Here's where AI can make the biggest difference in finance

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Nearly half (47%) of finance and accounting leaders say outdated technology increases manual or repetitive work, while only 29% say their organization can move quickly and confidently when adopting new technologies. For finance leaders looking to get more value from AI, the challenge is not simply finding possible applications. It is identifying the use cases that address a genuine business problem and can be implemented responsibly.

Some opportunities produce clearer benefits than others, and not every process is a good candidate for AI. The examples below highlight areas where finance teams are already seeing results, followed by a framework to help leaders identify similar opportunities in their own organizations.

Practical AI use cases in finance

What are the most useful AI use cases in finance?
How can finance leaders identify the right processes for AI?
What skills do finance teams need to work effectively with AI?

What are the most useful AI use cases in finance?

Most finance teams use AI in 3 primary ways.

1. Predictive AI for finding patterns and flagging problems

Predictive models find patterns in historical results, test scenarios and draft variance explanations. They can also flag unusual transactions or duplicate payments for finance professionals to investigate.

2. Finance automation for invoices and reconciliations

Invoice and close systems can extract data, match invoices with purchase orders and send discrepancies for review. During reconciliation, they can match transactions and organize unresolved items. Reliable master data and documented exception rules are essential.

3. Generative AI in finance for reporting and compliance support

Generative AI can draft reporting commentary, summarize records and organize evidence for compliance monitoring. It gives you something to react to and refine, which is faster than starting from a blank page. The downside? It produces something confident-sounding whether or not the numbers back it up. Someone who knows the figures needs to read the draft before it goes any further.

The categories often overlap

These distinctions are useful for understanding how AI is used in finance, but many applications combine multiple approaches. For example, an invoice system might use rules to check required fields, predictive AI to flag an unusual payment and generative AI to summarize the exception.

How can finance leaders identify the right processes for AI?

Not every process is equally well suited to AI. The following four considerations can help finance leaders identify the opportunities most likely to deliver meaningful value.

1. Assess whether the problem has a non-AI solution

Organizations sometimes reach for AI when the underlying problem is something else. Determine how the process performs today and what improvement you're trying to achieve.

Then ask whether you actually need AI at all. Many finance-process challenges turn out to be workflow problems, or skills gaps disguised as technology gaps. Could a better ERP setup or a redesigned approval chain close most of those gaps? If so, consider addressing that first.

2. Test process and data readiness

Before considering AI for a process, look at how the work is performed today. Does it follow a consistent set of rules, or does it depend heavily on exceptions, manual workarounds and individual judgment? Processes with clear inputs and repeatable steps are often easier to improve with AI than those that vary significantly from case to case.

This foundational work remains a challenge for many finance organizations. Among finance and accounting leaders, 40% say skills shortages have affected initiatives involving data accessibility, completeness and security, while 37% point to ERP and financial systems upgrades and 36% cite enhanced business and data analytics capabilities.

3. Match the level of oversight to the risk

Not every AI output carries the same risk. A draft budget summary shared with your own team is one thing; a number that ends up in a quarterly filing is something else entirely. Before pursuing a use case, consider where the output will be used, who will rely on it and what the consequences would be if it's wrong. The higher the stakes, the more important human review and accountability become.

In other words, the level of oversight should reflect the potential consequences of an error. Applications that support internal drafts or routine administrative work may require less review than those that affect financial reporting, compliance or other high-stakes decisions.

4. Weigh total effort against potential benefit

Consider the likely benefits alongside the time, resources and organizational change required to implement a potential AI application. The strongest opportunities not only target a clear business need but also a realistic path to adoption.

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What skills do finance teams need to work effectively with AI?

The success of AI applications in accounting and finance depends on the people using them.

Yet, Robert Half research finds that only 35% of workers feel very confident using AI tools effectively. At the same time, 50% of finance and accounting leaders say technology modernization is driving hiring for new skills required by modern technology, and 50% are hiring professionals to support implementation projects.

Finance teams need three things to work effectively with AI: an understanding of AI tools and their limitations, confidence in using those tools appropriately and the ability to interpret AI-generated information in context and recognize potential errors.

Targeted training can help close that gap. Employees should understand what approved AI tools can do, where they are most likely to make mistakes and how AI-generated information should be interpreted in their day-to-day work. They also need the judgment to recognize when an output deserves closer scrutiny. Robert Half research reinforces that point: 64% of finance and accounting leaders identify critical thinking as one of the most important competencies for effective AI use.

Without that foundation, even well-designed AI initiatives may fall short of expectations.

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