings Optimization: Given the complexity of tax planning, GenAI is well-suited to analyze both historical data and current operations to optimize structures and identify tax savings.
- Tax Return Review: Corporate tax filings may be audited by tax authorities, and LLMs can enhance audit defense by checking returns for inconsistencies and red flags before filing.
Whether your firm’s goal is cutting costs, streamlining operations, or producing more accurate and reliable forecasts, AI is a strategic lever that can position an institution for continued innovation, evolution, and competitive advantage. The journey to AI-powered finance transformation is not a one-size-fits-all solution. Reshaping your finance function begins with a willingness to explore, experiment, and identify the most impactful use cases. While the use cases listed in this article are non-exhaustive, they can serve as an ideation starting point for CFOs to tap into AI’s unlimited potential to enable growth and transform the finance function.
Now is the time to act. To truly begin the AI transformation journey, CFOs and finance leaders must move beyond ideation to implementation by starting small and piloting strategically. The true power of AI lays not only in its capabilities, but in its thoughtful application. Organizations that act today will lead tomorrow by identifying and selecting high-impact, feasible use cases that align with business objectives, paving the way for meaningful innovation and sustainable growth.
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