AI in Accounting: Uses and Trends for 2025

AI in accounting involves using AI-powered software to automate, streamline, and improve accounting tasks. It’s like your trusty assistant who doesn’t ask for coffee breaks and analyses financial data with lightning speed and precision.

How is AI used in accounting?

AI is used to:

• Automate repetitive accounting tasks: Tedious jobs like data entry, invoice processing, and bank reconciliation are easy to automate with an AI system.
• Detect fraud: AI can sift through large amounts of transaction data in seconds and find unusual patterns or dodgy behaviour, such as duplicate invoices or inconsistent spending. It alerts you if it finds anything unusual so you can investigate further and take action if there has been any fraudulent activity.
• Forecast financial statements: AI can spot trends and make better forecasts. Managing inventory during peak season or cash flow when the economy is about to slow down is easier if you can access forecasts generated by an AI system.
• Assist during audits: Since AI is efficient at analysing large datasets, it can quickly identify anomalies or areas of risk that warrant a closer look. This helps the auditor focus on transactions that require attention instead of looking at every transaction on the statement.

How can AI support accountants?

AI systems are like an accountant’s sidekick. Here’s how they help:

• Save time: AI takes care of mundane tasks like data entry, invoice matching, and payroll processing, so you can focus on the bigger picture.
• Improve accuracy: Humans get tired, distracted, and make typos. AI keeps things tidy and accurate, which is critical when dealing with financial records or compliance-related tasks. Speaking of compliance…
• Lower tax compliance burden: AI systems can apply most tax rules and regulations to transactions and reduce tax compliance burden. You’re also a lot less likely to miss deductions when the process is automated.
• Provide insights: AI systems can offer insights that help you with strategic parts of your work, such as budgeting and capital allocation.

Advantages of AI in accounting

The main advantages of AI in accounting include:

✅ Takes care of time-consuming repetitive tasks, saving you plenty of time

✅ Improves the accuracy of financial records

✅ Offers detailed and actionable insights for budgeting and forecasting

Disadvantages of AI in accounting

Here are some disadvantages of using AI in accounting:

• There’s an upfront cost to setting up an AI system

• Your team members might have to go through a learning curve

The market size of AI in accounting is expected to grow at a mind-boggling 41.27% CAGR between 2025 and 2030. One of the key reasons for massive growth potential is the speed at which AI technologies are evolving. Here are some finance trends currently paving the way for AI to become mainstream:

1. AI-powered OCR data extraction

Optical character recognition (OCR) technology helps convert text on a physical document into digital text.
In accounting, it’s used to pull data from documents like invoices and contracts, convert it into machine-readable text, and populate it in a digital document. It also reduces errors common in manual data transfer.

“Eighteen percent of accountants make financial errors at least daily, with a third making at least a few financial errors every week, and over half (59%) making several errors per month.”

Source: Gartner

Take my hypothetical friend Jane for example. She manages hundreds of invoices for her company each month. She used to manually enter the vendor name, invoice number, payment due date, and other information into the company’s accounting system.

Jane’s company recently decided to invest in an accounting system with OCR. Now, Jane just pulls documents and scans them. The accounting system does the rest.

Not only did OCR speed up processes like invoice management for Jane, it also reduced manual errors and improved document management by making everything searchable.

2. Automated data entry and processing

Automated data entry and processing solves some of the most pressing challenges for accountants.
Spending years learning about accounting only to be stuck transferring data from documents into a computer is frustrating, to say the least. Still, 56% of finance teams spend an average of 10+ hours processing invoices and administering supplier payments.

Modern AI systems eliminate the need for manual transfers and automate various other tasks like clearing payroll payments and auto-generating and sending invoices to clients. While running these processes, AI does almost everything a human does.

Suppose you purchased 100 sneakers last month. The vendor sent the invoice this month and charged you for 125 sneakers. The AI system can pick up on this because it matches the invoice with the purchase order and spends a grand total of zero extra minutes on this.

3. Predictive analytics

Modern AI systems are backed by an underlying algorithm that can find patterns in historical data and use it to predict future outcomes. The historical data can be internal (invoices, statements, purchase orders, etc.) or external (market data, economic forecasts, etc.).

“By 2027, 90% of descriptive (“what happened”) and diagnostic (“how or why it happened”) analytics in finance will be fully automated.”

Source: Gartner

Let’s take Maria, a hypothetical senior accountant at a mid-sized retail company, as an example. Her main task this quarter is to ensure the business has enough cash to cover expenses, including payroll, supplier invoices, and a planned store renovation for next year.

She turns to her AI-powered accounting software for help. The AI system makes cash flow forecasts based on historical sales data, seasonal demand spikes, and the economic environment. Then, Maria accounts for any one-off expenses, like renovation, manually.

Maria notices a potential cash flow gap in late February coinciding with slower post-holiday sales. At this point, Maria can take action—this could be delaying non-essential expenses or renegotiating payment terms with vendors.

Accounting software is typically powered by specialised accounting AI. Tools like ChatGPT and Google’s Bard can assist in drafting financial content, but they can’t process accounting data, calculate your tax liability, or verify compliance of records with GAAP.

Specialised accounting AI is purpose-built for accounting tasks. Here’s how it’s different:

• Specialised training

It’s trained using financial datasets that include invoices, tax records, and payroll data. When you feed a document into an AI accounting system, it understands the contents and applies knowledge in relevant areas—like taxation and disclosure requirements—to process that information and take the required action.

