Three high-impact AI use cases in Finance and Accounting

Finance and Accounting teams are under pressure to deliver faster closes, cleaner data, and sharper insights-all with fewer resources.

Artificial intelligence is often hailed as the answer, but most organizations are still finding their footing.

MagentIQ’s recent research found that 62 percent of finance leaders reported they are “researching and experimenting,” while less than 5 percent are running AI in production.

Infographic: State of Agentic AI in Finance & Accounting (click image to enlarge)

That reality reflects the broader market. According to BCG, only 4 percent of companies generate real value from AI investments, while McKinsey reports 74 percent struggle to move beyond pilots. As Ian Barkin, co-founder and co-CEO of magentIQ, explained: “95 percent of AI projects fail… and what we tend to find is projects don’t fail because of the tools being used. It’s because of everything else.”

So where should Finance and Accounting begin? The panelists agreed that value comes from targeted, outcome-driven use cases-not scattershot tool adoption.

Here are three areas where AI is already showing measurable impact:

Month-end close 

Closing the books remains one of the most resource-intensive and stressful cycles in finance. Kelly Dunn, who leads Finance Transformation at Georgia Pacific, described her team’s current focus: “We’re currently in an initiative to reduce our closing cycle time, trying to take one to two full days out of it.”

AI agents can support this by handling reconciliations, anomaly detection, and predictive entries. Instead of armies of analysts chasing down mismatched reports, AI systems surface discrepancies early, while human operators provide oversight and judgment.

This aligns with broader industry data. Deloitte’s CFO Signals survey notes that finance leaders cite “faster reporting and forecasting” as a top three AI priority for 2025. AI-driven anomaly detection, paired with human review, is one of the clearest paths to that outcome.

Accounts payable and invoice processing

Back-office transaction processing is fertile ground for automation. As Tariq Munir, digital transformation advisor and author of Reimagine Finance, put it: “Today, we are in 2025… as a base, your entire back office support or your entire transaction processing should be automated. There is no excuse for that.”

AI is particularly effective at:

• Extracting data from invoices and receipts
• Classifying transactions
• Managing exceptions through conversational workflows
• Routing approvals in line with policy

These improvements translate directly to higher first-pass yield and lower cost per invoice. Gartner has found that AI-enabled invoice automation can cut processing costs by more than 60 percent-a significant lever for any CFO managing cash flow and supplier relationships.

P&L review and variance analysis

Financial statement review has traditionally been labor-intensive, with analysts combing through data to identify anomalies. AI now offers the ability to surface unusual trends across P&L lines, highlight variances against forecast, and trigger deeper investigation.

Dunn noted her team is already testing this: “We are looking at using it to do our P&L review… whether you call it P&L review or anomaly detection… potentially, this is a tool that we can use in that space that has to reason, or mimic some of what a human does that is not rule-based at all.”

This is a textbook example of AI + people orchestration. AI agents flag the outliers; humans provide the context, interpret results, and decide on corrective actions. The result is a faster, more reliable review cycle-without sacrificing control or compliance.

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Why these use cases work

David Brain, magentIQ’s co-founder, warned against piecemeal adoption: “Most pilots stall because they automate tasks in isolation. The wins we see come from outcome-focused sprints where AI agents and human operators work together, end to end.”

That principle explains why these three use cases succeed. They are:

• Well-defined processes with repeatable steps
• Rich in structured data, making them AI-friendly
• High-volume and high-cost, ensuring visible ROI
• Outcome-oriented, with measurable impact on cycle time, accuracy, or cost

The road ahead for finance leaders

AI in Finance and Accounting is not about moonshots; it is about milestones. Start with reconciliations, AP throughput, or P&L anomaly detection. Define the outcome-days out of close, cost per invoice, errors avoided. Then run a small, governed experiment.

As Dunn cautioned, “We absolutely picked the wrong first use case for our first experiment, but we learned a ton along the way.” That lesson is invaluable: every attempt, even imperfect, builds the organizational muscle needed to scale.

The winners in this next era will not be those who deploy the most AI tools. They will be those who orchestrate AI and people best, delivering measurable outcomes where it matters most.

Key takeaway for F&A leaders

Start small, focus on outcomes, and deploy AI where it can move the needle: closing the books, automating payables, and sharpening P&L reviews. With the right orchestration, AI + people can turn Finance into not just a cost center, but a competitive advantage.