5 Generative AI use cases in finance CFOs and their teams can adopt right now

You’ve probably read a few LinkedIn posts and a bunch of news articles about AI transforming every aspect of business. But the truth is, many CFOs are reluctant to implement it just yet. According to a survey, 58% of finance leaders understand very little about AI.

This means they’re skeptical about the risks of using AI. After all, regulators can’t hold AI accountable for errors, so it’s natural for you to be extra careful. However, AI can give you a few easy wins without taking on significant risk.

In this guide, we look at five generative AI use cases you can reliably implement now.

What is generative AI?

Generative AI is a type of artificial intelligence that creates new content (text, pictures, music, and more) by mimicking patterns and relationships found in existing data.

In FP&A, you might use generative AI to generate context-aware narratives when writing commentary for management reports or create multiple what-if scenarios based on variables like sales volume or interest rates.

Benefits of generative AI for FP&A

In its current state, generative AI offers the following benefits to you as an FP&A professional:

• Improved decision-making: You can ask generative AI to dig through your spreadsheets and reports for instant answers and insights into trends and risks. Instead of reading through a 50-page report from your team, let generative AI do the heavy lifting so you can focus on making decisions based on the report’s findings.
• Increased productivity: Think about the hours you save when you hand over grunt work like writing executive summaries and variance explanations to generative AI. It leaves you more time to focus on strategic thinking and gives you a few extra minutes to talk business with your colleagues by the watercooler.
• More accurate forecasts: You can use generative AI to scan past trends, signals in external data, or unstructured data (like news or earnings reports) and use the findings to flag anomalies or blind spots in current forecasts.
• Natural language Q&A: Remember the last time you scanned a financial statement and were surprised by an unexpected spike or drop in a certain figure? Generative AI can quickly look up data and fetch the answer. So the next time you’re worried why OpEx spiked in April, just ask generative AI instead of digging through 12 tabs in an Excel spreadsheet.

5 use cases finance teams can adopt right now

Generative AI has a wide range of use cases for finance teams. Let’s look at five specific ones to realize value from your investment in generative AI as quickly as possible.

AI-powered accounts payable (AP) automation

AP is the easiest process to automate using generative AI. It essentially replaces someone approving payments and making the transfer, and it’s usually a person with other, more strategic responsibilities who is happy to hand over this mundane task to a robot.

Other examples include generating responses to supplier questions, creating customized reports that explain discrepancies and exception handling, and approval workflows that minimize the number of sign-offs necessary to pay an invoice. 

For these use cases, you can reasonably expect minimal resistance to change. And, because they don’t require a lot of process re-engineering, you can probably complete the implementation of your new AI-assisted automation within a few days to weeks.

Accounts receivable (AR) and collections optimization

According to a survey by The Kaplan Group, 93% of companies lose revenue from late payments. Some lose over 10% of their annual revenue.

Generative AI can optimize your collection efforts and shrink the accounts receivable balance on your balance sheet. It can craft tone-appropriate, customer-specific messages to remind customers about invoices already due or about to become due. This polite nudge reduces friction with customers and gets money into your bank account.

For example, here’s what a payment reminder might look like: “Hi Sarah, just a friendly heads-up that Invoice #3042 is due in 3 days. Let us know if you need anything to process it.”

Cash flow forecasting and analytics

Cash position changes with every transaction, and unless you don’t mind holding too much or too little cash, you need real-time visibility over cash flow.

Assume you’ve integrated your generative AI tool with your internal systems (ERP, accounting, banking, and others). It pulls live data on payables, receivables, payrolls, and revenue. It uses this data to generate a clear summary of liquidity position, meaning you waste a total of zero minutes staring at raw numbers.

At the same time, generative AI can use past data to build more accurate forecasts. Of course, these forecasts still need your insights to factor in the estimated impact of future events like an impending recession or the entry of competing innovative products.

Expense management and fraud detection

How would you like to feed a generative AI tool your bank statement and ask, “Any signs of duplicate payments this quarter?” instead of manually checking each line item? Generative AI can run fraud detection checks for you and present findings in plain English.

In fact, generative AI is exceptionally good at detecting fraud. A recent study comparing different ML models suggests that ML algorithms can achieve up to 96% accuracy in flagging illicit transactions.

Similarly, it can also help you manage expenses. For example, a generative AI tool can create a clean summary when you ask, “What were the top five expense categories that grew last month?” so you don’t have to deal with a spreadsheet dump.

The best part is that deployment isn’t too resource-intensive. Most AI-powered fraud detection and expense management solutions are cloud-based and have minimal IT requirements.

Financial reporting and close automation

Accurate financial reporting is non-negotiable. Errors cost money and reputation. Also, can we all agree that financial reporting is a compliance requirement, and it’s rarely the most exciting part of an FP&A professional’s job?

Generative AI can eliminate errors from the process, achieve near-perfect accuracy in reconciliations, and wrap up the month-end close in a few days. It effectively frees your team from mundane reporting tasks and helps them focus on strategic parts of their job.

What are the barriers to adopting AI in finance?

When your leaders ask questions about how you’ll overcome potential barriers, you want to have all the answers handy. Let’s look at some common barriers you can expect when adopting AI for finance so you can be prepared to address them:

Figuring out what technologies you need

You probably know what generative AI is, but you might not know what you need to successfully implement it. 

Do you need a data warehouse, an AI agent, or something you’ve never heard of? A lack of clarity here can lead to overspending on generic AI tools, so start by learning what tools you need and why.