• Ability to process accounting data

Generic AI models can’t reliably perform accounting tasks because of a lack of specialised training. On the other hand, specialised accounting AI pulls data from various sources, such as physical documents and other tools in your tech stack. It interprets that data and takes appropriate action while ensuring the action complies with guidelines in the tax code and financial reporting requirements.

• Contextual understanding

Specialised accounting AI understands concepts like accruals, depreciation schedules, and multi-currency conversions. It understands how financial statements converted into another currency may show an unrealized foreign currency translation gain or loss. Generic AI may have difficulty interpreting and applying these nuanced concepts.

AI takes mundane tasks off the CFO’s plate and allows them to focus on strategic areas of their job. Here are some examples of how AI does that:

Financial planning and analysis

According to a study, 71% of the accounting industry believes AI will bring substantial changes to the accounting industry.

A large part of these changes has to do with how CFOs and finance teams look at the “big picture,” which includes looking at what happened to the company’s financial position over the last period and what’s expected in the next period.

Traditionally, CFOs and finance teams spent time manually pulling data from ERP systems to analyse past trends and create financial forecasts. Often, the person migrating data would make mistakes, making the process time-consuming and tedious.

AI changes the entire process. AI-powered accounting systems integrate with your ERP and automatically pull data in real time. It uses this data to give you insights into various areas of finance, such as cash flows, financial and operational leverage, and optimum cost of capital.

These forecasts are automatically updated as new data from the ERP keeps rolling in, so there’s no need to start over if there’s a mid-quarter market shift.

Suppose you’re the CFO of a luxury goods company selling high-end watches, handbags, and jewellery. Your business is spread across America, Europe, and Asia, and you’re planning next year’s budget while navigating supply chain disruptions and fluctuating demand due to an uncertain economy.

If you track monthly revenues using an ERP or spreadsheet, you probably won’t know that sales in Europe plummeted by 15% due to reduced consumer spending until much later.

By then, the budget allocation might have already been locked in and you could end up overspending on marketing campaigns in Europe.

On the other hand, AI-powered accounting software would have pulled real-time revenue data from each region in real-time and alerted you as soon as it noticed a drop in demand.

At the same time, predictive analytics could’ve given you insights into the Asian market, where sales can rise up to 20% next quarter. Access to this information would allow you to reallocate your marketing budget to focus on Asia.

Cost optimisation

In addition to lowering costs by automating expense approvals and invoicing, AI accounting software provides insights that enable further cost optimisation.

Finance teams and CFOs often have a hard time sifting through each department’s expenses and justifications. Expenses are often reported at the end of the quarter, which means by the time anomalies are identified, it’s too late.

AI accounting software tracks expenses in real-time, which means CFOs and finance teams can use it to identify a breach of budget or possible fraud before it leads to a bigger problem.

Modern AI accounting software can also prepare your budget using predictive analytics based on historical data.

You can always tweak the budget based on your outlook for the budget period, but it gives a head start. This means you spend less time making unnecessary calculations and more time tweaking predicted numbers to simulate the impact of changes in budget.

Let’s say you’re the CFO of a midsized manufacturing company. Spiralling costs of raw materials and shipping due to a volatile market environment are making you anxious.

Fortunately, your company invested in AI accounting software last year.

This means you use the software to dig deeper into data. While doing so, you notice that the AI system has flagged a few transactions—one of your suppliers has been steadily increasing the price of components by 2% every quarter.

If the cost of this component hasn’t increased across the market, it’s worth investigating the matter.

That’s the kind of insight you get with AI.

Payroll processing

Payroll processing includes multiple tasks, such as benefits allocations, tax filings, and compliance across jurisdictions. AI accounting systems can take many of these tasks off your plate.

For instance, an AI system can analyse historical and real-time data to predict the financial impact of workforce changes, such as hiring for a seasonal surge or downsizing during a downturn.

Some AI accounting systems include timesheet management features that provide insights into productivity, project profitability, and resource allocation.

For example, you could use an AI-powered timesheet to identify underutilised staff in specific regions and reallocate those resources to higher-value projects.

AI systems can find problems related to employee turnover too. It can forecast the financial implications of growing attrition and the cost of replacing employees.

Once your system flags this problem, you might consider running a cost-benefit analysis to choose between hiring new full-time employees or working with contractors.

Risk management

CFOs manage various types of risks. Since it’s difficult for one person to monitor multiple aspects of business for risks, a big chunk of this responsibility is transferred to the finance team.

Instead of wasting human resources, AI accounting software can be a smarter choice. Here are some risks that AI accounting software can help manage.

• Fraud: Fraud risks like receiving fake invoices, making duplicate payments, or when you’ve signed a contract with a dodgy vendor are hard to monitor at scale. AI accounting software can analyse millions of transactions in seconds and find anomalies that indicate fraud. For example, if two invoices with the same amount and specifications were sent by different vendors, it flags them for review.
• Compliance: Regulatory compliance is burdensome and expensive. Keeping up with evolving tax codes, accounting standards, and data privacy laws, especially across regions, is overwhelming. AI accounting systems can stay on top of global regulations and cross-check transactions, reports, and filings against the latest rules to ensure compliance.
• Liquidity: AI accounting software helps monitor liquidity by projecting cash flows over future periods. It looks at payment trends to project cash flows, and you can also factor in other things into the projections like revenue growth or changes in vendor payment agreements. If you want to dig deeper, you can use the AI system to simulate scenarios—if sales dip 15% over the next quarter, would you still be liquid enough?