Data and integration with AI tools

Any AI tool you deploy will rely on data, which is also the biggest barrier to moving towards “intelligent planning,” as reported by KPMG. For many mid-sized companies, cleaning and organizing data spread across spreadsheets and other disconnected systems like ERP or CRM can be a friction point.

There’s no substitute for clean, structured data, but you can choose systems wisely to steer clear of an integration problem. Drivetrain, for example, works like a plug-and-play AI system for financial analysis because it easily integrates with all the popular systems available on the market.

Upskilling needed to use AI effectively

Using generative AI for FP&A requires a strategic approach. Most teams aren’t data science experts, and many don’t have access to an in-house expert either. You'll need to teach your team how to play to make the most of your new AI FP&A power tools.

Training costs time and money, and if you’re cash-strapped, that can turn into yet another barrier. Be mindful of these issues before you start creating a roadmap. If you already have one, revisit it with this in mind to avoid your implementation stalling out due to a lack of necessary skills.

Develop a training strategy based on the tools you plan to deploy, estimate the cost of upskilling the team, and make sure you have enough resources available before you put money on the table.

Identify the skills your team will need to make AI integration work

The need for upskilling is cited in almost everything you read today about implementing AI in your business. This makes sense of course, given how quickly AI has emerged as a tool for transformation. 

The reality is that any business interested in implementing AI is going to be somewhere in the first or second mile of that journey where skills are concerned. So, you need targeted training to make sure your team can acquire the right skills and effectively leverage AI to avoid unnecessary effort. 

Let’s look at which skills you should focus on if you want to implement one of the generative AI use cases we’ve described above. 

Skills needed for implementing AI-powered AP automations

AP teams are trained for precision. Traditionally, they have been responsible for flagging invoicing errors and duplicates, reconciling mismatches, and requesting payment approvals. They’re also familiar with compliance regulations, tax codes, and internal controls because they often face auditors.

With AI entering their workplace, AP’s role shifts to monitoring AI tools that automatically extract and process invoice data (extractive AI). This requires some data literacy; they need to know how invoice formats, metadata, and structured fields impact the AI tool’s accuracy. They also need to be able to configure AP AI tools with ERP and other systems.

For generative AI uses, specifically, prompting skills are critical because how you ask the questions can strongly influence the results you get if you’re doing a DIY implementation using one or more of the publicly available AI models. You can probably expect these skills to be somewhat less critical with AI-powered AP automation tools that have generative AI capabilities built in as they often provide example prompts. 

Skills needed for optimizing AR and collections using generative AI

AR teams traditionally stayed busy negotiating payment terms and following up on overdue accounts. They also resolved payment and invoice issues and matched payments to open invoices.

AI makes their work more strategic. They can use AI to forecast payment behavior and optimize collection efforts. AI can also help your team develop segment-specific collection strategies. Your team still needs to manually manage cases that automation can’t resolve, of course, but your team already does that and may not require additional training for it.

Skills needed for using generative AI to improve cash flow forecasting and analytics

When your finance team hears “cash flow analytics,” they think of financial models, interpreting trends, and the effect of seasonality and payment cycles on cash flow. These fundamental skills are invaluable, but with AI in the mix, they’ll need to shift focus from building models to evaluating the AI tool’s output.

Your team will be the critical humans in the loop (HITL) here, responsible for validating forecasts, challenging assumptions, and using AI to run scenario analyses. They’ll also need to learn how to simplify AI’s insights for non-technical stakeholders and continuously strive to fine-tune AI tools based on feedback and business changes. As with other generative AI use cases, AI prompting skills will be critical.

Skills needed for AI-powered expense management and fraud detection

Expense management and fraud detection have traditionally relied heavily on manual review. For this, your team needed strong attention to detail, process knowledge, and an understanding of how your company usually spends money.

AI automates much of that mundane work. Your team must now focus on supervising the AI tools’ output, flagging errors or misses, and resolving them. For this, they’ll need to be aware of AI’s potential for biased output and understand the logic the tools are using to understand and evaluate the flagged transactions. These skills take a bit of first-hand experience, so be sure to consider that when you plan their training.

Skills needed for using generative AI to improve financial reporting and close processes

Financial reporting requires a thorough understanding of GAAP, IFRS, and local accounting standards. Then there’s the manual part of the workflow that requires you to match sub-ledgers and consolidate books (if you’re dealing with multiple entities).

AI can handle all these tasks, but relies on your team for accurate and standardized data. Even with perfect data, you need a team member who is able to interpret and evaluate the AI’s recommendations and changes during the close process. 

Most importantly, you need someone on the team who understands risks and compliance issues that come with automated reporting.

Embracing generative AI in finance

There’s plenty that AI tools can take off your plate as soon as you implement them.

Drivetrain, for example, offers real-time forecasts and anomaly detection, scenario modeling, and over 800 native integrations to make the automation part easy. These capabilities simplify analysis, eliminate mundane monitoring tasks from your workflow, and augment your FP&A workflow through AI-powered automation.

If you look at the big picture, you’ll notice a pattern in how AI is being implemented right now: AI doesn’t “take over” finance as a function, it just augments your capabilities through automation and real-time interpretation of extensive datasets. 

And that’s why embracing AI technologies is mission-critical for finance leaders and their teams today. 

Start your AI journey with an easy-to-implement tool. Check out how Drivetrain compares to other platforms on the market.